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799 Lamens A & Bajorath J. Generation of molecular counterfactuals for explainable machine learning based on core-substituent recombination. ChemMedChem, in press.
798 Bajorath J. Chemical space models for molecular design. Mol Inf, in press.
797 Janela T & Bajorath J. Anatomy of potency predictions focusing on structural analogues with increasing potency differences including activity cliffs. J Chem Inf Model, in press.
796 Mastropietro A, Pasculli G & Bajorath J. Learning characteristics of graph neural networks predicting protein-ligand affinities. Nat Mach Intell, in press.
795 Mastropietro A, Feldmann C & Bajorath J. Calculation of exact Shapley values for explaining support vector machine models using the radial basis function kernel. Sci Rep, 13, 19561, 2023.
794 Janela T & Bajorath J. Rationalizing general limitations in assessing and comparing methods for compound potency prediction. Sci Rep 13, 17816, 2023.
793 Chen H & Bajorath J. Meta-learning for transformer-based prediction of potent compounds. Sci Rep 13, 16145, 2023.
792 Gambacorta N, Ciriaco F, Amoroso N, Altomare C, Bajorath J & Nicolotti O. CIRCE: web-based platform for the prediction of cannabinoid receptor ligands using explainable machine learning. J Chem Inf Model 63, 5916-5926, 2023.
791 Xerxa E & Bajorath J. Data sets of human and mouse protein kinase inhibitors with curated activity data including covalent inhibitors. Future Science OA 9, FSOA892, 2023.
790 Bajorath J. Data and code availability requirements in open science and consequences for different research environments. Artif Intell Life Sci 4, 100085, 2023.
789 Lamens A & Bajorath J. Explaining multiclass compound activity predictions using counterfactuals and Shapley values. Molecules 28, 5601, 2023.
788 Xerxa E, Laufkötter O & Bajorath J. Systematic analysis of covalent and allosteric protein kinase inhibitors. Molecules 28, 5805, 2023.
787 Xerxa E, Miljković F & Bajorath J. Data-driven global assessment of protein kinase inhibitors with emphasis on covalent compounds. J Med Chem 66, 7657-7665, 2023.
786 Dimitrov T, Moschopoulou AA, Seidel L, Kronenberger T, Kudolo M, Poso A, Geibel C. Woelffing P, Dauch D, Zender L, Schollmeyer D, Bajorath J, Forster M & Laufer S. Design and optimization of novel benzimidazole- and imidazo[4,5-b]pyridine-based ATM kinase inhibitors with subnanomolar activities. J Med Chem 66, 7304-7330, 2023.
785 Yoshimori A & Bajorath J. Motif2Mol: Prediction of new active compounds based on sequence motifs of ligand binding sites in proteins using a biochemical language model. Biomolecules 13, 833, 2023.
784 Chen H & Bajorath J. Designing highly potent compounds using a chemical language model. Sci Rep 13, 7412, 2023.
783 Siemers F & Bajorath J. Differences in learning characteristics between support vector machine and random forest models for compound classification revealed by Shapley value analysis. Sci Rep 13, 5983, 2023.
782 Janela T & Bajorath J. Large-scale predictions of compound potency with original and modified activity classes reveal general prediction characteristics and intrinsic limitations of conventional benchmarking calculations. Pharmaceuticals 16, 530, 2023.
781 Bajorath J. Generative kinase inhibitor modeling viewed from a medicinal chemistry perspective. Future Med Chem 15, 313-315, 2023.
780 Bajorath J. Data science and data analytics in life science research. Artif Intell Life Sci 3, 100067, 2023.
779 Yoshimori A, Chen H & Bajorath J. Chemical language models for applications in medicinal chemistry. Future Med Chem 15, 119-121, 2023.
778 Janela T, Takeuchi K & Bajorath J. Predicting potent compounds using a conditional variational autoencoder based upon a new structure-potency fingerprint. Biomolecules 13, 393, 2023.
777 Umedera K, Yoshimori A, Chen H, Kouji H, Nakamura H & Bajorath J. DeepCubist: molecular generator for designing peptidomimetics based on complex three-dimensional scaffolds. J Comput-Aided Mol Des 37, 107-115, 2023.
776 Lamens A & Bajorath J. Explaining accurate predictions of multitarget compounds with machine learning models derived for individual targets. Molecules 28, 825, 2023.
775 Tamura S, Miyao T & Bajorath J. Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity. J Cheminform 15, 4, 2023.
774 Pietruś W, Kurczab R, Warszycki D, Bojarski AJ & Bajorath J. Isomeric activity cliffs – a case study for fluorine substitution of aminergic G protein coupled receptor ligands. Molecules 28, 490, 2023.
773 Bajorath J. Specific contributions of artificial intelligence to interdisciplinary life science research – exploring and communicating new opportunities. Artif Intell Life Sci 4, 100052, 2023.
772 Janela T & Bajorath J. Simple nearest neighbor analysis meets the accuracy of compound potency predictions using complex machine learning models. Nat Mach Intell 4, 1246–1255, 2022.
771 Mastropietro A, Pasculli G & Bajorath J. Protocol to explain graph neural network predictions using an edge-centric Shapley value-based approach. STAR Protocols 3, 101887, 2022.
770 Feldmann C & Bajorath J. Advances in computational polypharmacology. Mol Inf 41, 2200190, 2022.
769 Chen H, Vogt M & Bajorath J. DeepAC - Conditional transformer-based chemical language model for the prediction of activity cliffs formed by bioactive compounds. Digital Discovery 1, 898-909, 2022.
768 Bajorath J. Revisiting active learning in drug discovery through open science. Artif Intell Life Sci 2,100051, 2022.
767 Umedera K, Yoshimori A, Bajorath J & Nakamura H. Design of MMP-1 inhibitors via SAR transfer and experimental validation. Sci Rep 12, 20915, 2022.
766 Bajorath J, Chávez-Hernández AL, Duran-Frigola M et al. Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds. J Cheminform 14, 82, 2022.
765 Siemers FM, Feldmann C & Bajorath J. Minimal data requirements for accurate compound activity prediction using machine learning methods of different complexity. Cell Rep Phys Sci 3, 101113, 2022.
764 Mastropietro A, Pasculli G, Feldmann C, Rodríguez-Pérez R & Bajorath J. EdgeSHAPer: bond-centric Shapley value-based explanation method for graph neural networks. iScience 25, 105043, 2022.
763 Feldmann C & Bajorath J. Calculation of exact Shapley values for support vector machines with Tanimoto kernel enables model interpretation. iScience 25, 105023, 2022.
762 Yoshimori A & Bajorath J. Computational analysis, alignment, and extension of analogue series from medicinal chemistry. Future Sci OA 8, FSO804, 2022.
761 Bajorath J. Explainable machine learning for medicinal chemistry - exploring multi-target compounds. Future Med Chem 14, 1171-1173, 2022.
760 Shanmugasundaram V, Bajorath J, Christoffersen RE et al. Epilogue to the Gerald Maggiora Festschrift: a tribute to an exemplary mentor, colleague, collaborator, and innovator. J. Comp-Aided Mol Des 36, 623-638, 2022.
759 Rodríguez-Pérez R & Bajorath J. Evolution of support vector machine and regression modeling in chemoinformatics and drug discovery. J Comput-Aided Mol Des 36, 355-362, 2022.
758 Blaschke T & Bajorath J. Fine-tuning of a generative neural network for designing multi-target compounds. J Comput-Aided Mol Des 36, 363-371, 2022.
757 Yoshimori A & Bajorath J. Computational method for the systematic alignment of analogue series with structure-activity relationship transfer potential across different targets. Eur J Med Chem 239, 114558, 2022.
756 Kunimoto R, Bajorath J & Aoki K. From traditional to data-driven medicinal chemistry – a case study. Drug Discov Today 27, 2065-2070, 2022.
755 Bajorath J. Deep learning of protein-ligand interactions - remembering the actors. Artif Intell Life Sci 2, 100037, 2022.
754 Laufer S, Bajorath J, Gehringer M, Gray N, Frye S, Lindsley CW. Publication criteria and requirements for studies on protein kinase inhibitors – what is expected? J Med Chem 65, 6973-6974, 2022.
753 Yoshimori A & Bajorath J. DeepAS – chemical language model for the extension of active analogue series. Bioorg Med Chem 66, 116808, 2022.
752 Rodríguez-Pérez R, Miljković F & Bajorath J. Machine learning in chemoinformatics and medicinal chemistry. Ann Rev Biomed Data Sci 5, 43-65, 2022.
751 Feldmann C & Bajorath J. Differentiating inhibitors of closely related protein kinases with single- or multi-target activity via explainable machine learning and feature analysis. Biomolecules 12, 557, 2022.
750 Janela T, Takeuchi K & Bajorath J. Introducing a chemically intuitive core-substituent fingerprint designed to explore structural requirements for effective similarity searching and machine learning. Molecules 27, 2331, 2022.
749 Bajorath J. Understanding uncertainty in deep learning builds confidence. Artif Intell Life Sci 2, 100033, 2022.
748 Bajorath J. Artificial intelligence in interdisciplinary life science and drug discovery research. Future Sci OA 8, FSO792, 2022.
747 Bajorath J. Deep machine learning for computer-aided drug design. Front Drug Discov 2, 829043, 2022.
746 Laufer S & Bajorath J. New horizons in drug discovery - understanding and advancing different types of kinase inhibitors: seven years in kinase inhibitor research with impressive achievements and new future prospects. J Med Chem 65, 891-892, 2022.
745 Laufkötter O, Hu H, Miljković F & Bajorath J. Structure- and similarity-based survey of allosteric kinase inhibitors, activators, and closely related compounds. J Med Chem 65, 935-954, 2022.
744 Bajorath J. AI in life sciene research - the road ahead. Artif Intell Life Sci 2, 100030, 2022.
743 Hu H & Bajorath J. Systematic identification of activity cliffs with dual-atom replacements and their rationalization on the basis of single-atom replacement analogs and X-ray structures. Chem Biol Drug Des 99, 308-319, 2022.
742 Yoshimori A, Miljković F & Bajorath J. Approach for the design of covalent protein kinase inhibitors via focused deep generative modeling. Molecules 27, 570, 2022.
741 Bajorath J. Comprehensive analysis of R-groups in medicinal chemistry. Future Med Chem 14, 5-7, 2022.
740 Rodríguez-Pérez R & Bajorath J. Explainable machine learning for property predictions in compound optimization. J Med Chem 64, 1774-17752, 2021.
739 Miljković F, Rodríguez-Pérez R & Bajorath J. Impact of artificial intelligence on compound discovery, design, and synthesis. ACS Omega 6, 33293-33299, 2021.
738 Bajorath J. Second-generation artificial intelligence approaches for life science research. Artif Intell Life Sci 1, 100026, 2021.
737 Iqbal J, Vogt M & Bajorath J. Learning functional group chemistry from molecular images leads to accurate prediction of activity cliffs. Artif Intell Life Sci 1, 100022, 2021.
736 Iqbal J, Vogt M & Bajorath J. Prediction of activity cliffs on the basis of images using convolutional neural networks. J Comput-Aided Mol Des 35, 1157–1164, 2021.
735 Yoshimori A & Bajorath J. Iterative DeepSARM modeling for compound optimization. Artif Intell Life Sci, 1, 100015, 2021.
734 Pietruś W, Kurczab R, Stumpfe D, Bojarski AJ & Bajorath J. Data-driven analysis of fluorination of ligands of aminergic G protein coupled receptors. Biomolecules 11, 1647, 2021.
733 Feldmann C, Philipps M & Bajorath J. Explainable machine learning predictions of dual-target compounds reveal characteristic structural features. Sci Rep 11, 21594, 2021.
732 Takeuchi K, Kunimoto R & Bajorath J. Searchable database of frequent R-groups in medicinal chemistry and their preferred replacements. Data in Brief 39, 107456, 2021.
731 Rodríguez-Pérez R & Bajorath J. Chemistry-centric explanation of machine learning models. Artif Intell Life Sci 1, 100009, 2021.
730 Takeuchi K, Kunimoto R & Bajorath J. Systematic mapping of R-group space enables the generation of an R-group replacement system for medicinal chemistry. Eur J Med Chem 225, 113771, 2021.
729 Rodríguez-Pérez R & Bajorath J. Feature importance correlation from machine learning indicates functional relationships between proteins and similar compound binding characteristics. Sci Rep 11, 14245, 2021.
728 Utomo RY, Asawa Y, Okada S, Yoshimori A, Bajorath J & Nakamura H. Development of curcumin-based amyloid β aggregation inhibitors for Alzheimer's disease using the SAR matrix approach. Bioorg Med Chem 46, 116357, 2021.
727 Takeuchi K, Kunimoto R & Bajorath J. R-group replacement database for medicinal chemistry. Future Sci OA 7, FSOA742, 2021.
726 Bajorath J. Evolution of assay interference concepts in drug discovery. Expert Opin Drug Discov 16, 719-721, 2021.
725 Bajorath J. Minimal screening requirements for identifying highly promiscuous kinase inhibitors. Future Med Chem 13, 1083-1085, 2021.
724 Hu H & Bajorath J. Systematic assessment of structure-promiscuity relationships between different types of kinase inhibitors. Bioorg Med Chem 41, 116226, 2021.
723 Bajorath J. Structural characteristics of compounds with multi-target activity. Future Drug Discov 3, FDD60, 2021.
722 Yoshimori A & Bajorath J. Adapting the DeepSARM approach for dual-target ligand design. J Comput-Aided Mol Des 35, 587-600, 2021.
721 Medina-Franco JL, Martinez-Mayorga K, Fernández-de Gortari E, Kirchmair J & Bajorath J. Rationality over fashion and hype in drug design. F1000Research 10(Chem Inf Sci), 397, 2021.
720 Bajorath J. State-of-the-art of artificial intelligence in medicinal chemistry. Future Sci OA 7, FSO702, 2021.
719 Blaschke T & Bajorath J. Compound data set and custom code for deep generative multi-target compound design. Future Sci OA 7, FSO715, 2021.
718 Feldmann C & Bajorath J. Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations. Sci Rep 11, 7863, 2021.
717 Stumpfe D, Hoch A & Bajorath J. Introducing the metacore concept for multi-target ligand design. RSC Med Chem 12, 628-635, 2021.
716 Bajorath J, Coley CW, Landon MR, Walters WP & Zheng M. Reproducibility, reusability, and community efforts in artificial intelligence research. Artif Intell Life Sci 1, 100002, 2021.
715 Feldmann C, Yonchev D & Bajorath J. Structured data sets of compounds with multi-target and corresponding single-target activity from biological assays. Future Sci OA 7, FSO685, 2021.
714 Zheng M, Horta Andrade C & Bajorath J. Introducing artificial intelligence in the life sciences. Artif Intell Life Sci 1, 100001, 2021.
713 Rodríguez-Pérez R & Bajorath J. Evaluation of multi-target deep neural network models for compound potency prediction under increasingly challenging test conditions. J Comput-Aided Mol Des 35, 285-295, 2021.
712 Galati S, Yonchev D, Rodríguez-Pérez R, Vogt M, Tuccinardi T & Bajorath J. Predicting isoform-selective carbonic anhydrase inhibitors via machine learning and rationalizing structural features important for selectivity. ACS Omega 6, 4080-4089, 2021.
711 Hu H, Laufkötter O, Miljković F & Bajorath J. Data set of competitive and allosteric protein kinase inhibitors confirmed by X-ray crystallography. Data in Brief 35, 106816, 2021.
710 Bajorath J. Rationalizing multi-target activities of small molecules on the basis of X-ray structures and diagnostic machine learning, in: Current Issues in Medicine: Biochemistry, Genomics, Physiology, and Pharmacology, Bawa R & Audette GF (Eds.), Volume 1, Chapter 31, Jenny Stanford Publishing Pte. Ltd., Singapore, 2021.
709 Bajorath J. Paradigm shift in medicinal chemistry towards data-driven approaches, in: Current Issues in Medicine: Fundamentals and Basic Medical Sciences, Bawa R & Audette GF (Eds.), Volume 2, Chapter 27, Jenny Stanford Publishing Pte. Ltd., Singapore, 2021.
708 Hu H, Laufkötter O, Miljković F & Bajorath J. Systematic comparison of competitive and allosteric kinase inhibitors reveals common structural characteristics. Eur J Med Chem 214, 113206, 2021.
707 Lopez E, Bajorath J & Medina-Franco J. Informatics for chemistry, biology, and biomedical sciences. J Chem Inf Model 61, 26-35, 2021.
706 Blaschke T, Feldmann C & Bajorath J. Prediction of promiscuity cliffs using machine learning. Mol Inf 40, e2000196, 2021.
705 Maggiora G, Medina-Franco JL, Iqbal J, Vogt M & Bajorath J. From qualitative to quantitative analysis of activity and property landscapes. J Chem Inf Model 60, 5873-5880, 2020.
704 Takeuchi K, Kunimoto R & Bajorath J. Global assessment of substituents on the basis of analog series. J Med Chem 63, 15013-15020, 2020.
703 Feldmann C, Yonchev D, Stumpfe D & Bajorath J. Systematic data analysis and diagnostic machine learning reveal differences between compounds with single- and multi-target activity. Mol Pharmaceutics 17, 4652-4666, 2020.
702 Yoshimori A & Bajorath J. The SAR Matrix method and an artificially intelligent variant for the identification and structural organization of analog series, SAR analysis, and compound design. Mol Inf 39, e2000045, 2020.
701 Yonchev D, Vogt M & Bajorath J. From SAR diagnostics to compound design: development chronology of the compound optimization monitor (COMO) method. Mol Inf 39, e2000046, 2020.
700 Feldmann C, Yonchev D & Bajorath J. Analysis of biological screening compounds with single- or multi-target activity via diagnostic machine learning. Biomolecules 10, e1605, 2020.
699 Blaschke T, Engkvist O, Bajorath J & Chen H. Memory-assisted reinforcement learning for diverse molecular de novo design. J Cheminf 12, e68, 2020.
698 Blaschke T & Bajorath J. Compound design using generative neural networks, in: Artificial Intelligence in Drug Discovery (Drug Discovery Series), N Brown (Ed.), RSC Publishing, Cambridge, 215-227, 2020.
697 Yonchev D & Bajorath J. DeepCOMO: from structure-activity relationship diagnostics to generative molecular design using the compound optimization monitor methodology. J Comput-Aided Mol Des 34, 1207-1218, 2020.
696 Hu H & Bajorath J. Data set of activity cliffs with single-atom modification and associated X-ray structure information for medicinal and computational chemistry applications. Data in Brief 33, 106364, 2020.
695 Stumpfe D & Bajorath J. Current trends, overlooked issues, and unmet challenges in virtual screening. J Chem Inf Model 60, 4112-4115, 2020.
694 Iqbal J, Vogt M & Bajorath J. Quantitative comparison of three-dimensional activity landscapes of compound data sets based upon topological features. ACS Omega 5, 24111-24117, 2020.
693 Hu H & Bajorath J. Activity cliffs produced by single-atom modification of active compounds: systematic identification and rationalization based on X-ray structures. Eur J Med Chem 207, 112846, 2020.
692 Asawa Y, Yoshimori A, Bajorath J & Nakamura H. Prediction of an MMP-1 inhibitor activity cliff using the SAR Matrix approach and its experimental validation. Sci Rep 10, e14710, 2020.
691 Iqbal J, Vogt M & Bajorath J. Computational method for quantitative comparison of activity landscapes on the basis of image data. Molecules 25, e3952, 2020.
690 Hu H & Bajorath J. Evidence for the presence of core structure-dependent activity cliffs. Future Med Chem 12, 1451-1455, 2020.
689 Rodríguez-Pérez R & Bajorath J. Interpretation of machine learning models using Shapley values: application to compound potency and multi-target activity predictions. J Comput-Aided Mol Des 34, 1013-1026, 2020.
688 Rodríguez-Pérez R & Bajorath J. Interpretation of compound activity predictions from complex machine learning models using local approximations and Shapley values. J Med Chem 63, 8761-8777, 2020.
687 Miljković F, Rodríguez-Pérez R & Bajorath J. Machine learning models for accurate prediction of kinase inhibitors with different binding modes. J Med Chem 63, 8738-8748, 2020.
686 Bajorath J, Kearnes S, Walters WP, Meanwell NA, Georg G & Wang S. Artificial intelligence in drug discovery – into the great wide open. J Med Chem 63, 8651-8652, 2020.
685 Laufkötter O, Laufer S & Bajorath J. Kinase inhibitor data set for systematic analysis of representative kinases across the human kinome. Data in Brief 32, 106189, 2020.
684 Laufer S, Briner K, Bajorath J, Georg G & Wang S. New horizons in drug discovery - understanding and advancing kinase inhibitors. J Med Chem 63, 7921-7922, 2020.
683 Lemke C, Cianni L, Feldmann C, Gilberg E, Yin J, dos Reis Rocho F, de Vita D, Bartz U, Bajorath J, Montanari & Gütschow M. N-sulfonyl dipeptide nitriles as inhibitors of human cathepsin S: in silico design, synthesis and biochemical characterization. Bioorg Med Chem Lett 30, 127420, 2020.
682 Laufkötter O, Laufer S & Bajorath J. Identifying representative kinases for inhibitor evaluation via systematic analysis of compound-based target relationships. Eur J Med Chem 204, 112641, 2020.
681 Stumpfe D, Hu H & Bajorath J. Advances in exploring activity cliffs. J Comput-Aided Mol Des 34, 929-942.
680 Hu H & Bajorath J. Simplified activity cliff network representations with high interpretability and immediate access to SAR information. J Comput-Aided Mol Des 34, 943-952, 2020.
