Olexandr Isayev

According to our database, Olexandr Isayev authored at least 23 papers between 2007 and 2024.

Collaborative distances:

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

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

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