The seminar is sponsored by NYU-ECNU Center for Computational Chemistry at NYU Shanghai
Abstract:
By taking advantage of the high-performance computers, docking-based virtual screening (VS) has gained more and more attention, and has become one of the core technologies in drug design and development. However, the prediction accuracy of molecular docking may be impaired by some inherent defects, such as simplified scoring functions and ignorance of protein flexibility. In this talk, I will discuss some new strategies developed in my group to improve the efficiency and accuracy of docking-based virtual screening. In the first part, I will discuss the MIEC-SVM approach based on free energy decomposition and machine learning algorithm, which shows good capability to identify binding peptides of modular domains and small molecule inhibitors of drug targets. In the second part, I will discuss a novel parallel virtual screening strategy by integrating molecular docking and complex-based pharmacophore searching based on multiple protein structures.
Biography:
Tingjun Hou obtained his Ph. D. in Physical Chemistry from Peking University in 2002. He is a postdoctoral scholar and a research scientist in the College of Chemistry and Molecular Engineering at Peking University (2002~2004) and the Department of Chemistry and Biochemistry at UCSD (2004~2009). He joined Soochow University as a full professor in 2009 and then moved to Zhejiang University in 2013. His research focuses on development of new methods for computer-aided drug design (CADD) and design of potential lead compounds for important drug targets. He is a member of Editorial Board or Editorial Advisory Board for Journal of Cheminformatics, Journal of Chemical Information and Modeling, Current Pharmaceutical Design, Chemical Biology & Drug Design, Mini-reviews in Medicinal Chemistry, Current Computer-aided Drug Design, Theoretical Biology & Medical Modelling, and Journal of Pharmaceutics.