Research in our group mostly focuses on the development of computational methods for pharmaceutical research and chemical biology and on large-scale compound data mining. Among others, these include methods for the systematic detection, analysis, and visualization of structure-activity relationships (SARs), generation of activity landscapes of compound data sets, exploration of molecular similarity, and prediction of novel active compounds. In addition, compound data mining aims to comprehensively explore, for example, activity cliffs and the way in which they are formed, compound selectivity and promiscuity patterns, or molecular building blocks associated with specific biological activities. Furthermore, we study fundamental questions related to chemical space design and navigation of biologically relevant chemical space. Common to the areas of compound data mining and chemical space representation is increasing focus on addressing 'Big Data' issues including data heterogeneity and complexity.
Chemical Computing Group and OpenEye Scientific Software are gratefully acknowledged for supporting the educational and research activities of the LSI Department.