679 Feldmann C & Bajorath J. Compounds with multi-target activity: structure-based analysis and machine learning. Future Drug Discov 2, FDD44, 2020.
678 Omar AM, Bajorath J, Ihmaid S, Mohamed HM, El-Agrody AM, Mora A, El-Araby ME, Ahmed HEA. Novel molecular discovery of promising amidine-based thiazole analogues as potent dual matrix metalloproteinase-2 and 9 inhibitors: anticancer activity data with prominent cell cycle arrest and DNA fragmentation analysis effects. Bioorg Chem 101, e103992, 2020.
677 Yonchev D & Bajorath J. Inhibitor bias in luciferase-based luminescence assays. Future Science OA 6, FSO594, 2020.
676 Muratov EN, Bajorath J, Sheridan RP, Tetko I, Filimonov D, Poroikov V, Oprea T, Baskin II, Varnek A, Roitberg A, Isayev O, Curtalolo S, Fourches D, Cohen Y, Aspuru-Guzik A, Winkler DA, Agrafiotis D, Cherkasov A & Tropsha A. QSAR without borders. Chem Soc Rev 49, 3525-3564, 2020.
675 Feldmann C & Bajorath J. X-ray structure-based chemoinformatic analysis identifies promiscuous ligands binding to proteins from different classes with varying shapes. Int J Mol Sci 21, e3782, 2020.
674 Rodríguez-Pérez R, Miljković F & Bajorath J. Assessing the information content of structural and protein-ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning. J Cheminf 12, e36, 2020.
673 Iqbal J, Vogt M & Bajorath J. Activity landscape image analysis using convolutional neural networks. J Cheminf 12, e34, 2020.
672 Hu H & Bajorath J. Increasing the public activity cliff knowledge base with new categories of activity cliffs. Future Sci OA 6, FSO472, 2020.
671 Yoshimori A & Bajorath J. Deep SAR Matrix: SAR Matrix expansion for advanced analog design using deep learning architectures. Future Drug Discov 2, FDD36, 2020.
670 Cianni L, Lemke C, Gilberg E, Feldmann C, Rosini F, dos Reis Rocho F, Ribeiro JFR, Tezuka DY, Lopes CD, de Albuquerque S, Bajorath J, Laufer S, Leitão A, Gütschow & Montanari CA. Mapping the S1 and S1’ subsites of cysteine proteases with new dipeptidyl nitrile inhibitors as trypanocidal agents. PLoS Negl Trop Dis 14, e0007755, 2020.
669 Hu H & Bajorath J. Systematic exploration of activity cliffs containing privileged substructures. Mol Pharmaceutics 17, 979-989, 2020.
668 Bonanni D, Lolli ML & Bajorath J. Computational method for structure-based analysis of SAR transfer. J Med Chem 63, 1388-1396, 2020.
667 Feldmann C & Bajorath J. Biological activity profiles of multitarget ligands from X-ray structures. Molecules 25, e794, 2020.
666 Vogt M & Bajorath J. ccbmlib – a Python package for modeling Tanimoto similarity value distributions. F1000Research 9(Chem Inf Sci), e100, 2020.
665 Hu H & Bajorath J. Introducing a new category of activity cliffs combining different compound similarity criteria. RSC Med Chem 11, 132-141, 2020.
664 Stumpfe D, Hu H & Bajorath J. Computational method for the identification of third generation activity cliffs. MethodsX 7, e100793, 2020.
663 Yonchev D & Bajorath J. Integrating computational lead optimization diagnostics with analog design and candidate selection. Future Sci OA 6, FSO451, 2020.
662 Miljković F & Bajorath J. Data structures for computational compound promiscuity analysis and exemplary applications to inhibitors of the human kinome. J Comput-Aided Mol Des 34, 1-10, 2020.
661 González-Medina M, Miljković F, Haase GS, Drueckes P, Trappe J, Laufer S & Bajorath J. Promiscuity analysis of a kinase panel screen with designated p38 alpha inhibitors. Eur J Med Chem 187, 112004, 2020.
660 Hu H & Bajorath J. Exploring structure-promiscuity relationships using dual-site promiscuity cliffs and corresponding single-site analogs. Bioorg Med Chem 28, 115238, 2020.
659 Bajorath J. Exploring polypharmacology and molecular promiscuity. J Comp Aided Chem 20, 43-46, 2019.
658 Cianni L, Feldmann CL, Gilberg E, Gütschow M, Juliano L, Leitão A, Bajorath J & Montanari C. Can cysteine protease cross-class inhibitors achieve selectivity? J Med Chem 62, 10497-10525, 2019.
657 Feldmann C, Miljković F, Yonchev D & Bajorath J. Identifying promiscuous compounds with activity against different target classes. Molecules 24, e4185, 2019.
656 Yonchev D, Vogt M & Bajorath J. Compound optimization monitor (COMO) method for computational evaluation of progress in medicinal chemistry projects. Future Drug Discov 1, FDD15, 2019.
655 David L, Walsh J, Sturm N, Feierberg I, Nissink JWM, Bajorath J & Engkvist O. Detection of molecules interfering with HTS assay technologies. ChemMedChem 14, 1795-1802, 2019.
654 Yoshimori A, Horita Y, Tanaoue T & Bajorath J. Method for systematic analog search using the mega SAR matrix database. J Chem Inf Model 59, 3727-3734, 2019.
653 Miyao T, Jasial S, Bajorath J & Funatsu K. Evaluation of different virtual screening strategies on the basis of compound sets with characteristic core distributions and dissimilarity relationships. J Comput-Aided Mol Des 33, 729-743, 2019.
652 Naveja JJ, Pilón-Jiménez A, Bajorath J & Medina-Franco JL. A general approach for retrosynthetic molecular core analysis. J Cheminf 11, e61, 2019.
651 Laufkötter O, Sturm N, Bajorath J, Chen H & Engkvist O. Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability. J Cheminform, 11, e54, 2019.
650 Stumpfe D, Hu H & Bajorath J. The evolving concept of activity cliffs. ACS Omega 4, 14360-14368, 2019.
649 Laufkötter O, Miyao T & Bajorath J. Large-scale comparison of alternative similarity search strategies with varying chemical information contents. ACS Omega 4, 15304-15311, 2019.
648 Miljković F & Bajorath J. Data structures for compound promiscuity analysis: cliffs, pathways, and hubs formed by inhibitors of the human kinome. Future Science OA 5, FSO404, 2019.
647 Bajorath J. Forward-looking perspective on publishing in drug discovery. Future Drug Discov 1, FDD2, 2019.
646 Stumpfe D, Hu H & Bajorath J. Introducing a new category of activity cliffs with chemical modifications at multiple sites and rationalizing contributions of individual substitutions. Bioorg Med Chem 27, 3605-3612, 2019.
645 Bajorath J. Repositioning the chemical information science gateway. F1000Research 8(Chem Inf Sci), e976, 2019.
644 Naveja JJ, Stumpfe D, Medina-Franco JL & Bajorath J. Exploration of target synergy in cancer treatment by cell-based screening assay and network propagation analysis. J Chem Inf Model 59, 3072-3079, 2019.
643 Bajorath J, Kearnes S, Walters WP, Georg GI & Wang S. The future is now: artificial intelligence in drug discovery. J Med Chem 62, 5249-5249, 2019.
642 Bajorath J. Duality of activity cliffs in drug discovery. Expert Opin Drug Discov 14, 517-520, 2019.
641 Miljković F, Vogt M & Bajorath J. Systematic computational identification of promiscuity cliff pathways formed by inhibitors of the human kinome. J Comput-Aided Mol Des 33, 559-572, 2019.
640 Yoshimori A, Tanoue T & Bajorath J. Integrating the structure-activity relationship matrix method with molecular grid maps and activity landscape models for medicinal chemistry applications. ACS Omega 4, 7061-7069, 2019.
639 Blaschke T, Miljković F & Bajorath J. Prediction of different classes of promiscuous and non-promiscuous compounds using machine learning and nearest neighbor analysis. ACS Omega 4, 6883-6890, 2019..
638 Hu H, Stumpfe D & Bajorath J. Second generation activity cliffs identified on the basis of target set-dependent potency difference criteria. Future Med Chem 11, 379-394, 2019.
637 Miyao T, Funatsu K & Bajorath J. Exploring alternative strategies for the identification of potent compounds using support vector machine and regression modeling. J Chem Inf Model 59, 983-992, 2019.
636 Miyao T, Funatsu K & Bajorath J. Three-dimensional activity landscape models of different design and their application to compound mapping and potency prediction. J Chem Inf Model 59, 993-1004, 2019.
635 Rodríguez-Pérez R & Bajorath J. Multi-task machine learning for classifying highly and weakly potent kinase inhibitors. ACS Omega 4, 4367-4375, 2019.
634 Hu H, Stumpfe D & Bajorath J. Systematic identification of target set-dependent activity cliffs. Future Science OA 5, FSO363, 2019.
633 Casciuc I, Zabolotna Y, Horvath D, Marcou G, Bajorath J & Varnek A. Virtual screening with generative topographic maps: how many maps are required? J Chem Inf Model 59, 564-572, 2019.
632 Gilberg E & Bajorath J. Recent progress in structure-based evaluation of compound promiscuity. ACS Omega 4, 2758-2765, 2019.
631 Casciuc I, Horvath D, Gryniukova A, Tolmachova KA, Vasylchenko OV, Borysko P, Moroz YS, Bajorath J & Varnek A. Pros and cons of virtual screening based on public "big data": in silico mining for new bromodomain inhibitors. Eur J Med Chem 165, 258-272, 2019.
630 Gilberg E, Gütschow M & Bajorath J. Promiscuous ligands from experimental structures, binding conformations, and protein family dependent interaction hotspots. ACS Omega 4, 1729-1737, 2019.
629 Naveja JJ, Vogt M, Stumpfe D, Medina-Franco JL & Bajorath J. Systematic extraction of analog series from large compound collections using a new computational compound-core relationship method. ACS Omega 4, 1027-1032, 2019.
628 Brown JB & Bajorath J. Computational chemical biology on the rise. Future Med Chem 11, 1-3, 2019.
627 Schmitz J, Gilberg E, Löser R, Bajorath J, Bartz U & Gütschow M. Cathepsin B: active site mapping with peptidic substrates and inhibitors. Bioorg Med Chem 27, 1-15, 2019.
626 Vogt M, Yonchev D & Bajorath J. Computational method to evaluate progress in lead optimization. J Med Chem 61, 10895-10900, 2018.
625 Miljković F & Bajorath J. Computational analysis of kinase inhibitors identifies promiscuity cliffs across the human kinome. ACS Omega 3, 17295-17308, 2018.
624 Jasial S, Gilberg E, Blaschke T & Bajorath J. Machine learning distinguishes with high accuracy between pan-assay interference compounds that are promiscuous or represent dark chemical matter. J Med Chem 61, 10255-10264, 2018.
623 Anighoro A, Pinzi L, Rastelli G & Bajorath J. Virtual Screening for Hsp90/B-Raf inhibitors, in: Roy K (Ed.): Multi-Target Drug Design Using Chem-Bioinformatic Approaches. Methods of Pharmacology and Toxikology. SpringerProtocols Springer Nature Switzerland AG, 355-365, 2018.
622 Yonchev D, Vogt M, Stumpfe D, Kunimoto R, Miyao T & Bajorath J. Computational assessment of chemical saturation of analog series under varying conditions. ACS Omega 3, 15799-15808, 2018.
621 Vogt M, Jasial S & Bajorath J. Computationally derived compound profiling matrices. Future Science OA 4, FSO327, 2018.
620 Rodríguez-Pérez R & Bajorath J. Prediction of compound profiling matrices, part II: relative performance of multi-task deep learning and random forest classification on the basis of varying amounts of training data. ACS Omega 3, 12033-12040, 2018.
619 Bajorath J. Foundations of data-driven medicinal chemistry. Future Science OA 4, FSO320, 2018.
618 Hu Y & Bajorath J. SAR matrix method for large-scale analysis of compound structure-activity relationships and exploration of multi-target activity spaces. Methods Mol Biol 1825, 339-352, 2018.
617 Dimova D & Bajorath J. Mapping biological activities to different types of molecular scaffolds: exemplary application to protein kinase inhibitors. Methods Mol Biol 1825, 327-337, 2018.
616 Miljković F & Bajorath J. Data-driven exploration of selectivity and off-target activities of designated chemical probes. Molecules 23, e2434, 2018.
615 Miljković F & Bajorath J. Evaluation of kinase inhibitor selectivity using cell-based profiling data. Mol Inf 37, e1800024, 2018.
614 Miyao T & Bajorath J. Exploring ensembles of bioactive or virtual analogs of X-ray ligands for shape similarity searching. J Comput-Aided Mol Des 32, 759-767, 2018.
613 Iqbal J, Abbasi MSA, Zaib S, Afridi S, Furtmann N, Bajorath J & Langer P. Identification of new chromenone derivatives as cholinesterase inhibitors and molecular docking studies. Med Chem 14, 809-817, 2018.
612 Bajorath J. Improving the utility of molecular scaffolds for medicinal and computational chemistry. Future Med Chem 10, 1645-1648, 2018.
611 Bajorath J. Data analytics and deep learning in medicinal chemistry. Future Med Chem 10, 1541-1543, 2018.
610 Anighoro A & Bajorath J. A hybrid virtual screening protocol based on binding mode similarity. Methods Mol Biol 1824, 165-175, 2018.
609 Hu H, Stumpfe D & Bajorath J. Rationalizing the formation of activity cliffs in different compound data sets. ACS Omega 3, 7736-7744, 2018.
608 Pinzi L, Anighoro A, Bajorath J & Rastelli G. Identification of 4-aryl-1H-pyrrole[2,3-b]pyridine derivatives for the development of new B-Raf inhibitors. Chem Biol Drug Des 92, 1382-1386, 2018.
607 Yonchev D, Dimova D, Stumpfe D, Vogt M & Bajorath J. Redundancy in two major compound databases. Drug Discov Today 23, 1183-1186, 2018.
606 Rodríguez-Pérez R, Miyao T, Jasial S, Vogt M & Bajorath J. Prediction of compound profiling matrices using machine learning. ACS Omega 3, 4713-4723, 2018.
605 Vogt M, Jasial S & Bajorath J. Extracting compound profiling matrices from screening data. ACS Omega 3, 4706-4712, 2018.
604 Dimova D & Bajorath J. Collection of analog series-based (ASB) scaffolds from public compound sources. Future Science OA 4, FSO287, 2018.
603 Kunimoto R & Bajorath J. Combining similarity searching and network analysis for the identification of active compounds. ACS Omega 3, 3768-3777, 2018.
602 Dimova D & Bajorath J. Rationalizing promiscuity cliffs. ChemMedChem 13, 490-494, 2018.
601 Stumpfe D, Gilberg E & Bajorath J. Series of screening compounds with high hit rates for the exploration of multi-target activities and assay interference. Future Science OA 4, FSO279, 2018.
600 Miljković F & Bajorath J. Reconciling selectivity trends from a comprehensive kinase inhibitor profiling campaign with known activity data. ACS Omega 3, 3113-3119, 2018.
599 Blaschke T, Olivecrona M, Engkvist O, Bajorath J & Chen H. Application of generative autoencoder in de novo molecular design. Mol Inf 37, 1700123, 2018.
598 Kunimoto R & Bajorath J. Design of a tripartite network for the prediction of drug targets. J Comput-Aided Mol Des 32, 321-330, 2018.
597 Gilberg E, Gütschow M & Bajorath J. X-ray structures of target-ligand complexes containing compounds with assay interference potential. J Med Chem 61, 1276-1284, 2018.
596 Schulz-Fincke AC, Tikhomirov AS, Braune A, Girbl T, Gilberg E, Bajorath J, Blaut M, Nourshargh S & Gütschow M. Design of an activity-based probe for human neutrophil elastase: implementation of the Lossen rearrangement to induce Förster resonance energy transfers. Biochemistry 57, 742-752, 2018.
595 Kunimoto R, Miyao T & Bajorath J. Computational method for estimating progression saturation of analog series. RSC Adv 8, 5484-5492, 2018.
594 Miljković F & Bajorath J. Exploring selectivity of multi-kinase inhibitors across the human kinome. ACS Omega 3, 1147-1153, 2018.
593 Dimova D, Stumpfe D & Bajorath J. Computational design of new molecular scaffolds for medicinal chemistry, part II: generalization of ASB scaffolds. Future Science OA 4: FSO267, 2018.
592 Gilberg E, Stumpfe D & Bajorath J. X-ray structure based identification of compounds with activity against targets from different families and generation of templates for multi-target ligand design. ACS Omega 3, 106-111, 2018.
591 Kayastha S, Kunimoto R, Horvath D, Varnek A & Bajorath J. From bird's eye views to molecular communities: two-layered visualization of structure-activity relationships in large compound data sets. J Comput-Aided Mol Des 31, 961-977, 2017.
590 Cerchia C, Dimova D, Lavecchia A & Bajorath J. Exploring structural relationships between bioactive and commercial chemical space and developing target hypotheses for compound acquisition. ACS Omega 2, 7760-7766, 2017.
589 Jasial S & Bajorath J. Dark chemical matter in public screening assays and derivation of target hypotheses. Med Chem Commun 8, 2100-2104, 2017.
588 Bajorath J. From activity cliffs to promiscuity cliffs. Future Science OA 3:FSO227, 2017.
587 Rodríguez-Pérez R, Vogt M & Bajorath J. Support vector machine classification and regression prioritize different structural features for binary compound activity and potency value prediction. ACS Omega 2, 6371-6379, 2017.
586 Kunimoto R & Bajorath J. Exploring sets of molecules from patents and relationships to other active compounds in chemical space networks. J Comput-Aided Mol Des 31, 779-788, 2017.
585 Bajorath J. Expanding the chemical information science gateway. F1000Research 6:1764, 2017.
584 Gilberg E, Stumpfe D & Bajorath J. Towards a systematic assessment of assay interference: identification of extensively tested compounds with high assay promiscuity. F1000Research 6(Chem Inf Sci):1505, 2017.
583 Miljković F, Kunimoto R & Bajorath J. Identifying relationships between unrelated pharmaceutical target proteins on the basis of shared active compounds. Future Science OA 3, FSO212, 2017.
582 Miyao T, Funatsu K & Bajorath J. Exploring differential evolution for inverse QSAR analysis. F1000Research 6(Chem Inf Sci):1285, 2017.
581 Stumpfe D, Tinivella A, Rastelli G & Bajorath J. Promiscuity of inhibitors of human protein kinases at varying data confidence levels and test frequency. RSC Adv 7, 41265-41271, 2017.
580 Dimova D & Bajorath J. Is scaffold hopping a reliable indicator for the ability of computational methods to identify structurally diverse active compounds? J Comput-Aided Mol Des 31, 603-608, 2017.
579 Bajorath J. Representation and identification of activity cliffs. Expert Opin Drug Discov 12, 879-883, 2017.
578 Gilberg E, Stumpfe D & Bajorath J. Activity profiles of analog series containing pan assay interference compounds. RSC Adv 7, 35638-35649, 2017.
577 Vogt M & Bajorath J. Modeling Tanimoto similarity value distributions and predicting search results. Mol Inf 36, 1600131, 2017.
576 Anighoro A, Pinzi L, Marverti G, Bajorath J & Rastelli G. Heat shock protein 90 and serine/threonine kinase B-Raf inhibitors have overlapping chemical space. RSC Adv 7, 31069-31074, 2017.
575 Anighoro A & Bajorath J. Compound ranking on the basis of fuzzy 3D similarity improves the performance of docking into homology models of G-protein coupled receptors. ACS Omega 2, 2583-2592, 2017.
574 Bajorath J. Computational scaffold hopping - cornerstone for the future of drug design? Future Med Chem 9, 629-631, 2017.
573 Hu Y & Bajorath J. Entering the ‘big data’ era in medicinal chemistry: molecular promiscuity analysis revisited. Future Science OA 3, FSO179, 2017.
572 Kayastha S, Horvath D, Gilberg E, Gütschow M, Bajorath J & Varnek A. Privileged structural motif detection and analysis using generative topographic maps. J Chem Inf Model 57, 1218-1232, 2017.
571 Jasial S, Hu Y & Bajorath J. How frequently are pan assay interference compounds active? Large-scale analysis of screening data reveals diverse activity profiles, low global hit frequency, and many consistently inactive compounds. J Med Chem 60, 3879-3886, 2017.
570 Hu Y, Kunimoto R & Bajorath J. Mapping of inhibitors and activity data to the human kinome and exploring promiscuity from a ligand and target perspective. Chem Biol Drug Des 89, 834-845, 2017.
569 Dimova D & Bajorath J. Assessing scaffold diversity of kinase inhibitors using alternative scaffold concepts and estimating the scaffold hopping potential for different kinases. Molecules 22, E730, 2017.
568 Hu Y, Jasial S, Gilberg E & Bajorath J. Structure-promiscuity relationship puzzles - extensively assayed analogs with large differences in target annotations. AAPS J 19, 856-864, 2017.
567 Rodríguez-Pérez R, Vogt M & Bajorath J. Influence of varying training set composition and size on support vector machine-based prediction of active compounds. J Chem Inf Model 57, 710-716, 2017.
566 Kunimoto R, Dimova D & Bajorath J. Application of a new scaffold concept for computational target deconvolution of chemical cancer cell line screens. ACS Omega 2, 1463-1468, 2017.
565 Häußler D, Schulz-Fincke A-C, Beckmann A-M, Keils A, Gilberg E, Mangold M, Bajorath J, Stirnberg M, Steinmetzer T & Gütschow M. A fluorescent-labeled phosphono bisbenzguanidine as activity-based probe for matriptase. Chemistry Eur J 23, 5205-5209, 2017.
