Computer-Guided Efficient Discovery of Potent Enzyme Inhibitors

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. Emphasis is placed on optimization of the resultant leads to yield potent, drug-like inhibitors. Monte Carlo/free-energy perturbation (FEP) simulations are often executed to identify the most promising choices for substituents on rings, heterocycles, and linking groups.  The illustrated applications center on the design of inhibitors targeting HIV-1 reverse transcriptase, macrophage migration inhibitory factor, and JAK2 kinase. Micromolar leads have been rapidly advanced to low nanomolar inhibitors, and numerous crystal structures for protein-inhibitor complexes have been obtained. Development and use of fluorescence polarization assays provide direct binding data. Key computational issues are considered including force fields, atomic charge models, conformational sampling, computation of absolute free energies of binding, and unbinding pathways from metadynamics.