Location
Join us on September 13th at 1:00 pm Eastern, for the next installment in the Bridges-2 Webinar series, featuring the latest advancements in machine learning and AI for drug discovery and molecular design, as developed by Isayev's Lab at CMU.
Speaker: Olexandr Isayev, Carnegie Mellon University
Abstract: In this talk, we will provide an overview into the latest developments of machine learning and AI methods and application to the problem of drug discovery and molecular design at Isayev’s Lab at CMU. We identify several areas where existing methods have the potential to accelerate computational chemistry research and disrupt more traditional approaches. In this work, we present the 2nd generation of our atoms-in-molecules neural network potential (AIMNet2), which is applicable to species composed of up to 14 chemical elements in both neutral and charged states, making it a valuable model for modeling the majority of non-metallic compounds. Using an exhaustive dataset of 20 million hybrid quantum chemical calculations, AIMNet2 combines ML-parameterized short-range and physics-based long-range terms to attain generalizability that reaches from simple organics to divers! e molecules with “exotic” element-organic bonding. We show that AIMNet2 outperforms semi-empirical GFN-xTB and is on par with reference density functional theory for interaction energy contributions, conformer search tasks, torsion rotation profiles, and molecular-to-macromolecular geometry optimization.
About the speaker: Olexandr is a full-time professor in the Department of Chemistry at Carnegie Mellon University. In 2008, Olexandr received his Ph.D. in computational chemistry. He was a Postdoctoral Research Fellow at Case Western Reserve University and a scientist at the government research lab. Before CMU, he was a faculty at UNC Eshelman School of Pharmacy, the University of North Carolina at Chapel Hill. Olexandr is a 2023 Scialog Fellow and Associate Editor for the ACS Journal of Chemical Information and Modeling. The research in his lab focuses on connecting artificial intelligence (AI) with chemical sciences.