564 Stumpfe D, Dimova D & Bajorath J. Systematic analysis of structural and activity relationships between conventional hierarchical and analog series-based scaffolds. RSC Adv 7, 18718-18723, 2017.
563 Hu Y, Stumpfe D & Bajorath J. Recent advances in scaffold hopping. J Med Chem 60, 1238-1246, 2017.
562 Channar PA, Shah SJ, Hassan S, Nisa ZU, Lecka J, Sévigny J, Bajorath J, Saeed A & Iqbal J. Isonicotinohydrazones as inhibitors of alkaline phosphatase and ecto-5’-nucleotidase. Chem Biol Drug Des 89, 365-370, 2017.
561 Kunimoto R, Vogt M & Bajorath J. Tracing compound pathways using chemical space networks. Med Chem Commun 8, 376-384, 2017.
560 de la Vega de León A, Lounkine E, Vogt M & Bajorath J. Design of diverse and focused compound libraries, in: Varnek A (Ed.) Tutorials in Chemoinformatics, Chapter 5, 85-101, John Wiley & Sons Ltd, Chichester, UK, 2017.
559 Vogt M & Bajorath J. Hierarchical clustering in R, in: Varnek A (Ed.) Tutorials in Chemoinformatics, Chapter 6, 105-118, John Wiley & Sons Ltd, Chichester, UK, 2017.
558 Balfer J, Bajorath J & Vogt M. Compound classification using the scikit-learn library, in: Varnek A (Ed.) Tutorials in Chemoinformatics, Chapter 14, 223-239, John Wiley & Sons Ltd, Chichester, UK, 2017.
557 Vogt M, de la Vega de León A & Bajorath J. Algorithmic chemoinformatics, in: Varnek A (Ed.) Tutorials in Chemoinformatics, Chapter 24, 395-448, John Wiley & Sons Ltd, Chichester, UK, 2017.
556 Müller G, Berkenbosch T, Benningshof JCJ, Stumpfe D & Bajorath J. Charting biologically relevant spirocyclic compound space. Chemistry Eur J 23, 703-710, 2017.
555 Dimova D, Gilberg E & Bajorath J. Identification and analysis of promiscuity cliffs formed by bioactive compounds and experimental implications. RSC Adv 7, 58-66, 2017.
554 Abdelrahman A, Namasivayam V, Hinz S, Schiedel AC, Köse M, Burton M, El-Tayeb A, Gillard M, Bajorath J, de Ryck M & Müller CE. Characterization of P2X4 receptor agonists and antagonists by calcium influx and radioligand binding studies. Biochem Pharmacol 125, 41-54, 2017.
553 Bajorath J. Molecular similarity concepts for informatics applications. Methods Mol Biol 1526, 247-256, 2017.
552 Bajorath J. Compound data mining for drug discovery. Methods Mol Biol 1526, 231-245, 2017.
551 Gilberg E, Jasial S, Stumpfe D, Dimova D & Bajorath J. Highly promiscuous small molecules from biological screening assays include many pan-assay interference compounds but also candidates for polypharmacology. J Med Chem 59, 10285-10290, 2016.
550 de la Vega de León A & Bajorath J. Design of chemical space networks incorporating compound distance relationships. F1000Research 5(Chem Inf Sci):2634, 2016.
549 Bajorath J. Analyzing promiscuity at the level of active compounds and targets. Mol Inf 35, 583-587, 2016.
548 Anighoro A, de la Vega de León A & Bajorath J. Predicting bioactive conformations and binding modes of macrocycles. J Comput-Aided Mol Des 30, 841-849, 2016.
547 Braune A, Engst W, Elsinghorst P, Furtmann N, Bajorath J, Gütschow M & Blaut M. Chalcone isomerase from Eubacterium ramulus catalyzes the ring contraction of flavanonols. J Bacteriol 198, 2965-2974, 2016.
546 Hu Y & Bajorath J. Hierarchical analysis of bioactive matched molecular pairs, encoded chemical transformations, and associated substructures. Mol Inf 35, 483-488, 2016.
545 Dimova D, Stumpfe D, Hu Y & Bajorath J. ASB scaffolds: computational design and exploration of a new type of molecular scaffolds for medicinal chemistry. Future Science OA 2, FSO149, 2016.
544 Bajorath J. Complexity and heterogeneity of data for chemical information science, in: Bienstock RJ, Shanmugasundaram V, Bajorath J (Eds.) Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: Jürgen Bajorath. ACS Symposium Series 1222, Chapter 2, 9-17, 2016.
543 Hu Y & Bajorath J. Exploring molecular promiscuity from a ligand and target perspective, in: Bienstock RJ, Shanmugasundaram V, Bajorath J (Eds.) Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: Jürgen Bajorath. ACS Symposium Series 1222, Chapter 3, 19-34, 2016.
542 Hu Y & Bajorath J. Network variants for analyzing ligand-target interactions, in: Bienstock RJ, Shanmugasundaram V, Bajorath J (Eds.) Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: Jürgen Bajorath. ACS Symposium Series 1222, Chapter 4, 35-51, 2016.
541 Bajorath J, Jenkins J, Overington J & Walters WP. Drug discovery and development in the era of big data. Future Med Chem 8, 1807-1813, 2016.
540 Bajorath J. Pharmacophore, in: Encyclopedia of Cancer, 4th Edition, Springer-Verlag, Heidelberg, doi: 10.1007/978-3-642-27841-9_4502-2, 2016.
539 Bajorath J. Quantitative Structure Activity Relationship, in: Encyclopedia of Cancer, 4th Edition, Springer-Verlag, Heidelberg, doi: 10.1007/978-3-642-27841-9_4882-2, 2016.
538 Bajorath J. Computational chemistry and computer-aided drug discovery: part II. Future Med Chem 8, 1799-1800, 2016.
537 Horvath D, Marcou G, Varnek A, Kayastha S, de la Vega de León A & Bajorath J. Prediction of activity cliffs using condensed graphs of reaction representations, descriptor recombination, support vector machine classification, and support vector regression. J Chem Inf Model 56, 1631-1640, 2016.
536 de la Vega de León A & Bajorath J. Chemical space visualization: transforming multi-dimensional chemical spaces into similarity-based molecular networks. Future Med Chem 8, 1769-1778, 2016.
535 Bajorath J. Computational chemistry and computer-aided drug discovery: part I. Future Med Chem 8, 1705-1706, 2016.
534 Stumpfe D, Dimova D & Bajorath J. Computational method for the systematic identification of analog series and key compounds representing series and their biological activity profiles. J Med Chem 59, 7667-7676, 2016.
533 Bajorath J. Extending accessible chemical space for the identification of novel leads. Expert Opin Drug Discov 11, 825-829, 2016.
532 Kunimoto R, Vogt M & Bajorath J. Maximum common substructure-based Tversky index - an asymmetric hybrid similarity measure. J Comput-Aided Mol Des 30, 523-531, 2016.
531 Ibrar A, Tehseen Y, Khan I, Hameed A, Saeed A, Furtmann N, Bajorath J & Iqbal J. Coumarin-thiazole and -oxadiazole derivatives: synthesis, bioactivity and docking studies for aldose/aldehyde reductase inhibitors. Bioorg Chem 68, 177-186, 2016.
530 Anighoro A & Bajorath J. Binding mode similarity measures for ranking of docking poses - a case study on the adenosine A2A receptor. J Comput-Aided Mol Des 30, 447-456, 2016.
529 Beckmann A-M, Gilberg E, Gattner S, Huang TL, Vanden Eynde JJ, Mayence A, Bajorath J, Stirnberg M & Gütschow M. Evaluation of bisbenzamidines as inhibitors for matriptase-2. Bioorg Med Chem Lett 26, 3741-3745, 2016.
528 Bajorath J. Computational methods for chemical biology. Understanding biological pathways through computer-aided chemistry. GIT Lab J Eur 7/8, 14-18, 2016.
527 Hu Y & Bajorath J. Analyzing compound activity records and promiscuity degrees in light of publication statistics. F1000Research 5(Chem Inf Sci):1227, 2016.
526 Hu Y & Bajorath J. Systematic assessment of molecular selectivity at the level of targets, bioactive compounds, and structural analogs. ChemMedChem 11, 1362-1370, 2016.
525 Stumpfe D & Bajorath J. Recent developments in SAR visualization. Med Chem Commun 7, 1045-1055, 2016.
524 Häußler D, Mangold M, Furtmann N, Braune A, Blaut M, Bajorath J, Stirnberg M & Gütschow M. Phospho bisbenzguanidines as irreversible dipeptidomimetic inhibitors and activity-based probes of matriptase-2. Chemistry 22, 8525-8535, 2016.
523 Dimova D & Bajorath J. Systematic design of analogs of active compounds covering more than 1000 targets. Med Chem Commun 7, 859-863, 2016.
522 Shanmugasundaram V, Zhang L, Kayastha S, de la Vega de León A, Dimova D & Bajorath J. Monitoring the progression of structure-activity relationship information during lead optimization. J Med Chem 59, 4235-4244, 2016.
521 Hu Y, Stumpfe D & Bajorath J. Computational exploration of molecular scaffolds in medicinal chemistry. J Med Chem 59, 4062-4076, 2016.
520 Shoichet BK, Walters WP, Jiang H & Bajorath J. Advances in computational medicinal chemistry: a reflection on the evolution of the field and perspective going forward. J Med Chem 59, 4033-4034, 2016.
519 Dimova D & Bajorath J. Advances in activity cliff research. Mol Inf 35, 181-191, 2016.
518 Jasial S, Hu Y & Bajorath J. Determining the degree of promiscuity of extensively assayed compounds. PLoS One, 11, e0153873, 2016.
517 Jasial S, Hu Y, Vogt M and Bajorath J. Activity-relevant similarity values for fingerprints and implications for similarity searching. F1000Research 5(Chem Inf Sci):591, 2016.
516 Beckmann A-M, Maurer E, Lülsdorff V, Wilms A, Furtmann N, Bajorath J, Gütschow M & Stirnberg M. En route to new therapeutic options for iron overload diseases: matriptase-2 as a target for Kunitz-type inhibitors. ChemBioChem 17, 595-604, 2016.
515 Anighoro A & Bajorath J. Three-dimensional similarity in molecular docking: prioritizing ligand poses on the basis of experimental binding modes. J Chem Inf Model 56, 580-587, 2016.
514 Vogt M, Stumpfe D, Maggiora GM & Bajorath J. Lessons learned from the design of chemical space networks and opportunities for new applications. J Comput-Aided Mol Des 30, 191-208, 2016.
513 Jasial S, Hu Y & Bajorath J. Assessing the growth of bioactive compounds and scaffolds over time: implications for lead discovery and scaffold hopping. J Chem Inf Model 56, 300-307, 2016.
512 Ghosh A, Dimova D & Bajorath J. Classification of matching molecular series on the basis of SAR phenotypes and structural relationships. Med Chem Commun 7, 237-246, 2016.
511 Dimova D, Stumpfe D & Bajorath J. Systematic assessment of analog relationships between bioactive compounds and promiscuity of analog sets. Med Chem Commun 7, 230-236, 2016.
510 Wu M, Vogt M, Maggiora GM & Bajorath J. Design of chemical space networks on the basis of Tversky similarity. J Comput-Aided Mol Des 30, 1-12, 2016.
509 Hameed A, Zehra ST, Abbas S, Nisa RU, Mahmmod T, Ayub K, Al-Rashida M, Bajorath J, Khan KM & Iqbal J. One-pot synthesis of tetrazole-1,2,5,6-tetrahydronicotinonitriles and cholinesterase inhibition: probing the plausible reaction mechanism via computational studies. Bioorg Chem 65, 38-47, 2016.
508 Tropsha A & Bajorath J. Computational methods for drug discovery and design. J Med Chem 59, 1-1, 2016.
507 Furtmann N, Häußler D, Scheidt T, Stirnberg M, Steinmetzer T, Bajorath J & Gütschow M. Limiting the number of potential binding modes by introducing symmetry into ligands: structure-based design of inhibitors for trypsin-like serine proteases. Chemistry 22, 610-625, 2016.
506 Bajorath J. Pushing the boundaries of computational approaches: special focus issue on computational chemistry and computer-aided drug discovery. Future Med Chem 7, 2415-2417, 2015.
505 Walters WP & Bajorath J. On the evolving open peer review culture for chemical information science. F1000Research 4(Chem Inf Sci):1350, 2015.
504 Furtmann N, Hu Y, Gütschow M & Bajorath J. Identification of interaction hotspots in structures of drug targets on the basis of three-dimensional activity cliff information. Chem Biol Drug Des 86, 1458-1465, 2015.
503 Hu Y, Zhang B, Vogt M and Bajorath J. AnalogExplorer2 – Stereochemistry sensitive graphical analysis of large analog series. F1000Research 4(Chem Inf Sci):1031, 2015.
502 Zhang B, Vogt M, Maggiora GM & Bajorath J. Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures. J Comput-Aided Mol Des 29, 937-950, 2015.
501 Dimova D, Stumpfe D & Bajorath J. Identification of orthologous target pairs with shared active compounds and comparison of organism-specific activity patterns. Chem Biol Drug Des 86, 1105-1114, 2015.
500 Hameed A, Zehra ST, Shah SJA, Khan KM, Alharthy RD, Furtmann N, Bajorath J, Tahir MN & Iqbal J. Syntheses, cholinesterases inhibition and molecular docking studies of pyrido[2,3-b]pyrazine derivatives. Chem Biol Drug Des 86, 1115-1120, 2015.
499 Kohl F, Schmitz J, Furtmann N, Schulz-Fincke A-C, Mertens MD, Küppers J, Benkhoff M, Tobiasch E, Bartz U, Bajorath J, Stirnberg M & Gütschow M. Design, characterization and cellular uptake studies of fluorescence-labeled prototypic cathepsin inhibitors. Org Biomol Chem 13, 10310-10323, 2015.
498 Stumpfe D & Bajorath J. Monitoring global growth of activity cliff information over time and assessing activity cliff frequencies and distributions. Future Med Chem 7, 1565-1579, 2015.
497 Ghareeb D, Khalil S, Hafez HS, Bajorath J, Ahmed HEA, Sarhan E, El-Wakeel E & El-Demellawy MA. Berberine reduces neurotoxicity related to non-alcoholic steatohepatitis in rats. Evid Based Complement Alternat Med 2015, e361847, 2015.
496 Bajorath J. Computer-aided drug discovery. F1000Research 4(F1000 Faculty Rev):630, 2015.
495 de la Vega de León A, Kayastha S, Dimova D, Schultz T & Bajorath J. Visualization of multi-property landscapes for compound selection and optimization. J Comput-Aided Mol Des 29, 695-705, 2015.
494 Hu Y, Zhang B & Bajorath J. Method for systematic assessment of chemical changes in molecular scaffolds with conserved topology and application to the analysis of scaffold-activity relationships. Mol Inf 34, 531-549, 2015.
493 Schmitz J, Furtmann N, Ponert M, Frizler M, Löser R, Bartz U, Bajorath J & Gütschow M. Active site mapping of human cathepsin F with dipeptide nitrile inhibitors. ChemMedChem 10, 1365-1377, 2015.
492 Balfer J & Bajorath J. Visualization and interpretation of support vector machine activity predictions. J Chem Inf Model 55, 1136-1147, 2015.
491 Hu Y, Furtmann N, Stumpfe D & Bajorath J. Comprehensive knowledge base of two- and three-dimensional activity cliffs for medicinal and computational chemistry. F1000Research 4(Chem Inf Sci):168, 2015.
490 Zhang B, Vogt M, Maggiora GM & Bajorath J. Comparison of bioactive chemical space networks generated using substructure- and fingerprint-based measures of molecular similarity. J Comput-Aided Mol Des 29, 595-608, 2015.
489 Hameed A, Khan K, Zehra S, Ahmed R, Shafiq Z, Bakht S, Yaqub M, Hussain M, de la Vega de León A, Furtmann N, Bajorath J, Ahmad H, Tahir M & Iqbal J. Synthesis, biological evaluation and molecular docking of N-phenyl thiosemicarbazones as urease inhibitors. Bioorg Chem 61, 51-57, 2015.
488 Kayastha S, de la Vega de León A, Dimova D & Bajorath J. Target-based analysis of ionization states of bioactive compounds. Med Chem Commun 6, 1030-1035, 2015.
487 Stumpfe D, Dimova D & Bajorath J. Systematic assessment of scaffold hopping versus activity cliff formation across bioactive compound classes following a molecular hierarchy. Bioorg Med Chem 23, 3183-3191, 2015.
486 Hu Y, Jasial S & Bajorath J. Promiscuity progression of bioactive compounds over time. F1000Research 4(Chem Inf Sci):118, 2015.
485 Hu Y & Bajorath J. Quantifying the tendency of therapeutic target proteins to bind promiscuous or selective compounds. PLoS One 10, e0126838, 2015.
484 Furtmann N, Hu Y, Gütschow M & Bajorath J. Identification and analysis of currently available high-confidence three-dimensional activity cliffs. RSC Adv 5, 43660-43668, 2015.
483 Hu Y, Furtmann N & Bajorath J. Extension of three-dimensional activity cliff information through systematic mapping of active analogs. RSC Adv 5, 43006-43015, 2015.
482 Hu Y & Bajorath. Structural and activity profile relationships between drug scaffolds. AAPS J 17, 609-619, 2015.
481 Dimova D, Stumpfe D, Hu Y & Bajorath J. Activity cliff clusters as a source of structure-activity relationship information. Expert Opin Drug Discov 10, 441-447, 2015.
480 Gupta-Ostermann D, Hirose Y, Odagami T, Kouji H & Bajorath J. Follow-up: Prospective compound design using the ‘SAR Matrix’ method and matrix-derived conditional probabilities of activity. F1000Research 4:75, 2015.
479 Balfer J & Bajorath J. Systematic artifacts in support vector regression-based compound potency prediction revealed by statistical and activity landscape analysis. PLoS One 10, e0119301, 2015.
478 Anighoro A, Stumpfe D, Heikamp K, Beebe K, Neckers LM, Bajorath J & Rastelli G. Computational polypharmacology analysis of the heat shock protein 90 interactome. J Chem Inf Model 55, 676-686, 2015.
477 Garnett R, Gärtner T, Vogt M & Bajorath J. Introducing the 'active search' method for iterative virtual screening. J Comput-Aided Mol Des 29, 305-314, 2015.
476 Gupta-Ostermann D, Balfer J & Bajorath J. Hit expansion from screening data based upon conditional probabilities of activity derived from SAR matrices. Mol Inf 34, 134-146, 2015.
475 Jasial S, Balfer J, Vogt M & Bajorath J. Determination of meta-parameters for support vector machine linear combinations. Mol Inf 34, 127-133, 2015.
474 Furtmann N & Bajorath J. Structural and modeling studies on ecto-5’-nucleotidase aiding in inhibitor design. Mini Rev Med Chem 15, 34-40, 2015.
473 Kayastha S, Dimova D, Stumpfe D & Bajorath J. Structural diversity and potency range distribution of scaffolds from compounds active against current pharmaceutical targets. Future Med Chem 7, 111-122, 2015.
472 Bajorath J, Jiang H, Shoichet BK, Walters WP. Computational methods for medicinal chemistry. J Med Chem 58, 1019-1019, 2015.
471 Bajorath J. Entering new publication territory in chemoinformatics and chemical information science. F1000Research 4:35, 2015.
470 Zwierzyna M, Vogt M, Maggiora GM & Bajorath J. Design and characterization of chemical space networks for different compound data sets. J Comput-Aided Mol Des 29, 113-125, 2015.
469 Hu Y & Bajorath J. Exploring the scaffold universe of kinase inhibitors. J Med Chem 58, 315-332, 2015.
468 Furtmann N, Hu Y & Bajorath J. Comprehensive analysis of three-dimensional activity cliffs formed by kinase inhibitors with different binding modes and cliff mapping of structural analogs. J Med Chem 58, 252-264, 2015.
467 Hu Y, Furtmann N & Bajorath J. Current compound coverage of the kinome. J Med Chem 58, 30-40, 2015.
466 Laufer S & Bajorath J. Advancing the kinase field: new targets and second generation inhibitors. J. Med Chem 58, 1-1, 2015.
465 Dimova D, Stumpfe D & Bajorath J. Systematic assessment of coordinated activity cliffs formed by kinase inhibitors and detailed characterization of activity cliff clusters and associated SAR information. Eur J Med Chem 90, 414-427, 2015.
464 Hu Y & Bajorath J. Influence of search parameters and criteria on compound selection, promiscuity, and pan assay interference characteristics. J Chem Inf Model 54, 3056-3066, 2014.
463 Zhang B, Hu Y & Bajorath J. AnalogExplorer – a new method for graphical analysis of analog series and associated structure-activity relationship information. J Med Chem 57, 9184-9194, 2014.
462 Bajorath J. Evolution of the activity cliff concept for SAR analysis and drug discovery. Future Med Chem 6, 1545-1549, 2014.
461 de la Vega de León A & Bajorath J. Prediction of compound potency changes in matched molecular pairs using support vector regression. J Chem Inf Model 54, 2654-2663, 2014.
460 Bajorath J. On data sharing in computational drug discovery and the need for data notes [v1; ref status: not peer reviewed, f1000r.es/4qi] F1000Research 3:280 (doi: 10.12688/f1000research.5742.1), 2014.
459 Dimova D & Bajorath J. Extraction of structure-activity relationship information from activity cliff clusters via matching molecular series. Eur J Med Chem 87, 454-460, 2014.
