Olexandr Isayev

Connecting artificial intelligence (AI) with chemical sciences.


I am an assistant professor at the Department of Chemistry, Carnegie Mellon University. My current research interests focus on solving fundamental chemical problems with machine learning, molecular modeling, and quantum mechanics.

Featured publications:

The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules. Sci. Data 7, 2020, 134 [DOI: 10.1038/s41597-020-0473-z]

Less is more: Sampling chemical space with active learning.
Journal of Chemical Physics 148, 2018, 241733.
[DOI: 10.1063/1.5023802]

 Machine learning for molecular and materials science.
Nature. 2018, 559547–555.
[DOI: 10.1038/s41586-018-0337-2]

Extending the applicability of the ANI deep learning molecular potential to Sulfur and Halogens. J. Chem. Theory Comput., 2020, In press.
[DOI: 10.1021/acs.jctc.0c00121]

Deep Reinforcement Learning for De-Novo Drug Design.
Science Advances. 2018. 4 (7), eaap7885.
[DOI: 10.1126/sciadv.aap7885]

Universal fragment descriptors for predicting properties of inorganic crystals. Nature Communications 8, 2017, Article number: 15679.
[DOI: 10.1038/ncomms15679]