AI Solutions for Computational Chemistry

Title: Artificial Intelligence (AI) Solutions for Computational Chemistry and Organic Chemistry TasksAbstract: In this talk, we will present a fully transferable deep learning molecular potential that is applicable to complex and diverse molecular systems. Focusing on parametrization for organic molecules (with CHNOSFCl atoms so far), we have developed the ANI neural network potential which is highly accurate compared to reference QM calculations at speeds 10^7 faster. The ANI potential is shown to accurately represent the underlying physical chemistry of molecules through various test cases including chemical reactions (both thermodynamics and kinetics), thermochemistry, structural optimization, solvent effect, and molecular dynamics simulations. The results presented in this talk will provide evidence of the general applicability of ANI potential to various organic chemistry problems along the drug discovery and development pipeline.
Bio: Olexandr Isayev is an Assistant Professor at UNC Eshelman School of Pharmacy, the University of North Carolina at Chapel Hill. In 2008, Olexandr received his Ph.D. in computational chemistry. He was Postdoctoral Research Fellow at the Case Western Reserve University and a scientist at the government research lab before joining UNC. Olexandr received “Emerging Technology Award” from the American Chemical Society (ACS) and the GPU computing award from NVIDIA. The research in his lab focuses on connecting artificial intelligence (AI) with chemical sciences.