The Isayev lab works at the interface of theoretical chemistry, pharmaceutical sciences, and artificial intelligence (AI). We are working towards the acceleration of molecular discovery and design, in particular of kinase inhibitors. We also focus on both generative and predictive machine learning models for chemical and biological data and the automation of scientific experimentation
De Novo molecular design. The de novo molecular design problem involves generating novel molecular structures or focused molecular libraries with desirable properties. It solves a so-called inverse design problem. We develop artificial intelligence method that enables the design of chemical libraries with the desired physicochemical and biological properties or both.
Accelerating computational chemistry with deep learning. We are developing fully transferable deep learning potentials for molecular and materials systems. Such atomistic potentials are highly accurate compared to reference QM calculations at speeds 10^7 faster. Neural network potentials are shown to accurately represent the underlying physical chemistry of molecules through various test cases including chemical reactions, kinetics, thermochemistry, structural optimization, and molecular dynamics simulations.
2008 Ph.D. in Theoretical Chemistry, Jackson State University
2002 M.S. in Chemistry, Dnepropetrovsk National University, Ukraine
2009-2012 Postdoctoral Fellow, Case Western Reserve University
Department of Chemistry
Carnegie Mellon University
Mellon Institute, 511A
4400 Fifth Avenue
Pittsburgh, PA 15213