458 Hu Y & Bajorath J. Monitoring drug promiscuity over time [v2; ref status: indexed, f1000r.es/4bh] F1000Research 3:218 (doi: 10.12688/f1000research.5250.1), 2014.
457 Khan I, Ibrar A, Zaib S, Ahmad S, Furtmann N, Hameed S, Simpson J, Bajorath J & Iqbal J. Active compounds from a diverse library of triazolothiadiazole and triazolothiadiazine scaffolds: synthesis, crystal structure determination, cytotoxicity, cholinesterase inhibitory activity, and binding mode analysis. Bioorg Med Chem 22, 6163-6173, 2014.
456 Bajorath J. Activity artifacts in drug discovery and different facets of compound promiscuity [v1; ref status: indexed, f1000r.es/4gz] F1000Research 3:233 (doi: 10.12688/f1000research.5426.1), 2014.
455 Anighoro A, Bajorath J & Rastelli G. Polypharmacology: challenges and opportunities in drug discovery. J Med Chem 57, 7874-7887, 2014.
454 Zhang B, Vogt M & Bajorath J. Design of an activity landscape view taking compound-based feature probabilities into account. J Comput-Aided Mol Des 28, 919-926, 2014.
453 Balfer J & Bajorath J. Introduction of a methodology for visualization and graphical interpretation of Bayesian classification models. J Chem Inf Model 54, 2451-2456, 2014.
452 Stumpfe D & Bajorath J. Activity cliff networks for medicinal chemistry. Drug Dev Res 75, 291-298, 2014.
451 Dimova D, Stumpfe D & Bajorath J. Method for the evaluation of structure-activity relationship information associated with coordinated activity cliffs. J Med Chem 57, 6553-6563, 2014.
450 Saeed A, Tehseen Y, Rafique H, Furtmann N, Bajorath J, Flörke U & Iqbal J. Benzothiazolyl substituted iminothiazolidinones and benzamido-oxothiazolidines as potent and partly selective aldose reductase inhibitors. Med Chem Commun 5, 1371-1380, 2014.
449 Balfer J, Hu Y & Bajorath, J. Compound structure-independent activity prediction in high-dimensional target space. Mol Inf 33, 544-558, 2014.
448 Maggiora G & Bajorath J. Chemical space networks - a powerful new paradigm for the description of chemical space. J Comput-Aided Mol Des 28, 795-802, 2014.
447 Hu Y, Lounkine E & Bajorath J. Many approved drugs have bioactive analogs with different target annotations. AAPS J 16, 847-859, 2014.
446 Bajorath J. Exploring activity cliffs from a chemoinformatics perspective. Mol Inf 33, 438-442, 2014.
445 Balfer J, Heikamp K, Laufer S & Bajorath J. Modeling of compound profiling experiments using support vector machines. Chem Biol Drug Des 84, 75-85, 2014. dx.doi.org/10.1111/cbdd.12294
444 Gupta-Ostermann D & Bajorath J. The ‘SAR Matrix’ method and its extensions for applications in medicinal chemistry and chemogenomics [v2; ref status: indexed, f1000r.es/3gh] F1000Research 3:113 (doi: 10.12688/f1000research.4185.2), 2014.
443 Dimova D, Stumpfe D & Bajorath J. Specific chemical changes leading to consistent potency increases in structurally diverse active compounds. Med Chem Commun 5, 742-749, 2014. dx.doi.org/10.1039/C4MD00029C
442 Namasivayam V, Gupta-Ostermann D, Balfer J, Heikamp K & Bajorath J. Prediction of compounds in different local structure-activity relationship environments using emerging chemical patterns. J Chem Inf Model 54, 1301-1310, 2014. dx.doi.org/10.1021/ci500147b
441 Maggiora G, Vogt M, Stumpfe D & Bajorath J. Molecular similarity in medicinal chemistry. J Med Chem 57, 3186-3204, 2014. dx.doi.org/10.1021/jm401411z
440 de la Vega de León A, Hu Y & Bajorath J. Systematic identification of matching molecular series and mapping of screening hits. Mol Inf 33, 257-263, 2014. dx.doi.org/10.1002/minf.201400017
439 Mertens MM, Schmitz J, Horn M, Furtmann N, Bajorath J, Mares M & Gütschow M. A coumarin-labeled vinyl sulfone as tripeptidomimetic activity-based probe for cysteine cathepsins. ChemBioChem 15, 955-959, 2014. dx.doi.org/10.1002/cbic.201300806
438 Hu Y & Bajorath J. Learning from 'big data': compounds and targets. Drug Discov Today 19, 357-360, 2014. dx.doi.org/10.1016/j.drudis.2014.02.004
437 Hu Y & Bajorath J. Compound data sets and software tools for chemoinformatics and medicinal chemistry applications: update and data transfer [v1; ref status: indexed, f1000r.es/32j] F1000Research 3:69, 2014. dx.doi.org/10.12688/f1000research.3979
436 Gupta-Ostermann D, Shanmugasundaram V & Bajorath J. Neighborhood-based prediction of novel active compounds from SAR matrices. J Chem Inf Model 54, 801-809, 2014. dx.doi.org/10.1021/ci5000483
435 Stumpfe D, de la Vega de León A, Dimova D & Bajorath J. Advancing the activity cliff concept, part II [v1; ref status: indexed, f1000r.es/34p] F1000Research 3:75, 2014. dx.doi.org/10.12688/f1000research.4057
434 Aslam S, Zaib S, Ahmad M, Gardiner JM, Ahmad A, Hameed A, Furtmann N, Gütschow M, Bajorath J & Iqbal J. Novel structural hybrids of pyrazolobenzothiazines with benzimidazoles as cholinesterase inhibitors. Eur J Med Chem 78, 106-117, 2014. dx.doi.org/10.1016/j.ejmech.2014.03.035
433 Laufer S & Bajorath J. New frontiers in kinases: second generation inhibitors. J Med Chem 57, 2167-2168, 2014. dx.doi.org/10.1021/jm500195x
432 Hu Y & Bajorath J. Many drugs contain unique scaffolds with varying structural relationships to scaffolds of currently available bioactive compounds. Eur J Med Chem 76, 427-434, 2014. dx.doi.org/10.1016/j.ejmech.2014.02.040
431 Bajorath J. Improving data mining strategies for drug design. Future Med Chem 6, 255-257, 2014. dx.doi.org/10.4155/fmc.13.208
430 de la Vega de León A & Bajorath J. Formation of activity cliffs is accompanied by systematic increases in ligand efficiency from lowly to highly potent compounds. AAPS J 16, 335-341, 2014. dx.doi.org/10.1208/s12248-014-9567-x
429 Stumpfe D, Dimova D & Bajorath J. Composition and topology of activity cliff clusters formed by bioactive compounds. J Chem Inf Model 54, 451-461, 2014. dx.doi.org/10.1021/ci400728r
428 Kayastha S, Dimova D, Iyer P, Vogt M & Bajorath J. Large-scale assessment of activity landscape feature probabilities of bioactive compounds. J Chem Inf Model 54, 442-450, 2014. dx.doi.org/10.1021/ci400677b
427 Hu Y, de la Vega de León A, Zhang & Bajorath J. Matched molecular pair-based data sets for computer-aided medicinal chemistry [v2; ref status: indexed, f1000r.es/2w9] F1000Research 3:36, 2014. dx.doi.org/10.12688/f1000research.3-36.v2
426 Hu Y, Gupta-Ostermann D & Bajorath J. Exploring compound promiscuity patterns and multi-target activity spaces. Comput Struct Biotech J 9, e201401003, 2014. dx.doi.org/10.5936/csbj.201401003
425 Stumpfe D, Hu Y, Dimova D & Bajorath J. Recent progress in understanding activity cliffs and their utility in medicinal chemistry. J Med Chem 57, 18-28, 2014. dx.doi.org/10.1021/jm401120g
424 de la Vega de León A & Bajorath J. Matched molecular pairs derived by retrosynthetic fragmentation. Med Chem Commun 5, 64-67, 2014. dx.doi.org/10.1039/C3MD00259D
423 Wysocka M, Gruba N, Miecznikowska A, Popow-Stellmaszyk J, Gütschow M, Stirnberg M, Furtmann N, Bajorath J, Lesner A, Rolka K. Substrate specificity of human matriptase-2. Biochimie 97, 121-127, 2014. dx.doi.org/10.1016/j.biochi.2013.10.001
422 Heikamp K & Bajorath J. Support vector machines for drug discovery. Expert Opin Drug Discov 9, 93-104, 2014. dx.doi.org/10.1517/17460441.2014.866943
421 Hu Y & Bajorath J. Scaffold mining of publicly available compound data, in: Scaffold Hopping in Medicinal Chemistry. N. Brown (Ed.). Methods and Principles in Medicinal Chemistry 58, Wiley-VCH, Weinheim, 61-81, 2014.
420 Namasivayam V, Iyer P & Bajorath J. Prediction of individual compounds forming activity cliffs using emerging chemical patterns. J Chem Inf Model 53, 3131-3139, 2013. dx.doi.org/10.1021/ci400597d
419 Bajorath J. A perspective on computational chemogenomics. Mol Inf 32, 1025-1028, 2013. dx.doi.org/10.1002/minf.201300034
418 Hu Y, Stumpfe D & Bajorath J. Visualization of activity landscapes and chemogenomics data. Mol Inf 32, 954-963, 2013. dx.doi.org/10.1002/minf.201300044
417 Iqbal J, Saeed A, Raza R, Matin A, Hameed A, Furtmann N, Lecka J, Sévigny J & Bajorath J. Identification of sulfonic acids as efficient ecto-5'-nucleotidase inhibitors. Eur J Med Chem 70, 685-691, 2013. dx.doi.org/10.1016/j.ejmech.2013.10.053
416 Bajorath J. Molecular crime scene investigation - dusting for fingerprints. Drug Discov Today: Tech, e491-e498, 2013. dx.doi.org/10.1016/j.ddtec.2012.06.003
415 Hu Y, Stumpfe D & Bajorath J. Advancing the activity cliff concept. F1000Research 2:144, 2013. dx.doi.org/10.12688/f1000research.2-199.v1
414 Furtmann N & Bajorath J. Evaluation of molecular model-based discovery of ecto-5'-nucleotidase inhibitors on the basis of X-ray structures. Bioorg Med Chem 21, 6616-6622, 2013. dx.doi.org/10.1016/j.bmc.2013.08.021
413 Hu Y & Bajorath J. Activity profile relationships between structurally similar promiscuous compounds. Eur J Med Chem 69, 393-398, 2013. dx.doi.org/10.1016/j.ejmech.2013.08.044
412 Dimova D, Stumpfe D & Bajorath J. Quantifying the fingerprint descriptor dependence of structure-activity relationship information on a large scale. J Chem Inf Model 53, 2275-2281, 2013. dx.doi.org/10.1021/ci4004078
411 Balfer J, Vogt M & Bajorath J. Searching for closely related ligands with different mechanism-of-action using machine learning and mapping algorithms. J Chem Inf Model 53, 2252-2274, 2013. dx.doi.org/10.1021/ci400359n
410 Gupta-Ostermann D, Hu Y & Bajorath J. Systematic mining of analog series with related core structures in multi-target activity space. J Comput-Aided Mol Des 27, 665-674, 2013. dx.doi.org/10.1007/s10822-013-9671-5
409 Stumpfe D & Bajorath J. Critical assessment of virtual screening for hit identification, in: Chemoinformatics for Drug Discovery. J. Bajorath (Ed. ), John Wiley and Sons, Hoboken, 113-130, 2013. dx.doi.org/10.1002/9781118742785.ch6
408 Bajorath J. Chemoinformatics: from methods and models to pharmaceutical applications, in: Chemoinformatics for Drug Discovery. J. Bajorath (Ed. ), John Wiley and Sons, Hoboken, vii, 2013. dx.doi.org/10.1002/9781118742785.fmatter
407 Lentz CS, Stumpfe D, Bajorath J, Famulok M, Hoerauf A & Pfarr KM. New chemotypes for wALADin1-like inhibitors of delta-aminoevulinic acid dehydratase from Wolbachia endobacteria. Bioorg Med Chem Lett 23, 5558-5562, 2013. dx.doi.org/10.1016/j.bmcl.2013.08.052
406 Hu Y & Bajorath J. Promiscuity profiles of bioactive compounds: potency range and difference distributions and the relation to target numbers and families. Med Chem Commun 4, 1196-1201, 2013. dx.doi.org/10.1039/C3MD00105A
405 Vogt M & Bajorath J. Similarity searching for potent compounds using feature selection. J Chem Inf Model 53, 1613-1619, 2013. dx.doi.org/10.1021/ci4003206
404 Vogt M, Iyer P, Maggiora GM & Bajorath J. Conditional probabilities of activity landscape features for individual compounds. J Chem Inf Model 53, 1602-1612, 2013. dx.doi.org/10.1021/ci400288r
403 Heikamp K & Bajorath J. Comparison of inactive and randomly selected compounds as negative training examples in support vector machine-based virtual screening. J Chem Inf Model 53, 1595-1601, 2013. dx.doi.org/10.1021/ci4002712
402 Zhang B, Hu Y & Bajorath J. SAR transfer across different targets. J Chem Inf Model 53, 1589-1594, 2013. dx.doi.org/10.1021/ci400265b
401 Bajorath J. Hiking trails in activity landscapes - data visualization in medicinal chemistry. Lab&More 2/13, 9-13, 2013.
400 Iyer P, Stumpfe D, Vogt M, Bajorath J & Maggiora GM. Activity landscapes, information theory, and structure-activity relationships. Mol Inf 32, 421-430, 2013. dx.doi.org/10.1002/minf.201200120
399 Hu Y & Bajorath J. High-resolution view of compound promiscuity. F1000Research 2:144, 2013. dx.doi.org/10.12688/f1000research.2-144.v2
398 Bajorath J. Machine learning and similarity-based virtual screening techniques, in: In Silico Drug Discovery and Design Techniques. M. A. Lill (Ed.), Future Science Ltd. , London, 3-14, 2013. dx.doi.org/10.4155/ebo.12.419
397 Hu Y & Bajorath J. What is the likelihood of an active compound to be promiscuous? Systematic assessment of compound promiscuity on the basis of PubChem confirmatory bioassay data. AAPS J 15, 808-815, 2013. dx.doi.org/10.1208/s12248-013-9488-0
396 Hu Y & Bajorath J. Compound promiscuity - what can we learn from current data. Drug Discov Today 18, 644-650, 2013. dx.doi.org/10.1016/j.drudis.2013.03.002
395 Namasivayam V, Hu Y, Balfer J & Bajorath J. Classification of compounds with distinct or overlapping multi-target activities and diverse molecular mechanisms using emerging chemical patterns. J Chem Inf Model 53, 1272-1281, 2013. dx.doi.org/10.1021/ci400186n
394 de la Vega de León A & Bajorath J. Compound optimization through data set-dependent chemical transformations. J Chem Inf Model 53, 1263-1271, 2013. dx.doi.org/10.1021/ci400165a
393 Stumpfe D, Dimova D, Heikamp K & Bajorath J. Compound pathway model to capture SAR progression: comparison of activity cliff-dependent and -independent pathways. J Chem Inf Model 53, 1067-1072, 2013. dx.doi.org/10.1021/ci400141w
392 Bajorath J. Large-scale SAR analysis. Drug Discov Today: Tech 10, e419-e426, 2013. dx.doi.org/10.1016/j.ddtec.2013.01.002
391 Dimova D, Heikamp K, Stumpfe D & Bajorath J. Do medicinal chemists learn from activity cliffs? A systematic evaluation of cliff progression in evolving compound data sets. J Med Chem 56, 3339-3345, 2013. dx.doi.org/10.1021/jm400147j
390 Heikamp K & Bajorath J. Prediction of compounds with closely related activity profiles using weighted support vector machine linear combinations. J Chem Inf Model 53, 791-801, 2013. dx.doi.org/10.1021/ci400090t
389 Hu Y & Bajorath J. Introduction of target cliffs as a concept to identify and describe complex molecular selectivity patterns. J Chem Inf Model 53, 545-552, 2013. dx.doi.org/10.1021/ci300602m
388 Ahmadi M, Vogt M, Iyer P, Bajorath J & Fröhlich H. Predicting potent compounds via model-based global optimization. J Chem Inf Model 53, 553-559, 2013. dx.doi.org/10.1021/ci3004682
387 Anighoro A, Stumpfe D, Heikamp K, Bajorath J & Rastelli G. Targeting the Hsp90 interactome using in silico polypharmacology approaches. Chim Ind 3/13, 105-106, 2013.
386 Bajorath J. Virtual screening methods, in: Diversity-Oriented Synthesis: Basics and Applications in Organic Synthesis, Drug Discovery, and Chemical Biology. A. Trabocchi (Ed. ), Wiley-Blackwell, Hoboken, 483-505, 2013. dx.doi.org/10.1002/9781118618110.ch15
385 Bajorath J. Structure-activity relationship data analysis: activity landscapes and activity cliffs, in: Diversity-Oriented Synthesis: Basics and Applications in Organic Synthesis, Drug Discovery, and Chemical Biology. A. Trabocchi (Ed. ), Wiley-Blackwell, Hoboken, 507-531, 2013. dx.doi.org/10.1002/9781118618110.ch16
384 Hu Y, Maggiora GM & Bajorath J. Activity cliffs in PubChem confirmatory bioassays taking inactive compounds into account. J Comput-Aided Mol Des 227, 115-124, 2013. dx.doi.org/10.1007/s10822-012-9632-4
383 Elagawany M, Ibrahim MA, Ahmed HEA, El-Etrawy AS, Ghiaty A, El-Feky SA, Abdel-Samii ZK & Bajorath J. Design, synthesis, and molecular modelling of pyridazinone and phthalazinone derivatives as protein kinase inhibitors. Bioorg Med Chem Lett 23, 2007-2013, 2013. dx.doi.org/10.1016/j.bmcl.2013.02.027
382 Hu Y & Bajorath J. Systematic identification of scaffolds representing compounds active against individual targets and single or multiple target families. J Chem Inf Model 53, 312-326, 2013. dx.doi.org/10.1021/ci300616s
381 Hu Y & Bajorath J. How promiscuous are pharmaceutically relevant compounds? A data-driven assessment. AAPS J 15, 104-111, 2013. dx.doi.org/10.1208/s12248-012-9421-y
380 Heikamp K & Bajorath J. The future of virtual compound screening. Chem Biol Drug Des 81, 33-40, 2013. dx.doi.org/10.1111/cbdd.12054
379 Dimova D, Iyer P, Vogt M,Totzke F, Kubbutat MHG, Schächtele C, Laufer S & Bajorath J. Assessing the target differentiation potential of imidazole-based protein kinase inhibitors. J Med Chem 55, 11067-11071, 2012. dx.doi.org/10.1021/jm3014508
378 Zhang B, Wassermann AM, Vogt M & Bajorath J. Systematic assessment of compound series with SAR transfer potential. J Chem Inf Model 52, 3138-3143, 2012. dx.doi.org/10.1021/ci300481d
377 Wassermann AM, Dimova D, Iyer P & Bajorath J. Advances in computational medicinal chemistry - matched molecular pair analysis. Drug Develop Res 73, 518-527, 2012. dx.doi.org/10.1002/ddr.21045
376 Dimova D, Hu Y & Bajorath J. Matched molecular pair analysis of small molecule microarray data identifies promiscuity cliffs and reveals molecular origins of extreme compound promiscuity. J Med Chem 55, 10220-10228, 2012. dx.doi.org/10.1021/jm301292a
375 de la Vega de León A & Bajorath J. Design of a three-dimensional multi-target activity landscape. J Chem Inf Model 52, 2876-2883, 2012.
374 Namasivayam V & Bajorath J. Multi-objective particle swarm optimization: automated identification of SAR-informative compounds with favorable physicochemical properties. J Chem Inf Model 52, 2848-2855, 2012.
