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| About Alex Tropsha (University of North Carolina) |
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Alexander Tropsha was born in Moscow in 1960. He received his MS in Chemistry from Moscow State University in 1982 and PhD in Biochemistry and Pharmacology in 1986 from the same university. He immigrated to the US in 1989. In 1991, after two years of postdoctoral research at the University of North Carolina at Chapel Hill, he joined the UNC School of Pharmacy as an Assistant Professor and Director of the Laboratory for Molecular Modeling. Dr. Tropsha has since been promoted to the position of full Professor; he also holds position of the Associate Director of the Carolina Center for Genome Sciences.
The major area of Tropsha’s research is Biomolecular Informatics, which implies understanding relationships between structures (organic or macromolecular) and their properties (activity or function). In recent years, his group has developed several important methodologies and software tools for Computer Assisted Drug Design. Concurrently, they have developed a new approach to protein 3D structure analysis and prediction based on the principles of statistical geometry (Delaunay tessellation). This approach affords determination of key structural and sequence motifs responsible for protein function.
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Cheminformatics Analysis of Polypharmacological Data
Alexander Tropsha, The Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, School of Pharmacy, UNC-Chapel Hill, Chapel Hill, NC 27599, USA
Modern experimental drug discovery efforts are increasingly focusing on the development of multi-target directed ligands that produce the desired pharmacological and pharmaceutical effects. Recent advances in high-throughput screening and multi-target testing of compound libraries coupled with the establishing of publicly available databases of biologically tested compounds call for the development of sophisticated computational tools and models of complex chemical genomics data. We define a dataset as complex if multiple measures of biological activity/property are reported for all (or most of) compounds in the entire chemical library. The examples of complex datasets include Pubchem, PDSP, DSS-Tox, and others. We shall consider emerging methodologies for analyzing complex chemogenomics datasets such as subspace clustering, database graph analysis, and others. We shall present models that relate compound structure to their multi-target profiles (as opposed to more traditional single target specific models). Modeling of complex chemogenomics databases present new challenges and new frontiers in molecular modeling.
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