Theoretical calculations: Can Gibbs free energy for intermolecular complexes be predicted efficiently and accurately? - Olexandr Isayev; Leonid Gorb; Jerzy Leszczynski (J. Comput. Chem., 2007)

Toward robust computational electrochemical predicting the environmental fate of organic pollutants. - Liudmyla K. Sviatenko; Olexandr Isayev; Leonid Gorb; Frances Hill; Jerzy Leszczynski (J. Comput. Chem., 2011)

Are the reduction and oxidation properties of nitrocompounds dissolved in water different from those produced when adsorbed on a silica surface? A DFT M05-2X computational study. - Liudmyla K. Sviatenko; Olexandr Isayev; Leonid Gorb; Frances C. Hill; Danuta Leszczynska; Jerzy Leszczynski (J. Comput. Chem., 2015)

ANI-1: A data set of 20M off-equilibrium DFT calculations for organic molecules. - Justin S. Smith; Olexandr Isayev; Adrian E. Roitberg (CoRR, 2017)

Deep Reinforcement Learning for De-Novo Drug Design. - Mariya Popova; Olexandr Isayev; Alexander Tropsha (CoRR, 2017)

Less is more: sampling chemical space with active learning. - Justin S. Smith; Ben Nebgen; Nicholas Lubbers; Olexandr Isayev; Adrian E. Roitberg (CoRR, 2018)

Inter-Modular Linkers play a crucial role in governing the biosynthesis of non-ribosomal peptides. - Sherif Farag; Rachel M. Bleich; Elizabeth A. Shank; Olexandr Isayev; Albert A Bowers; Alexander Tropsha (Bioinform., 2019)

MolecularRNN: Generating realistic molecular graphs with optimized properties. - Mariya Popova; Mykhailo Shvets; Junier Oliva; Olexandr Isayev (CoRR, 2019)

Impressive computational acceleration by using machine learning for 2-dimensional super-lubricant materials discovery. - Marco Fronzi; Mutaz Abu Ghazaleh; Olexandr Isayev; David A. Winkler; Joe Shapter; Michael J. Ford (CoRR, 2019)

Quantitative Structure-Price Relationship (QS$R) Modeling and the Development of Economically Feasible Drug Discovery Projects. - Michael Fernández; Fuqiang Ban; Godwin Woo; Olexandr Isayev; Carl Perez; Valery Fokin; Alexander Tropsha; Artem Cherkasov (J. Chem. Inf. Model., 2019)

TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials. - Xiang Gao; Farhad Ramezanghorbani; Olexandr Isayev; Justin S. Smith; Adrian E. Roitberg (J. Chem. Inf. Model., 2020)

Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures. - Marco Fronzi; Olexandr Isayev; David A. Winkler; Joseph George Shapter; Amanda V. Ellis; Peter C. Sherrell; Nick A. Shepelin; Alexander Corletto; Michael J. Ford (Adv. Intell. Syst., 2021)

Simulation Intelligence: Towards a New Generation of Scientific Methods. - Alexander Lavin; Hector Zenil; Brooks Paige; David Krakauer; Justin Gottschlich; Tim Mattson; Anima Anandkumar; Sanjay Choudry; Kamil Rocki; Atilim Günes Baydin; Carina Prunkl; Olexandr Isayev; et al. (CoRR, 2021)

Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World. - Farshad Firouzi; Bahareh J. Farahani; Mahmoud Daneshmand; Kathy Grise; Jaeseung Song; Roberto Saracco; Lucy Lu Wang; Kyle Lo; Plamen Angelov 001; Eduardo A. Soares 001; Po-Shen Loh; Zeynab Talebpour; et al. (IEEE Internet Things J., 2021)

OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design. - Maria Korshunova; Boris Ginsburg; Alexander Tropsha; Olexandr Isayev (J. Chem. Inf. Model., 2021)

Simulations of Pathogenic E1α Variants: Allostery and Impact on Pyruvate Dehydrogenase Complex-E1 Structure and Function. - Hatice Gokcan; Jirair K. Bedoyan; Olexandr Isayev (J. Chem. Inf. Model., 2022)