373 Li R & Bajorath J. Systematic assessment of scaffold distances in ChEMBL: prioritization of compound data sets for scaffold hopping analysis in virtual screening. J Comput-Aided Mol Des 26, 1101-1109, 2012. dx.doi.org/10.1007/s10822-012-9603-9
372 Heikamp K & Bajorath J. Fingerprint design and engineering strategies: rationalizing and improving similarity search performance. Future Med Chem 4, 1945-1959, 2012. dx.doi.org/10.4155/fmc.12.126
371 Gupta-Ostermann D & Bajorath J. Identification of multi-target activity ridges in high-dimensional bioactivity space. J. Chem Inf Model 52, 2579-2586, 2012. dx.doi.org/10.1021/ci3003683
370 Hu Y & Bajorath J. Growth of ligand-target interaction data in ChEMBL is associated with increasing and measurement-dependent compound promiscuity. J Chem Inf Model 52, 2550-2558, 2012. dx.doi.org/10.1021/ci3003304
369 Hu Y & Bajorath J. Rationalizing structure and target relationships between current drugs. AAPS J 14, 764-771, 2012. dx.doi.org/10.1208/s12248-012-9392-z
368 Heikamp K, Hu X, Yan A & Bajorath J. Prediction of activity cliffs using support vector machines. J Chem Inf Model 52, 2354-2365, 2012. dx.doi.org/10.1021/ci300306a
367 Stumpfe D & Bajorath J. Frequency of occurrence and potency range distribution of activity cliffs in bioactive compounds. J Chem Inf Model 52, 2348-2353, 2012. dx.doi.org/10.1021/ci300288f
366 Iyer P, Dimova D, Vogt M & Bajorath J. Navigating high-dimensional activity landscapes: design and application of the ligand-target differentiation map. J Chem Inf Model 52, 1962-1969, 2012. dx.doi.org/10.1021/ci3002765
365 Vogt M & Bajorath J. Chemoinformatics: a view of the field and current trends in method development. Bioorg Med Chem 20, 5317-5323, 2012. dx.doi.org/10.1016/j.bmc.2012.03.030
364 Bajorath J. Chemoinformatics: recent advances at the interfaces between computer and chemical information sciences, chemistry, and drug discovery. Bioorg Med Chem 20, 5316-5316, 2012. dx.doi.org/10.1016/j.bmc.2012.08.051
363 Hu Y & Bajorath J. Freely available compound data sets and software tools for chemoinformatics and computational medicinal chemistry applications. F1000 Research 1:11, 2012. dx.doi.org/10.3410/f1000research.1-11.v1
362 Ripphausen P, Freundlieb M, Brunschweiger A, Zimmermann H, Müller C & Bajorath J. Virtual screening identifies novel sulfonamide inhibitors of ecto-5'-nucleotidase. J Med Chem 55, 6576-6581, 2012. dx.doi.org/10.1021/jm300658n
361 Hu Y & Bajorath J. Extending the activity cliff concept: structural categorization of activity cliffs and systematic identification of different types of cliffs in the ChEMBL database. J Chem Inf Model 52, 1806-1811, 2012. dx.doi.org/10.1021/ci300274c
360 Wassermann AM, Haebel P, Weskamp N & Bajorath J. SAR matrices: automated extraction of information-rich SAR tables from large compound data sets. J Chem Inf Model 52, 1769-1776, 2012. dx.doi.org/10.1021/ci300206e
359 Hu Y, Furtmann N, Gütschow M & Bajorath J. Systematic identification and classification of three-dimensional activity cliffs. J Chem Inf Model 52, 1490-1498, 2012. dx.doi.org/10.1021/ci300158v
358 Gupta-Ostermann D, Hu Y & Bajorath J. Introducing the LASSO graph for compound data set representation and structure-activity relationship analysis. J Med Chem 55, 5546-5553, 2012. dx.doi.org/10.1021/jm3004762
357 Hu X, Hu Y, Vogt M, Stumpfe D & Bajorath J. MMP-cliffs: systematic identification of activity cliffs on the basis of matched molecular pairs. J Chem Inf Model 52, 1138-1145, 2012. dx.doi.org/10.1021/ci3001138
356 Bajorath J. Modeling of activity landscapes for drug discovery. Expert Opin Drug Discov 7, 463-473, 2012. dx.doi.org/10.1517/17460441.2012.679616
355 Bajorath J. Progress in computational medicinal chemistry. J Med Chem 55, 3593-3594, 2012. dx.doi.org/10.1021/jm300429z
354 Gupta-Ostermann D, Wawer M, Wassermann AM & Bajorath J. Graph mining for SAR transfer series. J Chem Inf Model 52, 935-942, 2012. dx.doi.org/10.1021/ci300071y
353 Namasivayam V & Bajorath J. Searching for coordinated activity cliffs using particle swarm optimization. J Chem Inf Model 52, 927-934, 2012. dx.doi.org/10.1021/ci3000503
352 Stumpfe D & Bajorath J. Exploring activity cliffs in medicinal chemistry. J Med Chem 55, 2932-2942, 2012. dx.doi.org/10.1021/jm201706b
351 Hu Y & Bajorath J. Exploration of 3D activity cliffs on the basis of compound binding modes and comparison of 2D and 3D cliffs. J Chem Inf Model 52, 670-677, 2012. dx.doi.org/10.1021/ci300033e
350 Stumpfe D, Ripphausen P & Bajorath J. Virtual compound screening in drug discovery. Future Med Chem 4, 593-602, 2012. dx.doi.org/10.4155/fmc.12.19
349 Ripphausen P, Stumpfe D & Bajorath J. Analysis of structure-based virtual screening studies and characterization of identified active compounds. Future Med Chem 4, 603-613, 2012. dx.doi.org/10.4155/fmc.12.18
348 Hu Y & Bajorath J. Many structurally related drugs bind different targets whereas distinct drugs display significant target overlap. RSC Adv 2, 3481-3489, 2012. dx.doi.org/10.1039/C2RA01345B
347 Iyer P & Bajorath J. Mechanism-based bipartite matching molecular series graphs to identify structural modifications of receptor ligands that lead to mechanism hopping. Med Chem Commun 3, 441-448, 2012. dx.doi.org/10.1039/C2MD00281G
346 Dimova D & Bajorath J. Computational chemical biology: identification of small molecular probes that discriminate between members of target families. Chem Biol Drug Des 79, 369-375, 2012. dx.doi.org/10.1111/j.1747-0285.2011.01297.x
345 Wassermann AM & Bajorath J. Directed R-group combination graph: a methodology to uncover structure-activity relationship patterns in series of analogs. J Med Chem 55, 1215-1226, 2012. dx.doi.org/10.1021/jm201362h
344 Auer J, Vogt M & Bajorath J. Emerging chemical patterns - theory and applications, in: Contrast Data Mining: Concepts, Algorithms, and Applications. G. Dong, J. Bailey (Eds.). Data Mining and Knowledge Discovery Series, Chapman & Hall / CRC, Boca Raton, 243-260, 2012.
343 Bajorath J. Computational chemistry in pharmaceutical research: at the crossroads. J Comput-Aided Mol Des 26, 11-12, 2012. dx.doi.org/10.1007/s10822-011-9488-z
342 Stumpfe D & Bajorath J. Methods for SAR visualization. RSC Adv 2, 369-378, 2012. dx.doi.org/10.1039/C1RA00924A
341 Namasivayam V, Iyer P & Bajorath J. Exploring SAR continuity in the vicinity of activity cliffs. Chem Biol Drug Des 79, 22-29, 2012. dx.doi.org/10.1111/j.1747-0285.2011.01256.x
340 Maurer E, Sisay, MT, Stirnberg M, Steinmetzer T, Bajorath J & Gütschow M. Insights into matriptase-2 substrate binding and inhibition mechanisms by analyzing active site-mutated variants. ChemMedChem 7, 68-72, 2012. dx.doi.org/10.1002/cmdc.201100350
339 Vogt M & Bajorath J. Statistical methods for predicting compound recovery rates for ligand-based virtual screening and assessing the probability of activity, in: Statistical Modeling of Molecular Descriptors in QSAR/QSPR. D. Bonchev, M. Dehmer, K. Varmuza (Eds. ), Wiley-VCH, Weinheim, 229-243, 2012.
338 Wassermann AM, Nisius B, Vogt M & Bajorath J. Information entropic functions for molecular descriptor profiling. Methods Mol Biol 819, 43-55, 2012. dx.doi.org/10.1007/978-1-61779-465-0_4
337 Hu Y & Bajorath J. Target family-directed exploration of scaffolds with different SAR profiles. J Chem Inf Model 51, 3138-3148, 2011. dx.doi.org/10.1021/ci200461w
336 Stumpfe D & Bajorath J. Assessing the confidence level of public domain compound activity data and the impact of alternative potency measurements on SAR analysis. J Chem Inf Model 51, 3131-3137, 2011. dx.doi.org/10.1021/ci2004434
335 Hu Y & Bajorath J. Activity profile sequences: a concept to account for the progression of compound activity in target space and extract SAR information from analog series with multiple target annotations. ChemMedChem 6, 2150-2154, 2011. dx.doi.org/10.1002/cmdc.201100395
334 Li R, Stumpfe D, Vogt M, Geppert H & Bajorath J. Development of a method to consistently quantify the structural distance between scaffolds and to assess scaffold hopping potential. J Chem Inf Model 51, 2507-2514, 2011. dx.doi.org/10.1021/ci2003945
333 Vogt M & Bajorath J. Introduction of the Conditional Correlated Bernoulli Model of similarity value distributions and its application to the prospective prediction of fingerprint search performance. J Chem Inf Model 51, 2496-2506, 2011. dx.doi.org/10.1021/ci2003472
332 Ripphausen P, Wassermann AM & Bajorath J. REPROVIS-DB: a benchmark system for ligand-based virtual screening derived from reproducible prospective applications. J Chem Inf Model 51, 2467-2473, 2011. dx.doi.org/10.1021/ci200309j
331 Iyer P & Bajorath J. Representation of multi-target activity landscapes through target pair-based compound encoding in self-organizing maps. Chem Biol Drug Des 78, 778-786, 2011. dx.doi.org/10.1111/j.1747-0285.2011.01235.x
330 Heikamp K & Bajorath J. How do 2D fingerprints detect structurally diverse active compounds? Revealing compound subset-specific fingerprint features through systematic selection. J Chem Inf Model 51, 2254-2265, 2011. dx.doi.org/10.1021/ci200275m
329 Wassermann AM & Bajorath J. A data mining method to facilitate SAR transfer. J Chem Inf Model 51, 1857-1866, 2011. dx.doi.org/10.1021/ci200254k
328 Vogt M, Huang Y & Bajorath J. From activity cliffs to activity ridges: informative data structures for SAR analysis. J Chem Inf Model 51, 1848-1856, 2011. dx.doi.org/10.1021/ci2002473
327 Heikamp K & Bajorath J. Large-scale similarity search profiling of ChEMBL compound data sets. J Chem Inf Model 51, 1831-1839, 2011. dx.doi.org/10.1021/ci200199u
326 Hu Y, Stumpfe D & Bajorath J. Lessons learned from molecular scaffold analysis. J Chem Inf Model 51, 1742-1753, 2011. dx.doi.org/10.1021/ci200179y
325 Namasivayam V, Iyer P & Bajorath J. Extraction of discontinuous structure-activity relationships from compound data sets through particle swarm optimization. J Chem Inf Model 51, 1545-1551, 2011. dx.doi.org/10.1021/ci2001692
324 Wassermann AM, Dimova D & Bajorath J. Comprehensive analysis of single- and multi-target activity cliffs formed by currently available bioactive compounds. Chem Biol Drug Des 78, 224-228, 2011. dx.doi.org/10.1111/j.1747-0285.2011.01150.x
323 Hu Y & Bajorath J. Chemical transformations that yield compounds with distinct activity profiles. ACS Med Chem Lett 2, 523-527, 2011. dx.doi.org/10.1021/ml2000609
322 Wassermann AM & Bajorath J. Identification of target family-directed bioisosteric replacements. Med Chem Commun 2, 601-606, 2011. dx.doi.org/10.1039/C1MD00066G
321 Iyer P, Stumpfe D & Bajorath J. Molecular mechanism-based network-like similarity graphs reveal relationships between different types of receptor ligands and structural changes that determine agonistic, inverse-agonistic, and antagonistic effects. J Chem Inf Model 51, 1281-1286, 2011. dx.doi.org/10.1021/ci2001378
320 Bajorath J, Barreca ML, Bender A, Bryce R, Hutter M, Laggner C, Laughton C, Martin Y, Mitchell J, Padova A, Renner S, Selzer PM, Sherman W, Sippl W, Taft C, Tuccinardi T, Vistoli G & Willett P. Ask the experts: focus on computational chemistry. Future Med Chem 3, 909-921, 2011. dx.doi.org/10.4155/fmc.11.57
319 Wassermann AM & Bajorath J. BindingDB and ChEMBL - online compound databases for drug discovery. Expert Opin Drug Discov 6, 683-687, 2011. dx.doi.org/10.1517/17460441.2011.579100
318 Bill A, Blockus H, Stumpfe D, Bajorath J, Schmitz A & Famulok M. A homogeneous FRET-system for monitoring the activation of a protein switch in real time. J Am Chem Soc 133, 8372-8379, 2011. dx.doi.org/10.1021/ja202513s
317 Ripphausen P, Nisius B & Bajorath J. State-of-the-art in ligand-based virtual screening. Drug Discov Today 16, 372-376, 2011. dx.doi.org/10.1016/j.drudis.2011.02.011
316 Batista J, Schlechtingen G, Friedrichson, T, Braxmeier T & Bajorath J. Lipid-like sulfoxides and amine oxides as inhibitors of mast cell activation. Eur J Med Chem 46, 2147-2151, 2011. dx.doi.org/10.1016/j.ejmech.2011.02.068
315 Wawer M & Bajorath J. Local structural changes, global data views: graphical substructure-activity relationship trailing. J Med Chem 54, 2944-2951, 2011. dx.doi.org/10.1021/jm200026b
314 Ripphausen P, Nisius B, Wawer M & Bajorath J. Rationalizing the role of SAR tolerance for ligand-based virtual screening. J Chem Inf Model 51, 837-842, 2011. dx.doi.org/10.1021/ci200064c
313 Fustero S, Rodrigo V, Sánchez-Roselló M, del Pozo C, Timoneda J, Frizler M, Sisay MT, Bajorath J, Calle LP, Cañada FJ, Jiménez-Barbero J & Gütschow M. New cathepsin inhibitors to explore the fluorophilic properties of the S2 pocket of cathepsin B: design, synthesis and biological evaluation. Chem Eur J 17, 5256-5260, 2011.
312 Wassermann AM & Bajorath J. Large-scale exploration of bioisosteric replacements on the basis of matched molecular pairs. Future Med Chem 3, 425-436, 2011. dx.doi.org/10.4155/fmc.10.293
311 Iyer P, Hu Y & Bajorath J. SAR monitoring of evolving compound data sets using activity landscapes. J Chem Inf Model 51, 532-540, 2011. dx.doi.org/10.1021/ci100505m
310 Stumpfe D & Bajorath J. Similarity searching. Wiley Interdisciplinary Reviews: Computational Molecular Science 1, 260-282, 2011. dx.doi.org/10.1002/wcms.23
309 Wawer M & Bajorath J. Extracting SAR information from a large collection of anti-malarial screening hits by NSG-SPT analysis. ACS Med Chem Lett 2, 201-206, 2011. dx.doi.org/10.1021/ml100240z
308 Dimova D, Wawer M, Wassermann AM & Bajorath J. Design of multitarget activity landscapes that capture hierarchical activity cliff distributions. J Chem Inf Model 51, 258-266, 2011. dx.doi.org/10.1021/ci100477m
307 Hu Y & Bajorath J. Combining horizontal and vertical substructure relationships in scaffold hierarchies for activity prediction. J Chem Inf Model 51, 248-257, 2011. dx.doi.org/10.1021/ci100448a
306 Iyer P, Wawer M & Bajorath J. Comparison of two- and three-dimensional activity landscape representations for different compound data sets. Med Chem Commun 2, 113-118, 2011. dx.doi.org/10.1039/C0MD00188K
305 Stahl M & Bajorath J. Computational medicinal chemistry. J Med Chem 54, 1-2, 2011. dx.doi.org/10.1021/jm1013055
304 Nisius B & Bajorath J. Mapping of pharmacological space. Expert Opin Drug Discov 6, 1-7, 2011. dx.doi.org/10.1517/17460441.2011.533654
303 Wassermann AM, Heikamp K & Bajorath J. Potency-directed similarity searching using support vector machines. Chem Biol Drug Des 77, 30-38, 2011. dx.doi.org/10.1111/j.1747-0285.2010.01059.x
302 Wassermann AM, Geppert H & Bajorath J. Application of support vector machine-based ranking strategies to search for target-selective compounds. Methods Mol Biol 672, 517-530, 2011. dx.doi.org/10.1007/978-1-60761-839-3_21
301 Stumpfe D, Lounkine E & Bajorath J. Molecular test systems for computational selectivity studies and systematic analysis of compound selectivity profiles. Methods Mol Biol 672, 503-516, 2011. dx.doi.org/10.1007/978-1-60761-839-3_20
300 Vogt M & Bajorath J. Predicting the performance of fingerprint similarity searching. Methods Mol Biol 672, 159-174, 2011. dx.doi.org/10.1007/978-1-60761-839-3_6
299 Peltason L & Bajorath J. Computational analysis of activity and selectivity cliffs. Methods Mol Biol 672, 119-132, 2011. dx.doi.org/10.1007/978-1-60761-839-3_4
298 Stumpfe D & Bajorath J. Applied virtual screening: strategies, recommendations, and caveats. in: Methods and Principles in Medicinal Chemistry. Virtual Screening. Principles, Challenges, and Practical Guidelines. C. Sotriffer (Ed. ), Wiley-VCH, Weinheim, 73-103, 2011.
297 Hu Y & Bajorath J. Polypharmacology-directed compound data mining: identification of promiscuous chemotypes with different activity profiles and comparison to approved drugs. J Chem Inf Model 50, 2112-2118, 2010. dx.doi.org/10.1021/ci1003637
296 Ripphausen P, Nisius B, Peltason L & Bajorath J. Quo vadis, virtual screening? A comprehensive survey of prospective applications. J Med Chem 53, 8461-8467, 2010. dx.doi.org/10.1021/jm101020z
295 Wassermann AM, Wawer M & Bajorath J. Activity landscape representations for structure-activity relationship analysis. J Med Chem 53, 8209-8223, 2010. dx.doi.org/10.1021/jm100933w
294 Hu Y & Bajorath J. Global assessment of scaffold hopping potential for current pharmaceutical targets. Med Chem Commun 1, 339-344, 2010. dx.doi.org/10.1039/C0MD00156B
293 Wassermann AM, Nisius B, Vogt M & Bajorath J. Identification of descriptors capturing compound class-specific features by mutual information analysis. J Chem Inf Model 50, 1935-1940, 2010. dx.doi.org/10.1021/ci100319n
292 Vogt M & Bajorath J. Virtual screening methods based on Bayesian statistics, in: Chemoinformatics and Advanced Machine Learning Perspectives, H. Lodhi, Y. Yamanishi (Eds. ), IGI Global, Hershey, PA, 190-211, 2010. dx.doi.org/10.4018/978-1-61520-911-8.ch010
291 Vogt M, Wassermann AM & Bajorath J. Application of information-theoretic concepts in chemoinformatics. Information 1, 60-73, 2010. dx.doi.org/10.3390/info1020060
290 Hu Y & Bajorath J. Structural and potency relationships between scaffolds of compounds active against human targets. ChemMedChem 5, 1681-1685, 2010. dx.doi.org/10.1002/cmdc.201000272
289 Wawer M & Bajorath J. Similarity-potency trees: a method to search for SAR information in compound data sets and derive SAR rules. J Chem Inf Model 50, 1395-1409, 2010. dx.doi.org/10.1021/ci100197b
288 Stumpfe D, Bill A, Novak N, Loch G, Blockus H, Geppert H, Becker T, Hoch M, Schmitz A, Kolanus W, Famulok M & Bajorath J. Targeting multifunctional proteins by virtual screening: structurally diverse cytohesin inhibitors with differentiated biological functions. ACS Chem Biol 5, 839-849, 2010. dx.doi.org/10.1021/cb100171c
287 Wawer M, Lounkine E, Wassermann AM & Bajorath J. Data structures and computational tools for the extraction of SAR information from large compound sets. Drug Discov Today 15, 630-639, 2010. dx.doi.org/10.1016/j.drudis.2010.06.004
286 Tan L, Batista J & Bajorath J. Computational methodologies for compound database searching that utilize experimental protein-ligand interaction information. Chem Biol Drug Des 76, 191-200, 2010. dx.doi.org/10.1111/j.1747-0285.2010.01007.x
285 Sisay MT, Steinmetzer T, Stirnberg M, Maurer E, Hammami M, Bajorath J & Gütschow M. Identification of the first low molecular weight inhibitors of matriptase-2. J Med Chem 53, 5523-5535, 2010. dx.doi.org/10.1021/jm100183e
284 Vogt M, Stumpfe D, Geppert H & Bajorath J. Scaffold hopping using two-dimensional fingerprints: true potential, black magic, or a hopeless endeavor? Guidelines for virtual screening. J Med Chem 53, 5707-5715, 2010. dx.doi.org/10.1021/jm100492z
283 Wassermann AM & Bajorath J. Chemical substitutions that introduce activity cliffs across different compound classes and biological targets. J Chem Inf Model 50, 1248-1256, 2010. dx.doi.org/10.1021/ci1001845
282 Peltason L, Iyer P & Bajorath J. Rationalizing three-dimensional activity landscapes and the influence of molecular representations on landscape topology and formation of activity cliffs. J Chem Inf Model 50, 1021-1033, 2010. dx.doi.org/10.1021/ci100091e
281 Tan L, Batista J & Bajorath J. Rationalization of the performance and target dependence of similarity searching incorporating protein-ligand interaction information. J Chem Inf Model 50, 1042-1052, 2010. dx.doi.org/10.1021/ci1001197
280 Wassermann AM, Peltason L & Bajorath J. Computational analysis of multi-target structure-activity relationships to derive preference orders for chemical modifications toward target selectivity. ChemMedChem 5, 847-858, 2010. dx.doi.org/10.1002/cmdc.201000064
279 Nisius B & Bajorath J. Rendering conventional molecular fingerprints for virtual screening independent of molecular complexity and size effects. ChemMedChem 5, 859-868, 2010. dx.doi.org/10.1002/cmdc.201000089
278 Wassermann AM, Vogt M & Bajorath J. Iterative Shannon entropy - a methodology to quantify the information content of value range dependent data distributions. Application to descriptor and compound selectivity profiling. Mol Inf 29, 432-440, 2010. dx.doi.org/10.1002/minf.201000029
277 Geppert H & Bajorath J. Advances in 2D fingerprint similarity searching. Expert Opin Drug Discov 5, 529-542, 2010. dx.doi.org/10.1517/17460441.2010.486830
276 Hu Y & Bajorath J. Exploring target-selectivity patterns of molecular scaffolds. ACS Med Chem Lett 1, 54-58, 2010. dx.doi.org/10.1021/ml900024v
275 Hu Y & Bajorath J. Molecular scaffolds with high propensity to form multi-target activity cliffs. J Chem Inf Model 50, 500-510, 2010. dx.doi.org/10.1021/ci100059q
274 Ahmed HEA, Vogt M & Bajorath J. Design and evaluation of bonded atom pair descriptors. J Chem Inf Model 50, 487-499, 2010. dx.doi.org/10.1021/ci900512g
273 Behnke CA, Le Trong I, Godden JW, Merritt EA, Teller DC, Bajorath J & Stenkamp RA. Atomic resolution studies of carbonic anhydrase II. Acta Cryst D66, 616-627, 2010. dx.doi.org/10.1107/S0907444910006554
272 Batista J, Friedrichson T, Schlechtingen G, Braxmeier T, Jennings G & Bajorath J. Computational screening for membrane-directed inhibitors of mast cell activation. Eur J Med Chem 45, 2700-2704, 2010. dx.doi.org/10.1016/j.ejmech.2010.01.061
271 Wawer M, Sun S & Bajorath J. Computational characterization of SAR microenvironments in high-throughput screening data. Intl J High Throughput Screen 1, 15-27, 2010. dx.doi.org/10.2147/IJHTS.S7534
270 Stumpfe D, Geppert H & Bajorath J. In silico screening, in: Lead Generation Approaches in Drug Discovery, R. Morphy, Z. Rankovic (Eds. ), John Wiley & Sons, Hoboken, NJ, 73-103, 2010. dx.doi.org/10.1002/9780470584170.ch3
269 Baqi Y, Lee S-Y, Iqbal J, Ripphausen P, Lehr A, Zimmermann H, Bajorath J & Müller CE. Development of potent and selective inhibitors of ecto-5’-nucleotidase based on an anthraquinone scaffold. J Med Chem 53, 2076-2086, 2010. dx.doi.org/10.1021/jm901851t
268 Wang Y & Bajorath J. Advanced fingerprint methods for similarity searching: balancing molecular complexity effects. Combin Chem High Throughput Screen 13, 220-228, 2010. dx.doi.org/10.2174/138620710790980487
267 Geppert H, Vogt M & Bajorath J. Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J Chem Inf Model 50, 205-216, 2010. dx.doi.org/10.1021/ci900419k
266 Hu Y & Bajorath J. Scaffold distributions in bioactive molecules, clinical trials compounds, and drugs. ChemMedChem 5, 187-190, 2010. dx.doi.org/10.1002/cmdc.200900419
265 Lounkine E, Wawer M, Wassermann AM & Bajorath J. SARANEA – a freely available program to mine structure-activity and structure-selectivity relationship information in compound data sets. J Chem Inf Model 50, 68-78, 2010. dx.doi.org/10.1021/ci900416a
264 Batista J, Tan L & Bajorath J. Atom-centered interacting fragments and similarity search applications. J Chem Inf Model 50, 79-86, 2010. dx.doi.org/10.1021/ci9004223
263 Hu Y, Wassermann AM, Lounkine E & Bajorath J. Systematic analysis of public domain compound potency data identifies selective molecular scaffolds across druggable target families. J Med Chem 53, 752-758, 2010. dx.doi.org/10.1021/jm9014229
262 Nisius B & Bajorath J. Reduction and recombination of fingerprints of different design increase compound recall and the structural diversity of hits. Chem Biol Drug Des 75, 152-160, 2010. dx.doi.org/10.1111/j.1747-0285.2009.00930.x
261 Bajorath J. Computational studies, virtual screening, and theoretical molecular models. J Med Chem 53, 1-2, 2010. dx.doi.org/10.1021/jm901774n
260 Stumpfe D, Sisay MT, Frizler M,Vogt I, Gütschow M & Bajorath J. Inhibitors of cathepsin K and S identified using the DynaMAD virtual screening algorithm. ChemMedChem 5, 61-64, 2010. dx.doi.org/10.1002/cmdc.200900457
259 Vogt M & Bajorath J. Data mining approaches for compound selection and iterative screening, in: Pharmaceutical Data Mining: Approaches and Applications for Drug Discovery, K. V. Balakin (Ed. ), John Wiley & Sons, Hoboken, NJ, 115-143, 2009.
