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The Vanderbilt Institute of Chemical Biology presents Dr. William Jorgensen, "Drug Discovery Accelerated by Computational Methods"

Abstract
Drug discovery is being pursued through computer-aided design, synthesis, biological assaying, and crystallography. Lead identification features de novo design with the ligand growing program BOMB or docking of commercial compound libraries. The cheminformatics program QikProp is applied to filter candidate molecules to ensure that they have drug-like properties. The focus of this lecture will be optimization of the resultant leads to yield potent inhibitors. Specifically, Monte Carlo/free-energy perturbation simulations are executed to identify the most promising choices for substituents on rings, heterocycles, and linking groups. The designed compounds are then synthesized and assayed. Successful application has been achieved for HIV reverse transcriptase, FGFR1 kinase, and human and Plasmodium falciparum macrophage migration inhibitory factor (MIF); micromolar leads have been rapidly advanced to low nanomolar or picomolar inhibitors.

Research
Organic, medicinal, and computational chemistry including simulations of organic and enzymatic reactions, computer-aided drug design, and synthesis and development of therapeutic agents targeting infectious, inflammatory, and hyperproliferative diseases.

Our approach features focused synthetic organic chemistry driven by state-of-the-art molecular design. The computations center on modeling protein-inhibitor complexes including docking for virtual high-throughput screening, growing of combinatorial libraries inside binding sites with BOMB, and lead-optimization guided by Monte Carlo free-energy simulations. Synthesis and optimization of the most promising leads are performed in our laboratory, and biological testing and crystallography are pursued with collaborators. The approach has allowed efficient discovery of extraordinarily potent anti-HIV, anti-inflammatory, and anti-cancer agents. Current protein targets include HIV-1 reverse transcriptase, MIF, hDM2, hDMX, Tdp1, and FGFR1 kinase.

The aims include elucidation of reaction mechanisms, medium effects on reaction rates, and effects of site-specific mutations on enzymatic reactions. A QM/MM approach is taken; the energetics of the reacting systems are described quantum mechanically with ab initio, DFT, or advanced semiempirical QM methods such as our PDDG/PM3 procedure. The environment including solvent molecules are represented using molecular mechanics and the sampling is normally performed with Monte Carlo statistical mechanics. Our group is also recognized as a leader in the development of force fields for water, organic and biomolecular systems and in the development of comprehensive software for molecular modeling (BOSS and MCPRO).