Auto3D: Automatic Generation of the Low-Energy 3D Structures with ANI Neural Network Potentials. - Zhen Liu; Tetiana Zubatiuk; Adrian E. Roitberg; Olexandr Isayev (J. Chem. Inf. Model., 2022)

The transformational role of GPU computing and deep learning in drug discovery. - Mohit Pandey; Michael Fernández; Francesco Gentile; Olexandr Isayev; Alexander Tropsha; Abraham C. Stern; Artem Cherkasov (Nat. Mach. Intell., 2022)

MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows. - Pavlo O. Dral; Fuchun Ge; Yi-Fan Hou; Peikun Zheng; Yuxinxin Chen; Mario Barbatti; Olexandr Isayev; Cheng Wang; Bao-Xin Xue; Max Pinheiro Jr; Yuming Su; Yiheng Dai; et al. (CoRR, 2023)

Active Learning Guided Drug Design Lead Optimization Based on Relative Binding Free Energy Modeling. - Filipp Gusev; Evgeny Gutkin; Maria G. Kurnikova; Olexandr Isayev (J. Chem. Inf. Model., 2023)

Uncertainty-Aware Yield Prediction with Multimodal Molecular Features. - Jiayuan Chen 003; Kehan Guo; Zhen Liu; Olexandr Isayev; Xiangliang Zhang 001 (AAAI, 2024)

Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks. - Yanqiao Zhu 001; Jeehyun Hwang; Keir Adams; Zhen Liu; Bozhao Nan; Brock Stenfors; Yuanqi Du; Jatin Chauhan; Olaf Wiest; Olexandr Isayev; Connor W. Coley; Yizhou Sun; et al. (ICLR, 2024)

CACHE Challenge #1: Targeting the WDR Domain of LRRK2, A Parkinson's Disease Associated Protein. - Fengling Li; Suzanne Ackloo; Cheryl H. Arrowsmith; Fuqiang Ban; Christopher J. Barden; Hartmut Beck; Jan Beránek; Francois Berenger; Albina Bolotokova; Guillaume Bret; Marko Breznik; Emanuele Carosati; et al. (J. Chem. Inf. Model., 2024)

Editorial: Machine Learning in Materials Science. - Kenneth M. Merz Jr.; Yee-Siew Choong; Zoe Cournia; Olexandr Isayev; Thereza A. Soares; Guo-Wei Wei 001; Feng Zhu 004 (J. Chem. Inf. Model., 2024)

Active Learning-Guided Hit Optimization for the Leucine-Rich Repeat Kinase 2 WDR Domain Based on In Silico Ligand-Binding Affinities. - Filipp Gusev; Evgeny Gutkin; Francesco Gentile; Fuqiang Ban; S. Benjamin Koby; Fengling Li; Irene Chau; Suzanne Ackloo; Cheryl H. Arrowsmith; Albina Bolotokova; Pegah Ghiabi; Elisa Gibson; et al. (J. Chem. Inf. Model., 2025)

Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry. - Shuhao Zhang; Michael Chigaev; Olexandr Isayev; Richard A. Messerly; Nicholas Lubbers (J. Chem. Inf. Model., 2025)

A practical guide to machine learning interatomic potentials - Status and future. - Ryan Jacobs; Dane Morgan; Siamak Attarian; Jun Meng; Chen Shen; Zhenghao Wu; Clare Yijia Xie; Julia H. Yang; Nongnuch Artrith; Ben Blaiszik; Gerbrand Ceder; Kamal Choudhary; et al. (CoRR, 2025)

GEOM-Drugs Revisited: Toward More Chemically Accurate Benchmarks for 3D Molecule Generation. - Filipp Nikitin; Ian Dunn; David Ryan Koes; Olexandr Isayev (CoRR, 2025)

Applications of Modular Co-Design for De Novo 3D Molecule Generation. - Danny Reidenbach; Filipp Nikitin; Olexandr Isayev; Saee Paliwal (CoRR, 2025)

Anticipating the Selectivity of Cyclization Reaction Pathways with Neural Network Potentials. - Nicholas Casetti; Dylan Anstine; Olexandr Isayev; Connor W. Coley (CoRR, 2025)