258 Nisius B & Bajorath J. Fingerprint recombination – generating hybrid fingerprints for similarity searching from different fingerprint types. ChemMedChem 4, 1859-1863, 2009. dx.doi.org/10.1002/cmdc.200900243
257 Peltason L, Hu Y & Bajorath J. From structure-activity to structure-selectivity relationships: quantitative assessment, selectivity cliffs, and key compounds. ChemMedChem 4, 1864-1873, 2009. dx.doi.org/10.1002/cmdc.200900300
256 Wassermann A, Geppert H & Bajorath J. Ligand prediction for orphan targets using support vector machines and various target-ligand kernels is dominated by nearest neighbor effects. J Chem Inf Model 49, 2155-2167, 2009. dx.doi.org/10.1021/ci9002624
255 Sisay MT, Peltason L & Bajorath J. Structural interpretation of activity cliffs revealed by systematic analysis of structure-activity relationships in analog series. J Chem Inf Model 49, 2179-2189, 2009. dx.doi.org/10.1021/ci900243a
254 Tan L, Vogt M & Bajorath J. Three-dimensional protein-ligand interaction scaling of two-dimensional fingerprints. Chem Biol Drug Des 74, 449-456, 2009. dx.doi.org/10.1111/j.1747-0285.2009.00890.x
253 Wawer M & Bajorath J. Extraction of structure-activity relationship information from high-throughput screening data. Curr Med Chem 16, 4049-4057, 2009. dx.doi.org/10.2174/092986709789378189
252 Wawer M & Bajorath J. Systematic extraction of structure-activity relationship information from biological screening data. ChemMedChem 4, 1431-1438, 2009. dx.doi.org/10.1002/cmdc.200900222
251 Sisay MT, Hautmann S, Mehner C, König GM, Bajorath J & Gütschow M. Brunsvicamides A-C: selective inhibitors of human leukocyte elastase. ChemMedChem 4, 1425-1429, 2009.
250 Wang Y, Geppert H & Bajorath J. A Shannon entropy-based fingerprint similarity search strategy. J Chem Inf Model, 49, 1687-1691, 2009. dx.doi.org/10.1021/ci900159f
249 Ahmed HEA & Bajorath J. Methods for computer-aided chemical biology. Part 5: rationalizing the selectivity of cathepsin inhibitors on the basis of molecular fragments and topological feature distributions. Chem Biol Drug Des 74, 129-141, 2009. dx.doi.org/10.1111/j.1747-0285.2009.00848.x
248 Vogt M, Nisius B & Bajorath J. Predicting the similarity search performance of fingerprints and their combination with molecular property descriptors using probabilistic and information-theoretic modeling. Stat Anal Data Mining 2, 123-134, 2009. dx.doi.org/10.1002/sam.10035
247 Krüger F, Lounkine E & Bajorath J. Fragment formal concept analysis accurately classifies compounds with closely related biological activities. ChemMedChem 4, 1174-1181, 2009. dx.doi.org/10.1002/cmdc.200900035
246 Bajorath J, Peltason L, Wawer M, Guha R, Lajiness MS & van Drie J. Navigating structure-activity landscapes. Drug Discov Today 14, 698-705, 2009. dx.doi.org/10.1016/j.drudis.2009.04.003
245 Peltason L & Bajorath J. Systematic computational analysis of structure-activity relationships: concepts, challenges, and recent advances. Future Med Chem 1, 451-466, 2009. dx.doi.org/10.4155/fmc.09.41
244 Wang Y & Bajorath J. Development of a compound class-directed similarity coefficient that accounts for molecular complexity effects in fingerprint searching. J Chem Inf Model 49, 1369-1376, 2009. dx.doi.org/10.1021/ci900108d
243 Lounkine E, Stumpfe D & Bajorath J. Molecular formal concept analysis for compound selectivity profiling in biologically annotated databases. J Chem Inf Model 49, 1359-1368, 2009. dx.doi.org/10.1021/ci900095v
242 Nisius B, Vogt M & Bajorath J. Development of a fingerprint reduction approach for Bayesian similarity searching based on Kullback-Leibler divergence analysis. J Chem Inf Model 49, 1347-1358, 2009. dx.doi.org/10.1021/ci900087y
241 Hu Y, Lounkine E & Bajorath J. Filtering and counting of extended connectivity fingerprint features maximizes compound recall and the structural diversity of hits. Chem Biol Drug Des 74, 92-98, 2009. dx.doi.org/10.1111/j.1747-0285.2009.00830.x
240 Tan L & Bajorath J. Utilizing target-ligand interaction information in fingerprint searching for ligands of related targets. Chem Biol Drug Des 74, 25-32, 2009. dx.doi.org/10.1111/j.1747-0285.2009.00829.x
239 Peltason L, Weskamp N, Teckentrup A & Bajorath J. Exploration of structure-activity relationship determinants in analogue series. J Med Chem 52, 3212-3224, 2009. dx.doi.org/10.1021/jm900107b
238 Geppert H, Humrich J, Stumpfe D, Gärtner T & Bajorath J. Ligand prediction from protein sequence and small molecule information using support vector machines and fingerprint descriptors. J Chem Inf Model 49, 767-779, 2009. dx.doi.org/10.1021/ci900004a
237 Hu Y, Lounkine E & Bajorath J. Improving the performance of extended connectivity fingerprints through activity-oriented feature filtering and application of a bit density-dependent similarity function. ChemMedChem 4, 540-548, 2009. dx.doi.org/10.1002/cmdc.200800408
236 Wassermann A, Geppert H & Bajorath J. Searching for target-selective compounds using different combinations of multiclass support vector machine ranking methods, kernel functions, and fingerprint descriptors. J Chem Inf Model 49, 582-592, 2009. dx.doi.org/10.1021/ci800441c
235 Lounkine E, Hu Y, Batista J & Bajorath J. Relevance of feature combinations for similarity searching using general or activity class-directed molecular fingerprints. J Chem Inf Model 49, 561-570, 2009. dx.doi.org/10.1021/ci800377n
234 Wawer M, Peltason L & Bajorath J. Elucidation of structure-activity relationship pathways in biological screening data. J Med Chem 52, 1075-1080, 2009. dx.doi.org/10.1021/jm8014102
233 Lounkine E & Bajorath J. Topological fragment index for the analysis of molecular substructures and their topological environment in active compounds. J Chem Inf Model 49, 162-168, 2009. dx.doi.org/10.1021/ci8002599
232 Bajorath J. Ligand-based methods for virtual screening. Frontiers Med Chem 4, 1-22, 2009.
231 Ahmed HEA, Geppert H, Stumpfe D, Lounkine E & Bajorath J. Methods for computer-aided chemical biology. Part 4: selectivity searching for ion channel ligands and mapping of molecular fragments as selectivity markers. Chem Biol Drug Des 73, 273-282, 2009. dx.doi.org/10.1111/j.1747-0285.2009.00784.x
230 Stumpfe D, Frizler M, Sisay MT, Batista J, Vogt I, Gütschow M & Bajorath J. Hit expansion through selectivity searching. ChemMedChem 4, 52-54, 2009. dx.doi.org/10.1002/cmdc.200800304
229 Bajorath J. Pharmacophore. Encyclopedia of Cancer, 2nd Edition, M Schwab (Ed. ), Springer-Verlag, Heidelberg, 2310-2312, 2009.
228 Bajorath J. Quantitative structure activity relationship. Encyclopedia of Cancer, 2nd Edition, M Schwab (Ed. ), Springer-Verlag, Heidelberg, 2506-2509, 2009.
227 Nisius B, Göller AH & Bajorath J. Combining cluster analysis, feature selection, and multiple support vector machine models for the identification of hERG channel blocking compounds. Chem Biol Drug Des 73, 17-25, 2009.
226 Tan L, Lounkine E & Bajorath J. Similarity searching using fingerprints of molecular fragments involved in protein-ligand interactions. J Chem Inf Model 48, 2308-2312, 2008. dx.doi.org/10.1021/ci800322y
225 Bajorath J. Computational approaches in chemogenomics and chemical biology: current and future impact on drug discovery. Expert Opin Drug Discov 3, 1371-1376, 2008. dx.doi.org/10.1517/17460440802536496
224 Crisman TJ, Sisay MT & Bajorath J. Ligand-target interaction-based weighting of substructures for virtual screening. J Chem Inf Model 48, 1955-1964, 2008. dx.doi.org/10.1021/ci800229q
223 Hu Y, Lounkine E, Batista J & Bajorath J. RelACCS-FP: a structural minimalist approach to fingerprint design. Chem Biol Drug Des 72, 341-349, 2008. dx.doi.org/10.1111/j.1747-0285.2008.00723.x
222 Tan L, Geppert H, Sisay MT, Gütschow M & Bajorath J. Integrating structure-and ligand-based virtual screening: comparison of individual, parallel, and fused molecular docking and similarity search calculations on multiple targets. ChemMedChem 3, 1566-1571, 2008. dx.doi.org/10.1002/cmdc.200800129
221 Wawer M, Peltason L, Weskamp N, Teckentrup A & Bajorath J. Structure-activity relationship anatomy by network-like similarity graphs and local structure-activity relationship indices. J Med Chem 51, 6075-6084, 2008. dx.doi.org/10.1021/jm800867g
220 Auer J & Bajorath J. Distinguishing between bioactive and modeled compound conformations through mining of emerging chemical patterns. J Chem Inf Model 48, 1747-1753, 2008. dx.doi.org/10.1021/ci8001793
219 Wang Y & Bajorath J. Bit silencing in keyed fingerprints enables the derivation of compound class-directed similarity metrics. J Chem Inf Model 48, 1754-1749, 2008. dx.doi.org/10.1021/ci8002045
218 Lounkine E, Auer J & Bajorath J. Formal concept analysis for the identification of molecular fragment combinations specific for active and highly potent compounds. J Med Chem 51, 5342-5348, 2008. dx.doi.org/10.1021/jm800515r
217 Vogt I & Bajorath J. Design and exploration of target-selective chemical space representations. J Chem Inf Model 48, 1389-1395, 2008. dx.doi.org/10.1021/ci800106e
216 Lounkine E, Batista J & Bajorath J. Random molecular fragment methods in computational medicinal chemistry. Curr Med Chem 15, 2108-2121,2008. dx.doi.org/10.2174/092986708785747607
215 Lounkine E & Bajorath J. Core trees and consensus fragment sequences for molecular representation and similarity analysis. J Chem Inf Model 48, 1161-1166, 2008. dx.doi.org/10.1021/ci800020s
214 Bajorath J. Computational analysis of ligand relationships within target families. Curr Opin Chem Biol, Curr Opin Chem Biol 12, 352-358, 2008. dx.doi.org/10.1016/j.cbpa.2008.01.044
213 Vogt I, Ahmed H, Auer J & Bajorath J. Exploring structure-selectivity relationships of biogenic amine GPCR antagonists using similarity searching and dynamic compound mapping. Mol Divers 12, 25-40, 2008. dx.doi.org/10.1007/s11030-008-9071-2
212 Batista J & Bajorath J. Distribution of randomly generated activity class characteristic substructures in diverse active and database compounds. Mol Divers 12, 77-83, 2008. dx.doi.org/10.1007/s11030-008-9078-8
211 Eckert H & Bajorath J. Optimization of the MAD algorithm for virtual screening. Methods Mol Biol 453, 349-362,2008. dx.doi.org/10.1007/978-1-60327-429-6_18
210 Auer J & Bajorath J. Molecular similarity concepts and search calculations. Methods Mol Biol 453, 327-347, 2008. dx.doi.org/10.1007/978-1-60327-429-6_17
209 Peltason, L & Bajorath J. Molecular similarity analysis in virtual screening, in: Chemoinformatics: An Approach to Virtual Screening, A Varnek & A Tropsha (Eds. ), RSC Publishing, Cambridge, 120-149, 2008.
208 Wang Y, Geppert H & Bajorath J. Random reduction in fingerprint bit density improves compound recall in search calculations using complex reference molecules. Chem Biol Drug Des 71, 511-517, 2008. dx.doi.org/10.1111/j.1747-0285.2008.00664.x
207 Bajorath J. Computational chemistry publications in the journal of medicinal chemistry. J Med Chem 51, 2327-2327, 2008. dx.doi.org/10.1021/jm901774n
206 Stumpfe D, Geppert H & Bajorath J. Methods for computer-aided chemical biology, part 3: analysis of structure-selectivity relationships through single- or dual-step selectivity searching and Bayesian classification. Chem Biol Drug Des 71, 518-528, 2008. dx.doi.org/10.1111/j.1747-0285.2008.00670.x
205 Geppert H, Horvath T, Gärtner T, Wrobel S & Bajorath J. Support vector machine-based ranking significantly improves the effectiveness of similarity searching using 2D fingerprints and multiple reference compounds. J Chem Inf Model 48, 742-746, 2008. dx.doi.org/10.1021/ci700461s
204 Wang Y & Bajorath J. Balancing complexity effects in fingerprint similarity searching. J Chem Inf Model 48, 75-84, 2008. dx.doi.org/10.1021/ci700314x
203 Vogt M & Bajorath J. Bayesian similarity searching in high-dimensional descriptor spaces combined with Kullback-Leibler descriptor divergence analysis. J Chem Inf Model 48, 247-255, 2008. dx.doi.org/10.1021/ci700333t
202 Vogt M & Bajorath J. Bayesian screening for active compounds in high-dimensional chemical spaces combining property descriptors and fingerprints. Chem Biol Drug Des 71, 8-14, 2008. dx.doi.org/10.1111/j.1747-0285.2007.00602.x
201 Batista J & Bajorath J. Similarity searching using compound class-specific combinations of substructures found in randomly generated molecular fragment populations. ChemMedChem 3, 67-73, 2008. dx.doi.org/10.1002/cmdc.200700199
200 Auer J & Bajorath J. Simulation of sequential screening experiments using emerging chemical patterns. Med Chem 4, 80-90, 2008. dx.doi.org/10.2174/157340608783331452
199 Lounkine E, Batista J & Bajorath J. Mapping of activity-specific fragment pathways isolated from random fragment populations reveals the formation of coherent molecular cores. J Chem Inf Model 47, 2133-2139, 2007. dx.doi.org/10.1021/ci700251b
198 Peltason L & Bajorath J. SAR index: quantifying the nature of structure-activity relationships. J Med Chem 50, 5571-5578, 2007. dx.doi.org/10.1021/jm0705713
197 Vogt I, Stumpfe D, Ahmed H & Bajorath J. Methods for computer-aided chemical biology, part 2: evaluation of compound selectivity using 2D fingerprints. Chem Biol Drug Des 70, 195-205, 2007. dx.doi.org/10.1111/j.1747-0285.2007.00555.x
196 Stumpfe D, Ahmed H, Vogt I & Bajorath J. Methods for computer-aided chemical biology, part 1: design of a benchmark system for the evaluation of compound selectivity. Chem Biol Drug Des 70, 182-194, 2007. dx.doi.org/10.1111/j.1747-0285.2007.00554.x
195 Vogt M & Bajorath J. Introduction of a generally applicable method to estimate retrieval of active molecules for similarity searching using fingerprints. ChemMedChem 2, 1311-1320, 2007. dx.doi.org/10.1002/cmdc.200700090
194 Batista J & Bajorath J. Mining of randomly generated molecular fragment populations uncovers activity-specific fragment hierarchies. J Chem Inf Model 47, 1405-1413, 2007. dx.doi.org/10.1021/ci700108q
193 Yamazaki S, Tan L, Hartig JS, Mayer G, Song J-N, Reuter S, Restle T, Laufer SD, Grohmann D, Kräusslich H-G, Bajorath J & Famulok M. Aptamer displacement identifies alternative small-molecule target sites that escape viral resistance. Chem Biol 14, 489-497, 2007. dx.doi.org/10.1016/j.chembiol.2007.06.003
192 Eckert H & Bajorath J. Exploring peptide-likeness of active molecules using 2D fingerprint methods. J Chem Inf Model 47, 1366-1378, 2007. dx.doi.org/10.1021/ci700086m
191 Wang Y, Eckert H & Bajorath J. Apparent asymmetry in fingerprint searching is a direct consequence of differences in bit densities and molecular size. ChemMedChem 2, 1037-1042, 2007. dx.doi.org/10.1002/cmdc.200700050
190 Wang Y, Godden JW, Bajorath J. A novel histogram filtering method for database mining and the identification of active molecules. Lett Drug Design Discov 4, 286-292, 2007. dx.doi.org/10.2174/157018007784619970
189 Peltason L & Bajorath J. Molecular similarity analysis uncovers heterogeneous structure-activity relationships and variable activity landscapes. Chem Biol 14, 489-497, 2007. dx.doi.org/10.1016/j.chembiol.2007.03.011
188 Vogt I & Bajorath J. Analysis of a high-throughput screening data set using potency-scaled molecular similarity algorithms. J Chem Inf Model 47, 367-375, 2007. dx.doi.org/10.1021/ci6005432
187 Vogt M & Bajorath J. Introduction of an information-theoretic method to predict recovery rates of active compounds for Bayesian in silico screening: theory and screening trials. J Chem Inf Model 47, 337-341, 2007. dx.doi.org/10.1021/ci600418u
186 Shoda M, Harada T, Stahura FL, Himeno T, Shiojiri, S, Kogami Y, Kouji H & Bajorath J. Virtual screening leads to the discovery of an effective antagonist of lymphocyte function-associated antigen-1. ChemMedChem 2, 515-521, 2007. dx.doi.org/10.1002/cmdc.200600288
185 Godden JW & Bajorath J. Analysis of chemical information content using Shannon entropy. Rev Comput Chem 23, 263-289, 2007. dx.doi.org/10.1002/9780470116449.ch5
184 Eckert H & Bajorath J. Molecular similarity analysis in virtual screening: foundations, limitations, and novel approaches. Drug Discov Today 12, 225-233, 2007. dx.doi.org/10.1016/j.drudis.2007.01.011
183 Tovar A, Eckert H & Bajorath J. Comparison of 2D fingerprint methods for multiple-template similarity searching on compound classes of increasing structural diversity. ChemMedChem 2, 208-217, 2007. dx.doi.org/10.1002/cmdc.200600225
182 Vogt M, Godden JW & Bajorath J. Bayesian interpretation of a distance function for navigating high-dimensional descriptor spaces. J Chem Inf Model 47, 39-46, 2007. dx.doi.org/10.1021/ci600280b
181 Batista J & Bajorath J. Chemical database mining through entropy-based molecular similarity assessment of randomly generated structural fragment populations. J Chem Inf Model 47, 59-68, 2007. dx.doi.org/10.1021/ci600377m
180 Parker CN & Bajorath J. Towards unified compound screening strategies: a critical evaluation of error sources in experimental and virtual high-throughput screening. QSAR Comb Sci 25, 1153-1161, 2006. dx.doi.org/10.1002/qsar.200610069
179 Parker CN, Shamu CE, Kraybill B, Austin CP, Bajorath J. Measure, mine, model, manipulate – the future for HTS and chemoinformatics. Drug Discov Today 11, 863-865, 2006. dx.doi.org/10.1016/j.drudis.2006.08.006
178 Eckert H & Bajorath J. Design and evaluation of a novel class-directed 2D fingerprint to search for structurally diverse active compounds. J Chem Inf Model 46, 2515-2526, 2006. dx.doi.org/10.1021/ci600303b
177 Auer J & Bajorath J. Emerging Chemical Patterns: A New Methodology for Molecular Classification and Compound Selection. J Chem Inf Model 46, 2502-2514, 2006. dx.doi.org/10.1021/ci600301t
176 Batista J, Godden JW & Bajorath J. Assessment of molecular similarity from the analysis of randomly generated structural fragment populations. J Chem Inf Model, 46, 1937-1944, 2006. dx.doi.org/10.1021/ci0601261
175 Eckert H, Vogt I & Bajorath J. Mapping algorithms for molecular similarity analysis and ligand-based virtual screening: design of DynaMAD and comparison with MAD and DMC. J Chem Inf Model 46, 1623-1634, 2006. dx.doi.org/10.1021/ci060083o
174 Stahura FL & Bajorath J. Computational analysis of natural molecules and strategies for the design of natural product-based libraries, in: Combinatorial synthesis of natural product-based libraries, A Boldi (Ed.). Critical Reviews in Combinatorial Chemistry No. 2, Taylor & Francis, New York, USA, pp 53-64, 2006. dx.doi.org/10.1201/9781420009279.ch3
173 Godden JW & Bajorath J. A distance function for retrieval of active molecules from complex chemical space representations. J Chem Inf Model 46, 1094-1097, 2006. dx.doi.org/10.1021/ci050510i
172 Eckert H & Bajorath J. Determination and mapping of activity-specific descriptor value ranges for the identification of active compounds. J Med Chem 49, 2284-2293, 2006. dx.doi.org/10.1021/jm051110p
171 Lang TP, Kuntz ID, Maggiora GM & Bajorath J. Evaluating high-throughput screening computations. J Biomol Screen 10, 649-652, 2005. dx.doi.org/10.1177/1087057105281269
170 Godden JW, Stahura FL & Bajorath J. Anatomy of fingerprint search calculations on structurally diverse sets of active compounds. J Chem Inf Model 45, 1812-1819, 2005. dx.doi.org/10.1021/ci050276w
169 Haffar O, Dubrovsky L, Lowe R, Berro R, Kashanchi F, Godden J, Vanpouille C, Bajorath J & Bukrinsky M. Oxadiazols: a new class of rationally designed anti-human immunodeficiency virus compounds targeting the nuclear localization signal of the viral matrix protein. J Virol 79, 13028-13036, 2005. dx.doi.org/10.1128/JVI.79.20.13028-13036.2005
168 Bajorath J. Potency-scaled partitioning in descriptor spaces with increasing dimensionality. Curr Top Med Chem 5, 797-803, 2005.
