(357x) Computational Design of HIV-1 Entry Inhibitors

My current research focuses on the generation of new and improved inhibitors of the HIV-1 Env glycoprotein. By employing the latest computational drug design tools utilizing GPU-accelerated computation, high-performance computing, custom algorithms for target preparation, and conformational sampling based on all-atom molecular dynamics simulations, I aim both to support the generation of new molecules with superior activity, especially in terms of breadth across HIV-1 strains, and to rationalize with 3D all-atom models the mechanisms by which the inhibitors interact with the HIV-1 envelope glycoprotein complex (Env) in its various conformational states. I have experience in both developing mechanistic models, and statistical/machine learning based models which are more descriptive and explorative correlations with experimental data. Also, I have conducted large-scale screenings of drug candidates, using a combination of artificial neural networks and physics-based models to determine optimal drug candidates (activity, ADME/Tox ...), and understanding the mechanisms of ligand-protein interactions. In this poster, I will be presenting on selected research topics from my Ph.D. and Postdoctoral work related to structure-based drug design and improving understanding of the protein-inhibitor interactions.