Associate Professor, Chemistry
Education2009-2012 Postdoctoral Fellow, Case Western Reserve University
2008 Ph.D. in Theoretical Chemistry, Jackson State University
2002 M.S. in Chemistry, Dnepropetrovsk National University, Ukraine
Theoretical and computational chemistry, machine learning, cheminformatics, drug discovery, computer-aided molecular design, materials informatics
The Isayev lab works at the interface of theoretical chemistry, pharmaceutical sciences and computer science. In particular, we are using molecular simulations and artificial intelligence (AI) to solve hard problems in chemistry. We are working towards the acceleration of molecular discovery by the combination of AI, informatics and high-throughput quantum chemistry. We also focus on both generative and predictive ML models for chemical and biological data. Details on specific projects can be found below.
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 107faster. 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.
Material informatics is a rapidly emerging data- and knowledge-driven approach for the identification of novel materials for a range of applications, including solar energy conversion. As the proliferation of high-throughput methods in chemical sciences is increasing the wealth of data in the field, the gap between accumulated-information and derived knowledge widens. We address the issue of scientific discovery in chemical and biological databases by introducing novel analytical approaches based on large-scale data mining and machine learning.
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.
Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis
Marcus Reis, Filipp Gusev, Nicholas G. Taylor, Sang Hun Chung, Matthew D. Verber, Yueh Z. Lee, Olexandr Isayev, and Frank A. Leibfarth. Journal of the American Chemical Society 2021 143 (42), 17677-17689. DOI: 10.1021/jacs.1c08181
Learning molecular potentials with neural networks
Gokcan, H, Isayev, O. WIREs Comput Mol Sci. 2021. DOI: 10.1002/wcms.1564
Teaching a neural network to attach and detach electrons from molecules
Zubatyuk, R., Smith, J.S., Nebgen, B.T. et al. Nat Commun 12, 4870 (2021). DOI: 10.1038/s41467-021-24904-0
Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial Intelligence
Tetiana Zubatiuk and Olexandr Isayev. Accounts of Chemical Research 2021 54 (7), 1575-1585. DOI: 10.1021/acs.accounts.0c00868
OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design
Maria Korshunova, Boris Ginsburg, Alexander Tropsha, and Olexandr Isayev. Journal of Chemical Information and Modeling 2021 61 (1), 7-13. DOI: 10.1021/acs.jcim.0c00971
Crowdsourced mapping of unexplored target space of kinase inhibitors
A. Cichonska, B. Ravikumar, R. J Allaway, S. Park, F. Wan, O. Isayev, S. Li, M. Mason, A. Lamb, Z. Tanoli, M. Jeon, S. Kim, M. Popova, S. Capuzzi, J. Zeng, K. Dang, G. Koytiger, J. Kang, C. I. Wells, T. M. Willson, T. I. Oprea, A. Schlessinger, D. H. Drewry, G. Stolovitzky, K. Wennerberg, J. Guinney, T. Aittokallio. Nature Commun. 2021, 12, 3307. DOI: 10.1038/s41467-021-23165-1
Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network.
Zubatyuk, J S Smith, J. Leszczynski. O. Isayev, Science Adv. 2019. 4 (7), eaap7885.
Approaching coupled-cluster accuracy with a general-purpose neural network potential through transfer learning.
J S Smith, BT Nebgen, R Zubatyuk, N Lubbers, C Devereux, K Barros. S. Tretiak, O. Isayev, A. Roitberg, Nature Commun. 2019, 10, 2903.
Machine learning for molecular and materials science.
K. T Butler, D. W Davies, H. Cartwright, O. Isayev, A. Walsh, Nature. 2018, 559, 547–555.
Deep Reinforcement Learning for de-novo Drug Design
M. Popova, O. Isayev, A. Tropsha, Science Adv. 2018 4 (7), eaap7885.
AFLOW-ML: A RESTful API for machine-learning predictions of materials properties
E. Gossett, C. Toher, C. Oses, O. Isayev, F. Legrain, F. Rose, E. Zurek, J. Carrete, N. Mingo, A. Tropsha, S. Curtarolo, Computational Materials Science, 2018, 152, 134-145.
Transforming Computational Drug Discovery with Machine Learning and AI
J. S. Smith, A. E. Roitberg, O. Isayev, ACS Med. Chem. Lett. 2018. 9, 1065–1069.
Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks
B. Nebgen, N. Lubbers, J. S Smith, A. E Sifain, A. Lokhov, O. Isayev, A. E Roitberg, K. Barros, S. Tretiak, J. Chem. Theory Comput., 2018, 14, 4687–4698.
ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
J. S. Smith, O. Isayev, A. E. Roitberg, Chem. Sci., 2017, 8, 3192-3203.
Universal Fragment Descriptors for Predicting Electronic Properties of Inorganic Crystals
O. Isayev, C. Oses, C. Toher, E. Gossett, S. Curtarolo, A. Tropsha, Nature Commun. 2017, 8, 15679.
Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints
O. Isayev, D. Fourches, E.N. Muratov, C. Oses, K.M. Rasch, A. Tropsha, and S. Curtarolo, Chem. Mater., 2015, 27, 735-742.
|2020||Assistant Professor, Carnegie Mellon University|
|2017–2019||Research Assistant Professor, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill|
|2016||Senior Fellow, Institute for Pure & Applied Mathematics, University of California, Los Angeles|
|2013-2016||Research Scientist, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill|
|2012-2013||Senior Scientist, US Army Engineering Research and Development Center|
Awards and Distinctions
|2017, 2014||ACS Emerging Technology Award|
|2016||Eshelman Institute for Innovation Award|
|2015||Chemical Structure Association Trust Award|
|2014||NVIDIA GPU Computing Award|
|2009||IBM-Löwdin Memorial Fellowship|