167 Bajorath J. Theoretical approaches in medicinal chemistry and drug discovery. Curr Top Med Chem 5, 737-738, 2005. dx.doi.org/10.1038/nrd941
166 Bajorath J. Molecular similarity methods and QSAR models as virtual screening tools, in; Drug Discovery Handbook, Vol. 1, SC Gad (Ed.), John Wiley & Sons, Inc. , Hoboken, New Jersey, USA, pp 87-122, 2005.
165 Stahura FL & Bajorath J. New methodologies for virtual screening. Curr Pharm Design 11, 1189-1202, 2005. dx.doi.org/10.2174/1381612053507549
164 Larsen CP, Pearson TC, Adams AB, Tso P, Shirasugi N, Strobertm E, Anderson D, Cowan S, Price K, Naemura J, Emswiler J, Greene J, Turk LA, Bajorath J, Townsend R, Hagerty D, Linsley PS & Peach RJ. Rational development of LEA29Y (belatacept), a high-affinity variant of CTLA4-Ig with potent immunosuppressive properties. Am J Transplant 5, 443-453, 2005. dx.doi.org/10.1111/j.1600-6143.2005.00749.x
163 Xue L, Stahura FL & Bajorath J. Chemoinformatics: Perspectives and challenges, in: Chemometrics and Chemoinformatics, BK Lavine (Ed.), ACS Symposiums Series No. 894, Oxford University Press, New York, USA, 2005. dx.doi.org/10.1021/bk-2005-0894.ch004
162 Godden JW, Stahura FL & Bajorath J. POT-DMC - a virtual screening method for the identification of potent hits. J Med Chem 47, 5068-5611, 2004. dx.doi.org/10.1021/jm049505g
161 Xue L, Stahura FL, Bajorath J. Similarity search profiling reveals effects of fingerprint scaling in virtual screening. J Chem Inf Comput Sci 44, 2032-2039, 2004. dx.doi.org/10.1021/ci0400819
160 Kitchen DB, Decornez H, Furr JR, Bajorath J. Structure-based virtual screening and lead optimization: methods and applications. Nature Rev Drug Discov 3, 935-949, 2004. dx.doi.org/10.1038/nrd1549
159 Shoda M, Harada T, Kogami Y, Tsujita R, Akashi H, Kouji H, Stahura FL, Xue L & Bajorath J. Identification of structurally diverse growth hormone secretagogue agonists by virtual screening and structure-activity relationship analysis of 2-formylaminoacetamide derivatives. J Med Chem 47, 4286-4290, 2004. dx.doi.org/10.1021/jm040103i
158 Xue L, Godden JW, Stahura FL & Bajorath J. Similarity search profiles as a diagnostic tool for the analysis of virtual screening calculations. J Chem Inf Comput Sci 44, 1275-1281, 2004. dx.doi.org/10.1021/ci040120g
157 Stahura FL & Bajorath J. Virtual screening methods that complement HTS. Combin Chem High Throughput Screen 7, 259-269, 2004. dx.doi.org/10.2174/1386207043328706
156 Bajorath J. Chemoinformatics and its role in pharmaceutical research. Pharmatech 2004, 76-82, 2004.
155 Kitchen DB, Stahura FL & Bajorath J. Computational techniques in diversity analysis and compound classification. Mini Rev Med Chem 4, 1029-1039, 2004. dx.doi.org/10.2174/1389557043402982
154 Godden JW & Bajorath J. Partitioning in binary-transformed descriptor spaces. Methods Mol Biol 275, 291-300, 2004. dx.doi.org/10.1385/1-59259-802-1:291
153 Xue L, Stahura FL & Bajorath J. Cell-based partitioning algorithms. Methods Mol Biol 275, 279-289, 2004. dx.doi.org/10.1385/1-59259-802-1:279
152 Bajorath J. Diverse fingerprints. Modern Drug Discov 7, 2, 11-11, 2004.
151 Godden JW, Furr JR, Xue L, FL Stahura & Bajorath J. Molecular similarity analysis and virtual screening in binary-transformed chemical descriptor spaces with variable dimensionality. J Chem Inf Comput Sci 44, 21-29, 2004. dx.doi.org/10.1021/ci0302963
150 Bajorath J. Understanding chemoinformatics: a unifying approach. Drug Discov Today 9, 13-14, 2004. dx.doi.org/10.1016/S1359-6446(04)02916-2
149 Sica GL, Choi I-H, Zhu G, Tamada K, Wang, S-D, Tamura H, Chapoval AI, Flies DB, Bajorath J & Chen L. B7-H4, a new molecule of the B7 family, negatively regulates T cell immunity. Immunity 18, 849-861, 2003. dx.doi.org/10.1016/S1074-7613(03)00152-3
148 Xue L, Godden JW, Stahura FL & Bajorath J. Profile scaling increases the similarity search performance of molecular fingerprints containing numerical descriptors and structural keys. J Chem Inf Comput Sci 43, 1218-1225, 2003. dx.doi.org/10.1021/ci030287u
147 Xue L, Godden JW, Stahura FL & Bajorath J. Design and evaluation of a molecular fingerprint involving the transformation of property descriptor values into a binary classification scheme. J Chem Inf Comput Sci 43, 1151-1157, 2003. dx.doi.org/10.1021/ci030285+
146 Godden JW & Bajorath J. An information-theoretic approach to descriptor selection for database profiling and QSAR modeling. QSAR Comb Sci 22, 487-497, 2003. dx.doi.org/10.1002/qsar.200310001
145 Bajorath J. Structure prediction and binding site identification of members of the CD28 and B7 families of costimulatory immune cell surface proteins, in: The B7 Family and the Immune Response, L Chen (Ed. ), Landes Bioscience, Georgetown, Texas, USA, 2003.
144 Stahura FL & Bajorath J. Partitioning methods for the identification of active molecules. Curr Med Chem 8, 707-715, 2003. dx.doi.org/10.2174/0929867033457881
143 Wang S, Bajorath J, Flies DB, Dong H, Honjo T & Chen L. Molecular modeling and functional mapping of PD-1 ligands uncouple costimulatory function from PD-1 interaction. J Exp Med 197, 1083-1091, 2003. dx.doi.org/10.1084/jem.20021752
142 Xue L, Godden JW & Bajorath J. Mini-fingerprints for virtual screening: Design principles and generation of novel prototypes based on information theory. SAR QSAR Environ Res 14, 27-40, 2003. dx.doi.org/10.1080/1062936021000058764
141 Godden J, Furr JR & Bajorath J. Recursive median partitioning for virtual screening of large databases. J Chem Inf Comput Sci 43, 182-188, 2003. dx.doi.org/10.1021/ci0203848
140 Bajorath J. Chemoinformatics methods for systematic comparison of molecules from natural and synthetic sources and design of hybrid libraries. Mol Divers 5, 305-313, 2002. dx.doi.org/10.1023/A:1021321621406
139 Bajorath J. Affinity fingerprints – leading the way. Drug Discov Today 7, 1035-1035, 2002.
138 Bajorath, J. Integration of virtual and high-throughput screening. Nature Rev Drug Discov 1, 882-894, 2002. dx.doi.org/10.1038/nrd941
137 Godden JW, Xue L, Bajorath J. Classification of biologically active compounds by median partitioning. J Chem Inf Comput Sci 42, 1263-1269, 2002. dx.doi.org/10.1021/ci020372m
136 Bajorath J. Predicting novel proteins and their interactions. Drug Discov Today, 7, 945-946, 2002. dx.doi.org/10.1016/S1359-6446(02)02446-7
135 Bajorath J. Identification and validation of therapeutic target proteins. Targets 1, 45-46, 2002. dx.doi.org/10.1016/S1477-3627(02)02194-3
134 Godden JW, Xue L, Kitchen DB, Stahura FL, Schermerhorn EJ & Bajorath J. Median partitioning: A novel method for the selection of representative subsets from large compound pools. J Chem Inf Comput Sci 42, 885-893, 2002. dx.doi.org/10.1021/ci0203693
133 Stahura FL & Bajorath J. Bio- and chemoinformatics beyond data management: Crucial challenges and future opportunities. Drug Discov Today (Information Biotechnol Suppl), S41-S47, 2002. dx.doi.org/10.1016/S1359-6446(02)02271-7
132 Stahura FL, Xue L, Godden JW & Bajorath J. Methods for compound selection focused on hits and application in drug discovery. J Mol Graph Model 20, 439-446, 2002. dx.doi.org/10.1016/S1093-3263(01)00145-0
131 Xue L & Bajorath J. Accurate partitioning of compounds in diverse activity classes. J Chem Inf Comput Sci 42, 757-764, 2002. dx.doi.org/10.1021/ci010248n
130 Stahura FL, Godden JW & Bajorath J. Differential Shannon entropy analysis identifies molecular descriptors that predict aqueous solubility of synthetic compounds with high accuracy in binary QSAR calculations. J Chem Inf Comput Sci 42, 550-558, 2002. dx.doi.org/10.1021/ci010243q
129 Godden JW & Bajorath J. Computational molecular dynamics: Challenges, methods, ideas (book review). Theor Chem Acc 107, 250-251, 2002. dx.doi.org/10.1007/s00214-002-0329-y
128 Wang S, Zhu G, Tamada K, Chen L & Bajorath J. Ligand binding sites of inducible costimulator and high avidity mutants with improved function. J Exp Med 195, 1033-1041, 2002. dx.doi.org/10.1084/jem.20011607
127 Bajorath J. Virtual screening: methods, expectations, and reality. Curr Drug Discov 2 (3), 24-28, 2002.
126 Bajorath J. Chemoinformatics methods for systematic comparison of molecules from natural and synthetic sources and design of hybrid libraries. Mol Divers 5, 305-313, 2002. dx.doi.org/10.1023/A:1021321621406
125 Godden JW & Bajorath J. Chemical descriptors with distinct levels of information content and varying sensitivity to differences between selected compound databases identified by SE-DSE analysis, J Chem Inf Comput Sci. 42, 87-93, 2002. dx.doi.org/10.1021/ci0103065
124 Bajorath J. Selected concepts and investigations in compound classification, molecular descriptor analysis, and virtual screening. J Chem Inf Comput Sci 41, 233-245, 2001. dx.doi.org/10.1021/ci0001482
123 Bajorath J. Rational drug discovery revisited: Interfacing experimental programs with bio- and chemo-informatics. Drug Discov Today 6, 989-995, 2001. dx.doi.org/10.1016/S1359-6446(01)01961-4
122 Bajorath J. Structural biology of T cell costimulatory molecules: New insights, more surprises. J Mol Graph Model 19, 619-623, 2001.
121 Godden JW & Bajorath J. Differential Shannon entropy as a sensitive measure of differences in the variability of molecular descriptors. J Chem Inf Comput Sci 41, 1060-1066, 2001. dx.doi.org/10.1021/ci0102867
120 Xue L, Stahura FL, Godden JW & Bajorath J. Fingerprint scaling increases the probability of identifying molecules with similar activity in systematic virtual screening calculations. J Chem Inf Comput Sci 41, 746-753, 2001. dx.doi.org/10.1021/ci000311t
119 Xue L, Godden JW, Stahura FL & Bajorath J. A dual fingerprint-based approach for the design of focused combinatorial libraries and analogs. J Mol Model 7, 125-131, 2001. dx.doi.org/10.1007/s008940100019
118 Stahura FL, Godden JW, Xue L & Bajorath J. Distinguishing between natural products and synthetic molecules by Shannon descriptor entropy analysis and binary QSAR calculations. J Chem Inf Comput Sci 40, 1245-1252, 2000. dx.doi.org/10.1021/ci0003303
117 Bajorath J. Understanding the structural basis of T cell costimulation. J Mol Graph Model 18, 176-179, 2000.
116 Xue L, Godden JW & Bajorath J. Evaluation of descriptors and mini-fingerprints for the identification of molecules with similar activity. J Chem Inf Comput Sci 40, 1227-1234, 2000. dx.doi.org/10.1021/ci000327j
115 Stahura FL, Xue L, Godden JW & Bajorath J. Design of array-type libraries that combine information from natural products and synthetic molecules. J Mol Model 6, 550-562, 2000. dx.doi.org/10.1007/s0089400060550
114 Xue L & Bajorath J. Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combin Chem High Throuput Screen 3, 363-372, 2000.
113 Bowen MA, Aruffo A & Bajorath J. Cell surface receptors and their ligands: In vitro analysis of CD6-CD166 interactions. Proteins: Struct, Funct & Genet 40, 420-428, 2000. dx.doi.org/10.1002/1097-0134(20000815)40:3<420::AID-PROT70>3.0.CO;2-U
112 Godden JW & Bajorath J. Shannon entropy – A novel concept in molecular descriptor and diversity analysis. J Mol Graph Model 18, 73-76, 2000.
111 Xue L & Bajorath J. Molecular descriptors for effective classification of biologically active compounds based on principal component analysis identified by a genetic algorithm. J Chem Inf Comput Sci 40, 801-809, 2000. dx.doi.org/10.1021/ci000322m
110 Godden JW, Stahura, FL & Bajorath J. Variability of molecular descriptors in compound databases revealed by Shannon entropy calculations. J Chem Inf Comput Sci 40, 796-800, 2000. dx.doi.org/10.1021/ci000321u
109 Bajorath J. Molecular organization, structural features, and ligand binding characteristics of CD44, a highly variable cell surface glycoprotein with multiple functions. Proteins: Struct, Funct & Genet 39, 103-111, 2000. dx.doi.org/10.1002/(SICI)1097-0134(20000501)39:2<103::AID-PROT1>3.0.CO;2-G
108 Bajorath J. Combinatorial libraries and chemoinformatics in drug discovery. Investigational Drugs WH 5, 50-51, 2000.
107 Godden JW, Xue L & Bajorath J. Combinatorial preferences affect molecular similarity/diversity calculations using binary fingerprints and Tanimoto coefficients. J Chem Inf Comput Sci 40, 163-166, 2000. dx.doi.org/10.1021/ci990316u
106 Bajorath J. Structure and function of CD44: Characteristic molecular features and analysis of the hyaluronan binding site, in: Results and Problems in Cell Differentiation, Vol. 33. Mammalian Carbohydrate Recognition Systems. PR Crocker (Ed.), Springer-Verlag, Heidelberg New York, pp. 85-103, 2000.
105 Godden JW, Xue L, Stahura FL & Bajorath J. Searching for molecules with similar biological activity: Analysis by fingerprint profiling. Pac Symp Biocomput 8, 566-575, 2000.
104 Bajorath J. Specificity of the tumor necrosis factor receptor superfamily. J Mol Graph Model 17, 220-222, 1999.
103 Stahura FL, Xue L, Godden JW & Bajorath J. Molecular scaffold-based design and comparison of combinatorial libraries focused on the ATP binding site of protein kinases. J Mol Graph Model 17, 1-9, 1999.
102 Bajorath J. Three-dimensional analysis of CD6 mutagenesis and monoclonal antibody binding studies using the X-ray structure of the Mac-2 binding protein and a molecular model of the CD6 ligand binding domain. J Mol Model 5, 263-270, 1999.
101 Godden JW, Stahura F & Bajorath J. Statistical analysis of computational docking of large compound databases to distinct protein binding sites. J Comp Chem 20, 1634-1643, 1999.
100 Xue L, Godden JW & Bajorath J. Database searching for compounds with similar biological activity using short binary bit string representations of molecules. J Chem Inf Comput Sci 39, 881-886, 1999.
99 Bajorath, J. A molecular model of inducible costimulator protein and three-dimensional analysis of its relation to the CD28 family of T cell-specific costimulatory receptors. J Mol Model 5, 169-176, 1999.
98 Xue L, Godden JW, Gao H & Bajorath J. Identification of a preferred set of molecular descriptors for compound classification based on principal component analysis. J Chem Inf Comput Sci 39, 699-704, 1999.
97 Bajorath J. Identification of the ligand binding site in Fas (CD95) and analysis of Fas-ligand interactions. Proteins: Struct, Funct & Genet 35, 475-482, 1999.
96 Bajorath J. Analysis of Fas-ligand interactions using a molecular model of the receptor-ligand interface. J Comput-Aided Mol Des 13, 409-418, 1999.
95 Xue L & Bajorath J. Distribution of molecular scaffolds and R-groups isolated from large compound databases. J Mol Model 5, 97-102, 1999.
94 Cunningham MD, Bajorath J, Somerville JE & Darveau RP. Escherichia coli and Porphyromonas gingivalis interaction with CD14: Implications for myeloid and non-myeloid cell activation. Clin Infect Dis 28, 497-504, 1999.
93 Gao H & Bajorath J. Comparison of binary and 2D QSAR analysis using inhibitors of human carbonic anhydrase II as a test case. Mol Diversity 4, 115-130, 1999.
92 Gao H, Williams C, Labute P & Bajorath, J. Binary QSAR analysis of estrogen receptor ligands. J Chem Inf Comput Sci 39, 164-168, 1999.
91 Bajorath J, Klein TE, Lybrand TP, & Novotny J. Computer-aided drug discovery: From target proteins to drug candidates. Pac Symp Biocomput 7, 413-414, 1999.
90 Godden JW, Stahura F & Bajorath J. Evaluation of docking strategies for virtual screening of compound databases: cAMP-dependent Ser/Thr kinase as an example. J Mol Graph Model 16, 139-143, 1998.
89 Bajorath J. Detailed comparison of two molecular models of the human CD40 ligand with an X-ray structure and assessment of model-based mutagenesis and residue mapping studies. J Biol Chem 273, 24603-24609, 1998.
