Olexandr Isayev

According to our database, Olexandr Isayev authored at least 33 papers between 2007 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS).

Andrew Ferguson, Marisa Lafleur, Lars Ruthotto, Jesse Thaler, Yuan-Sen Ting, Pratyush Tiwary, Soledad Villar, E. Paulo Alves, Jeremy Avigad, Simon Billinge, Camille Bilodeau, Keith Brown, Emmanuel J. Candès, Arghya Chattopadhyay, Bingqing Cheng, Jonathan Clausen, Connor W. Coley, Andrew J. Connolly, Fred Daum, Sijia Dong, Chrisy Xiyu Du, Cora Dvorkin, Cristiano Fanelli, Eric B. Ford, Luis Manuel Frutos, Nicolás García Trillos, Cecilia Garraffo, Robert Ghrist, Rafael Gómez-Bombarelli, Gianluca Guadagni, Sreelekha Guggilam, Sergei Gukov, Juan B. Gutiérrez, Salman Habib, Johannes Hachmann, Boris Hanin, Philip C. Harris, Murray Holland, Elizabeth Holm, Hsin-Yuan Huang, Shih-Chieh Hsu, Nick Jackson, Olexandr Isayev, Heng Ji, Aggelos K. Katsaggelos, Jeremy Kepner, Yannis G. Kevrekidis, Michelle P. Kuchera, J. Nathan Kutz, Branislava Lalic, Ann Lee, Matt LeBlanc, Josiah Lim, Rebecca Lindsey, Yongmin Liu, Peter Y. Lu, Sudhir Malik, Vuk Mandic, Vidya B. Manian, Emeka P. Mazi, Pankaj Mehta, Peter Melchior, Brice Ménard, Jennifer Ngadiuba, Stella Offner, Elsa Olivetti, Shyue Ping Ong, Christopher Rackauckas, Philippe Rigollet, Chad Risko, Philip Romero, Grant M. Rotskoff, Brett Savoie, Uros Seljak, David Shih, Gary Shiu, Dima Shlyakhtenko, Eva Silverstein, Taylor Sparks, Thomas Strohmer, Christopher Stubbs, Stephen Thomas, Suriyanarayanan Vaikuntanathan, René Vidal, Francisco Villaescusa-Navarro, Gregory Voth, Benjamin Wandelt, Rachel Ward, Melanie Weber, Risa Wechsler, Stephen Whitelam, Olaf Wiest, Mike Williams, Zhuoran Yang, Yaroslava G. Yingling, Bin Yu, Shuwen Yue, Ann Zabludoff, Huimin Zhao, Tong Zhang

CoRR, September, 2025

MolErr2Fix: Benchmarking LLM Trustworthiness in Chemistry via Modular Error Detection, Localization, Explanation, and Revision.

Yuyang Wu, Jinhui Ye, Shuhao Zhang, Lu Dai, Yonatan Bisk, Olexandr Isayev

CoRR, September, 2025

Anticipating the Selectivity of Cyclization Reaction Pathways with Neural Network Potentials.

Nicholas Casetti, Dylan Anstine, Olexandr Isayev, Connor W. Coley

CoRR, July, 2025

Applications of Modular Co-Design for De Novo 3D Molecule Generation.

Danny Reidenbach, Filipp Nikitin, Olexandr Isayev, Saee Paliwal

CoRR, May, 2025

GEOM-Drugs Revisited: Toward More Chemically Accurate Benchmarks for 3D Molecule Generation.

Filipp Nikitin, Ian Dunn, David Ryan Koes, Olexandr Isayev

CoRR, May, 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, Gábor Csányi, Ekin Dogus Cubuk, Bowen Deng, Ralf Drautz, Xiang Fu, Jonathan Godwin, Vasant G. Honavar, Olexandr Isayev, Anders Johansson, Boris Kozinsky, Stefano Martiniani, Shyue Ping Ong, Igor Poltavsky, K. J. Schmidt, So Takamoto, Aidan Thompson, Julia Westermayr, Brandon M. Wood

CoRR, March, 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

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, Levon Halabelian, Scott Houliston, Rachel J. Harding, Ashley Hutchinson, Peter Loppnau, Sumera Perveen, Almagul Seitova, Hong Zeng, Matthieu Schapira, Artem Cherkasov, Olexandr Isayev, Maria G. Kurnikova

J. Chem. Inf. Model., 2025

All That Glitters Is Not Gold: Importance of Rigorous Evaluation of Proteochemometric Models.

Polina Avdiunina, Shamieraah Jamal, Filipp Gusev, Olexandr Isayev

J. Chem. Inf. Model., 2025

2024

Editorial: Machine Learning in Materials Science.

