2018.12.17 Integrated Molecular Modeling and Machine Learning for Molecular Design
题 目：Integrated Molecular Modeling and Machine Learning for Molecular Design
报告人：Professor Yingkai Zhang
Department of Chemistry at New York University
The overall goal of my research program is to develop and apply computational tools for rational molecular design. In this talk, I will present our recent advances in targeting protein-protein interactions, developing machine-learning based protein-ligand scoring functions, and exploring deep learning models to accurately predict molecular energies with force-field optimized geometries.
Yingkai Zhang is a professor in Department of Chemistry at New York University. He received his B.S. degree in Chemistry from Nanjing University and his Ph.D. degree from Duke University. His postdoctoral research was conducted at Howard Hughes Medical Institute, University of California at San Diego. He was a recipient of the National Science Foundation CAREER Award, the NYSTAR James D. Watson Young Investigator Award, Whitehead Fellowship for Junior Faculty in Biomedical and Biological Sciences, and Maximizing Investigators' Research Award (MIRA) from National Institute of General Medical Sciences. His current research interests are: integrated molecular modeling and machine learning, rational modulator design to target protein-protein interactions, and computer simulations of biomolecular systems.