88 Starling GC, Kiener PA, Aruffo A & Bajorath J. Analysis of the ligand binding site in Fas (CD95) by site-specific mutagenesis and comparison with TNFR and CD40. Biochemistry 37, 3723-3726, 1998. dx.doi.org/10.1021/bi972959d
87 Bajorath J, Greenfield B, Munro SB, Day AJ & Aruffo A. Identification of CD44 residues important for hyaluronan binding. J Biol Chem 273, 338-343, 1998.
86 Bajorath J. From tumor necrosis factor receptor to RANK, from selectins and link proteins to CD44: New molecular models of cell surface receptors and analysis of specificity determinants. J Mol Modeling 4, 239-249, 1998.
85 Bajorath J. Three-dimensional models of cell surface proteins and identification of binding sites. J Mol Modeling 4, 1-11, 1998.
84 Bajorath J, Klein TE & Lybrand TP. Molecular modeling in drug design and biotechnology. Pac Symp Biocomput 6, 303-304, 1998.
83 Bajorath J. Cell surface receptors and adhesion molecules, three-dimensional structures, in: Encyclopedia of Immunology, 2nd Edition, Eds. PJ Delves & I Roitt, Academic Press, London, 515-520, 1998.
82 Rosok MJ, Eghtedarzadeh-Kandri M, Young K, Bajorath J, Glaser S & Yelton DE. Analysis of BR96 binding sites for antigen and anti-idiotype by codon-based scanning mutagenesis. J Immunol 160, 2353-2359, 1998.
81 Skonier JE, Bodian DL, Emswiler J, Bowen MA, Aruffo A & Bajorath J. Mutational analysis of the CD6 ligand binding domain. Protein Eng 10, 943-947, 1997.
80 Bajorath, J. Antibody engineering. FEBS Lett 412, 646-647, 1997.
79 Aruffo A, Bowen M, Haynes B, Patel D, Starling G, Gebe JA & Bajorath J. Analysis of CD6-ligand interactions: A paradigm for SRCR domain function. Immunology Today 18, 498-504, 1997.
78 Skonier JE, Bowen MA, Aruffo A & Bajorath J. CD6 recognizes the neural adhesion molecule BEN. Protein Sci 6, 1768-1770, 1997. dx.doi.org/10.1002/pro.5560060818
77 Todderud G, Nair X, Lee D, Alford J, Davern L, Stanley P, Bachand C, Lapointe P, Marinier A, Martel A, Menard M, Wright JJK, Bajorath J, Hollenbaugh D, Aruffo A & Tramposch KM. BMS-190394, a selectin inhibitor, prevents rat cutaneous inflammatory reactions. J Pharmacol Exp Therapeutics 282, 1298-1304, 1997.
76 Bajorath J, Metzler WJ & Linsley PS. Molecular modeling of CD28 and three-dimensional analysis of residue conservation in the CD28/CD152 family. J Mol Graph Model 15, 135-139, 1997. dx.doi.org/10.1016/S1093-3263(97)00020-X
75 Marinier A, Martel A, Banville J, Bachand C, Remillard R, Lapointe P, Turmel B, Menard M, Harte WE Jr, Wright JJK, Todderud G, Tramposch KM, Bajorath J, Hollenbaugh D & Aruffo A. Sulfated galactocerebrosides as potential anti-inflammatory agents. J Med Chem 40, 3234-3247, 1997. dx.doi.org/10.1021/jm9606960
74 Bajorath J, Linsley PS & Metzler WJ. Molecular modeling of immunoglobulin superfamily proteins: CTLA-4 (CD152) - Comparison of a predicted and experimentally determined structure. J Mol Modeling 3, 287-293, 1997.
73 Metzler WJ, Bajorath J, Fenderson W, Shaw S-Y, Constantine KL, Naemura J, Leytze G, Peach RJ, Lavoie TB, Mueller L & Linsley PS. The solution structure of CTLA-4 identifies a binding site for CD80/CD86 which is conserved in CD28. Nature Struct Biol 4, 527-531, 1997.
72 Bajorath J. A molecular model of CD86 and analysis of mutations which disrupt receptor binding. J Mol Modeling 3, 216-223, 1997.
71 Bowen MA, Bajorath J, D’Egidio M, Whitney GS, Palmer D, Kobarg J, Starling GC, Siadak AW & Aruffo A. Characterization of mouse ALCAM (CD166): The CD6 binding site is conserved in different homologues and mediates cross-species binding. Eur J Immunol 27, 1469-1478, 1997.
70 Starling GC, Bajorath J, Ledbetter JA, Aruffo A & Kiener PA. Identification of amino acid residues important for ligand binding to Fas. J Exp Med 185, 1487-1492, 1997.
69 Loo DT, Chalupny NJ, Bajorath J, Shuford WW, Mittler RS & Aruffo A. Analysis of 4-1BBL and laminin binding to murine 4-1BB, a member of the tumor necrosis factor receptor superfamily, and comparison with human 4-1BB. J Biol Chem 272, 6448-6456, 1997.
68 Bodian DL, Skonier JE, Bowen MA, Neubauer M, Siadak AW, Aruffo A & Bajorath J. Identification of residues in CD6 which are critical for ligand binding. Biochemistry 36, 2637-2641, 1997. dx.doi.org/10.1021/bi962560+
67 Bajorath J & Aruffo A. Prediction of the three-dimensional structure of the human Fas receptor by comparative molecular modeling. J Comput-Aided Mol Des 11, 3-8, 1997.
66 Bowen MA, Bajorath J & Aruffo A. CD166, in: Leukocyte Typing VI. New Adhesion Structures and CD Antigens, Ed. M Miyasaka, Garland Publishing, Inc. , New York, 129-131, 1997.
65 Hollenbaugh D, Bajorath J & Aruffo A. Cell adhesion molecules and their cellular targets, in: Bioorganic Chemistry: Carbohydrates, Ed. SM Hecht, Oxford University Press, New York, 1997.
64 Bajorath J & Klein TE. Modern concepts in molecular modeling. Pac Symp Biocomput 5, 4-5, 1997.
63 Bajorath J & Linsley PS. Molecular modeling of immunoglobulin superfamily proteins: Predicting the three-dimensional structure of the extracellular domain of CTLA-4 (CD152). J Mol Modeling 3, 117-123, 1997.
62 Bajorath J & Aruffo A. Construction and analysis of a detailed molecular model of the ligand binding domain of the human B cell receptor CD40. Proteins: Struct, Funct & Genet. 27, 59-70, 1997.
61 Stenkamp R, Aruffo A & Bajorath J. Protein superfamily members as targets for computer modeling: The carbohydrate recognition domain of a macrophage lectin. Pac Symp Biocomput 5, 432-440, 1997.
60 Shapiro RA, Cunningham MD, Ratcliffe K, Seachord C, Blake J, Bajorath J, Aruffo A & Darveau RP. Identification of CD14 residues involved in specific lipopolysaccharide recognition. Infect Immun 65, 293-297, 1997.
59 Skonier JE, Bowen MA, Emswiler J, Aruffo A & Bajorath J. Mutational analysis of the CD6 binding site in activated leukocyte cell adhesion molecule. Biochemistry 35, 14743-14748, 1996.
58 Bajorath J. A molecular model of a macrophage lectin and analysis of its binding site. J Mol Graph 14, 297-301, 1996.
57 Greene JL, Leytze GM, Emswiler J, Peach R, Bajorath J, Cosand W & Linsley PS. Covalent dimerization of CD28/CTLA-4 and oligomerization of CD80/CD86 regulate T cell costimulatory interactions. J Biol Chem 271, 26762-26771, 1996.
56 Sheriff S, Jeffrey PD & Bajorath J. Comparison of CH1 domains in different classes of murine antibodies. J Mol Biol 263, 385-389, 1996. dx.doi.org/10.1006/jmbi.1996.0582
55 Skonier JE, Bowen MA, Emswiler J, Aruffo A & Bajorath J. Recognition of diverse proteins by members of the immunoglobulin superfamily: Delineation of the receptor binding site in the human CD6 ligand ALCAM. Biochemistry 35, 12287-12291, 1996. dx.doi.org/10.1021/bi961038k
54 Novotny J & Bajorath J. Computational biochemistry of antibodies and T-cell receptors. In: Advances in Protein Chemistry: Antigen binding molecules: Antibodies and T cell receptors. Adv Prot Chem 49, 147-258, 1996.
53 Foy TM, Aruffo A, Bajorath J, Buhlmann JE & Noelle RJ. Immune regulation by CD40 and its ligand gp39. Ann Rev Immunol 14, 591-617, 1996.
52 Rosok MJ, Yelton DE, Harris LJ, Bajorath J, Hellström KE, Hellström I, Cruz GA, Kristensson K, Lin H, Huse WD, Glaser SM. Combinatorial libraries for the rapid humanization of anticarcinoma BR96 Fab. J Biol Chem 271, 22611-22618, 1996.
51 Bowen MA, Bajorath J, Siadak AW, Modrell B, Malacko AR, Marquardt H, Nadler SG & Aruffo A. The amino-terminal immunoglobulin-like domain of activated leukocyte cell adhesion molecule (ALCAM) binds specifically to the membrane-proximal scavenger receptor cysteine rich domain of CD6. J Biol Chem 271, 17390-17396, 1996.
50 Sheriff S, Chang CY, Jeffrey PD, Bajorath J. X-ray structure of the uncomplexed anti-tumor antibody BR96 and comparison with its antigen-bound form. J Mol Biol 259, 938-946, 1996. dx.doi.org/10.1006/jmbi.1996.0371
49 Bajorath J, Seyama K, Nonoyama S, Ochs HD, Aruffo A. Classification of mutations in the human CD40 ligand, gp39, which are associated with X-linked hyper IgM syndrome. Protein Sci 5, 531-534, 1996. dx.doi.org/10.1002/pro.5560050316
48 Bajorath J & Aruffo A. Structure-based modeling of the ligand binding domain of the human cell surface receptor CD23 and comparison of two independently derived molecular models. Protein Sci 5, 240-247, 1996. dx.doi.org/10.1002/pro.5560050207
47 Siemers NO, Yelton DE, Bajorath J & Senter PD. Modifying the specificity of Enterobacter cloacae P99 ß-lactamase within an M13 phage vector. Biochemistry 35, 2104-2111, 1996. dx.doi.org/10.1021/bi9514166
46 Bajorath J & Sheriff S. Comparison of an antibody model with an X-ray structure: The variable fragment of BR96. Proteins: Struct, Funct & Genet 24, 152-157, 1996.
45 Chalupny NJ, Aruffo A, Esselstyn JM, Chang PY, Bajorath J, Blake J, Gilliland LK, Ledbetter JA & Tepper MA. Specific binding of Fyn and phosphatidylinositol 3-kinase to the B cell surface glycoprotein CD19 through their src homolgy 2 domains. Eur J Immunol 25, 2978-2984, 1995.
44 Lee N, Malacko AR, Ishitani A, Chen MC, Bajorath J, Marquardt H & Geraghty DE. The membrane-bound and soluble forms of HLA-G bind identical sets of endogenous peptides but differ with respect to TAP association. Immunity 3, 591-600, 1995.
43 Bajorath J & Aruffo A. A template for generation and comparison of three-dimensional selectin models. Biochem Biophys Res Comm 216, 1018-1023, 1995.
42 Edwards CP, Farr AG, Marken JS, Nelson A, Bajorath J, Hellström KE, Hellström I & Aruffo A. Cloning of the 12A8 Antigen, the Murine Homolog of the Tumor-Associated Antigen H-L6, and Fine Mapping of the Epitope Recognized by the Anti-H-L6 Monoclonal Antibody L6. Biochemistry 34, 12653-12660, 1995.
41 Bajorath J, Harris L & Novotny J. Conformational similarity and systematic displacements of CDR loops in high-resolution X-ray structures. J Biol Chem 270, 22081-22084, 1995.
40 Bajorath J, Bowen MA, Aruffo A. Molecular model of the N-terminal receptor-binding domain of the human CD6 ligand ALCAM. Protein Sci 4, 1644-1647, 1995.
39 Peach RJ, Bajorath J, Naemura J, Leytze G, Aruffo A & Linsley PS. Both extracellular immunoglobulin-like domains of CD80 contain residues critical for binding to T-cell surface receptors CTLA-4 and CD28. J Biol Chem 270, 21181-21187, 1995.
38 Yelton DE, Rosok MJ, Cruz G, Cosand WL, Bajorath J, Hellström I, Hellström KE, Huse WD & Glaser SM. Affinity maturation of the BR96 anti-carcinoma antibody in vitro by codon-based mutagenesis. J Immunol 155, 1994-2004, 1995.
37 Bajorath J, Marken JS, Chalupny NJ, Spoon TL, Siadak AW, Gordon M, Noelle RJ, Hollenbaugh D & Aruffo A. Analysis of gp39/CD40 interactions using molecular models and site directed mutagenesis. Biochemistry 34, 9884-9892, 1995.
36 Linsley PS, Nadler S, Bajorath J, Peach RJ, Rogers J, Bradshaw J, Leung HT, Rogers J, Bradshaw J, Stebbins M, Leytze G, Brady W, Malacko AR, Marquardt H & Shaw S-Y. Binding stoichiometry of the cytotoxic T lymphocyte-associated molecule-4 (CTLA-4). A disulfide-linked homodimer binds two CD86 molecules. J Biol Chem 270, 15417-15424, 1995.
35 Bajorath J & Novotny J. Model building of antibody combining sites. Therapeutic Immunol 2, 95-103, 1995.
34 Linsley PS, Ledbetter JA, Peach RJ & Bajorath J. CD28/CTLA-4 receptor structure, stoichiometry of ligand binding, and aggregation during T cell activation. Res Immunol 146, 130-140, 1995.
33 Jeffrey PD, Bajorath J, Chang CY, Yelton DE, Hellström I, Hellström KE & Sheriff S. The X-ray structure of an anti-tumour antibody in complex with antigen. Nature Struct Biol 2, 466-471, 1995.
32 Bajorath J, Chalupny NJ, Marken JS, Siadak AW, Skonier J, Gordon M, Hollenbaugh D, Noelle RJ, Ochs HD & Aruffo A. Identification of residues on CD40 and its ligand which are critical for receptor-ligand interactions. Biochemistry 34, 1833-1844, 1995.
31 Bajorath J, Stenkamp R & Aruffo A. Comparison of a protein model with its X-ray structure: The ligand binding domain in E-selectin. Bioconjug Chem 6, 3-6, 1995.
30 Harris L & Bajorath J. Profiles for the analysis of immunoglobulin sequences: Comparison of V gene subgroups. Protein Sci 4, 306-310, 1995. dx.doi.org/10.1002/pro.5560040217
29 Bajorath J & Aruffo A. Molecular model of the extracellular lectin-like domain in CD69. J Biol Chem 269, 32457-32463, 1994.
28 Bajorath J, Peach RJ, Linsley PS. Immunoglobulin fold characteristics of B7-1 (CD80) and B7-2 (CD86). Protein Sci 3, 2148-2150, 1994. dx.doi.org/10.1002/pro.5560031128
27 Stenzel-Johnson P, Yelton DE & Bajorath J. Identification of residues in the monoclonal anti-tumor antibody L6 important for the binding to tumor antigen. Biochemistry 33, 14400-14406, 1994.
26 Linsley PS, Peach RJ, Gladstone P & Bajorath J. Extending the B7 gene family. Protein Sci 3, 1341-1343, 1994. dx.doi.org/10.1002/pro.5560030820
25 Harte WE Jr & Bajorath J. Synergism of calcium and carbohydrate binding to a mammalian lectin suggested by a dynamic model. J Am Chem Soc 116, 10394-10398, 1994.
24 Bajorath J. Three-dimensional model structure of the BR96 monoclonal antibody variable fragment. Bioconjug Chem 5, 213-219, 1994.
23 Bajorath J & Aruffo A. Three-dimensional protein models: Insights into structure, function, and molecular interactions. Bioconjug Chem 5, 173-181, 1994.
22 Bajorath J, Hollenbaugh D, King, G Harte WE Jr, Darveau RP, Eustice D & Aruffo A. The CD62/P-selectin binding sites for HL-60 cells and sulfatides are overlapping. Biochemistry 33, 1332-1339, 1994.
21 Linsley PS, Greene JL, Brady W, Bajorath J, Ledbetter JA & Peach RJ. Human B7-1 (CD80) and B7-2 (CD86) bind with similar avidities but distinct kinetics to CD28 and CTLA-4 receptors. Immunity 1, 793-801, 1994.
20 Peach RJ, Bajorath J, Brady W, Leytze G, Greene J, Naemura J & Linsley PS. Conserved and non-conserved residues of CDR-analogous regions in CTLA-4 and CD28 determine the binding to B7-1. J Exp Med 180, 2049-2058, 1994.
19 Marken JS, Bajorath J, Hellström I, Hellström KE & Aruffo A. Isolation of the human tumor-associated antigen L6: Membrane topology of L6 and identification of the epitope region of an anti-L6 monoclonal antibody. J Biol Chem 269, 7397-7401, 1994.
18 Hayden MS, Linsley PS, Gayle MA, Bajorath J, Brady WA, Norris NA, Fell HP, Ledbetter JA & Gilliland LK. Single chain mono- and bispecific antibody derivatives with novel biological properties and anti-tumor activity from a COS cell transient expression system. Therapeutic Immunol 1, 3-15, 1994.
17 Chang CY, Jeffrey PD, Bajorath J, Hellström I, Hellström KE & Sheriff S. Crystallization and preliminary X-ray analysis of the monoclonal anti-tumor antibody BR96 and its complex with the LeY determinant. J Mol Biol 235, 372-376, 1994.
16 Hsiao K, Bajorath J & Harris LJ. Humanization of 60.3, an anti-CD18 antibody; importance of the L2 loop. Protein Eng 7, 815-822, 1994.
15 Hollenbaugh D, Bajorath J, Stenkamp R & Aruffo A. Interaction of P-selectin (CD62) and its cellular ligand: Analysis of critical residues. Biochemistry 32, 2960-2966, 1993.
14 Bajorath J, Stenkamp R & Aruffo A. Knowledge-based model building of proteins: Concepts and examples. Protein Sci 2, 1798-1810, 1993. dx.doi.org/10.1002/pro.5560021103
13 Aruffo A, Farrington M, Hollenbaugh D, Li X, Milatovitch A, Nonoyama S, Bajorath J, Grosmaire LS, Stenkamp R, Neubauer M, Roberts RL, Noelle RJ, Ledbetter JA, Francke U & Ochs HD. The CD40 ligand, gp39, is defective in activated cells from patients with X-linked hyper-IgM syndrome. Cell 72, 291-300, 1993.
12 Bajorath J & Fine RM. On the use of minimization from many randomly generated loop structures in modeling antibody combining sites. Immunomethods 1, 137-146, 1992.
11 Ganju RK, Smiley ST, Bajorath J, Novotny J & Reinherz E. Similarity between fluorescein-specific T cell receptor and antibody in chemical details of antigen recognition. Proc Natl Acad Sci USA 89, 11552-11556, 1992.
10 Fell HP, Gayle MA, Schieven GL, Yelton DE, Lipsich L, Hellström KE, Hellström I, Marken J, Aruffo A & Bajorath J. Chimeric L6 anti-tumor antibody. Genomic construction, expression, and characterization of the antigen binding site. J Biol Chem 267, 15552-15558, 1992.
9 Bajorath J, Kraut J, Li Z, Kitson DH & Hagler AT. Theoretical studies on the dihydrofolate reductase mechanism: Electronic polarization of bound substrates. Proc Natl Acad Sci USA 88, 6423-6426, 1991.
8 Bajorath J, Li Z, Fitzgerald G, Kitson DH, Farnum M, Fine RM, Kraut J & Hagler AT. Changes in the electron density of the cofactor NADPH on binding to E. coli dihydrofolate reductase. Proteins: Struct, Funct & Genet 11, 263-270, 1991.
7 Bajorath J, Kitson DH, Kraut J & Hagler AT. The electrostatic potential of Escherichia coli dihydrofolate reductase. Proteins: Struct, Funct & Genet 11, 1-12, 1991.
6 Bajorath J, Kitson DH, Fitzgerald G, Andzelm J, Kraut J & Hagler AT. Electron redistribution on binding of a substrate to an enzyme: Folate and dihydrofolate reductase. Proteins: Struct, Funct & Genet 9, 217-224, 1991.
5 Wolf W, Bajorath J, Müller A, Raghunathan S, Singh TP, Hinrichs W & Saenger W. Inhibition of proteinase K by methoxysuccinyl-Ala-Ala-Pro-Ala-chloromethyl ketone. An X-ray study at 2.2 Å resolution. J Biol Chem 26, 17695-17699, 1991.
4 Bajorath J, Raghunathan S, Hinrichs W & Saenger W. Long-range structural changes in proteinase K triggered by calcium ion removal. Nature 337, 481-484, 1989. dx.doi.org/10.1038/337481a0
3 Betzel C, Bellemann M, Pal GP, Bajorath J, Saenger W & Wilson KS. X-ray and model building studies on the specificity of the active site of proteinase K. Proteins: Struct, Funct & Genet 4, 157-164, 1988.
2 Bajorath J, Hinrichs W & Saenger W. The activity of proteinase K is controlled by calcium. Eur J Biochem 176, 441-447, 1988.
1 Bajorath J, Pal GP & Saenger W. Inhibition and autolysis of proteinase K, a subtilisin-related serine proteinase from the fungus Tritirachium album Limber. Biochem Biophys Acta 954, 176-182, 1988.