Kenneth M. Merz Jr., Yee-Siew Choong, Zoe Cournia, Olexandr Isayev, Thereza A. Soares, Guo-Wei Wei, Feng Zhu

J. Chem. Inf. Model., 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, Irene Chau, Yu Chen, Artem Cherkasov, Dennis Della Corte, Katrin Denzinger, Aiping Dong, Sorin Draga, Ian Dunn, Kristina Edfeldt, Aled M. Edwards, Merveille K. I. Eguida, Paul Eisenhuth, Lukas Friedrich, Alexander Fuerll, Spencer S. Gardiner, Francesco Gentile, Pegah Ghiabi, Elisa Gibson, Marta Glavatskikh, Christoph Gorgulla, Judith Guenther, Anders Gunnarsson, Filipp Gusev, Evgeny Gutkin, Levon Halabelian, Rachel J. Harding, Alexander Hillisch, Laurent Hoffer, Anders Hogner, Scott Houliston, John J. Irwin, Olexandr Isayev, Aleksandra Ivanova, Célien Jacquemard, Austin J. Jarrett, Jan H. Jensen, Dmitri Kireev, Julian Kleber, S. Benjamin Koby, David Koes, Ashutosh Kumar, Maria G. Kurnikova, Alina Kutlushina, Uta F. Lessel, Fabian Liessmann, Sijie Liu, Wei Lu, Jens Meiler, Akhila Mettu, Guzel Minibaeva, Rocco Moretti, Connor J. Morris, Chamali Narangoda, Theresa Noonan, Leon Obendorf, Szymon Pach, Amit Pandit, Sumera Perveen, Gennady Poda, Pavel G. Polishchuk, Kristina Puls, Vera Pütter, Didier Rognan, Dylan Roskams-Edris, Christina E. M. Schindler, François Sindt, Vojtech Spiwok, Casper Steinmann, Rick L. Stevens, Valerij Talagayev, Damon Tingey, Oanh Vu, W. Patrick Walters, Xiaowen Wang, Zhenyu Wang, Gerhard Wolber, Clemens Alexander Wolf, Lars Wortmann, Hong Zeng, Carlos A. Zepeda, Kam Y. J. Zhang, Jixian Zhang, Shuangjia Zheng, Matthieu Schapira

J. Chem. Inf. Model., 2024

Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks.

Yanqiao Zhu, Jeehyun Hwang, Keir Adams, Zhen Liu, Bozhao Nan, Brock Stenfors, Yuanqi Du, Jatin Chauhan, Olaf Wiest, Olexandr Isayev, Connor W. Coley, Yizhou Sun, Wei Wang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Uncertainty-Aware Yield Prediction with Multimodal Molecular Features.

Jiayuan Chen, Kehan Guo, Zhen Liu, Olexandr Isayev, Xiangliang Zhang

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

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., January, 2023

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, Yangtao Chen, Lina Zhang, Shuang Zhang, Arif Ullah, Quanhao Zhang, Yanchi Ou

CoRR, 2023

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

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

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

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

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, Eduardo A. Soares, Po-Shen Loh, Zeynab Talebpour, Reza Moradi, Mohsen Goodarzi, Haleh Ashraf, Mohammad Talebpour, Alireza Talebpour, Luca Romeo, Rupam Das, Hadi Heidari, Dana K. Pasquale, James Moody, Chris Woods, Erich S. Huang, Payam M. Barnaghi, Majid Sarrafzadeh, Ron C. Li, Kristen L. Beck, Olexandr Isayev, NakMyoung Sung, Alan Luo

IEEE Internet Things J., 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, Erik Peterson, Peter L. McMahon, Jakob H. Macke, Kyle Cranmer, Jiaxin Zhang, Haruko M. Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer

CoRR, 2021

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

2020

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

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

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

MolecularRNN: Generating realistic molecular graphs with optimized properties.

Mariya Popova, Mykhailo Shvets, Junier Oliva, Olexandr Isayev

CoRR, 2019

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

2018

Less is more: sampling chemical space with active learning.

Justin S. Smith, Ben Nebgen, Nicholas Lubbers, Olexandr Isayev, Adrian E. Roitberg

CoRR, 2018

2017

Deep Reinforcement Learning for De-Novo Drug Design.

Mariya Popova, Olexandr Isayev, Alexander Tropsha

CoRR, 2017

ANI-1: A data set of 20M off-equilibrium DFT calculations for organic molecules.

Justin S. Smith, Olexandr Isayev, Adrian E. Roitberg

CoRR, 2017

2015

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

2011

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

2007

Theoretical calculations: Can Gibbs free energy for intermolecular complexes be predicted efficiently and accurately?

Olexandr Isayev, Leonid Gorb, Jerzy Leszczynski

J. Comput. Chem., 2007