Olexandr Isayev

University of North Carolina at Chapel Hill — United States

Materials Science · Physical Sciences

55h-index17.8kcitations274works8.82yr avg
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Research Topics

Machine Learning in Materials Science(164), Computational Drug Discovery Methods(125), Protein Structure and Dynamics(37), Various Chemistry Research Topics(32), X-ray Diffraction in Crystallography(18)

Publications273 total

AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry
Angewandte Chemie International Edition·2025· 1 citedOpen Access
AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry
Angewandte Chemie·2025Open Access
Democratizing Reaction Kinetics through Machine Vision and Learning
2025
Proto-Yield: An Uncertainty-Aware Prototype Network for Yield Prediction in Real-world Chemical Reactions
2025Open Access
CACHE Challenge #1: Targeting the WDR Domain of LRRK2, A Parkinson’s Disease Associated Protein
UNC Libraries·2025Open Access
Machine Learning-Accelerated Screening of Hydroquinone Analogs for Proton-Coupled Electron Transfer
2025
AIQM3: Targeting Coupled-Cluster Accuracy with Semi-Empirical Speed Across Seven Main Group Elements
2025Open Access
AQuaRef: machine learning accelerated quantum refinement of protein structures
Nature Communications·2025· 7 citedOpen Access
Efficient Molecular Crystal Structure Prediction and Stability Assessment with AIMNet2 Neural Network Potentials
Crystal Growth & Design·2025· 3 citedOpen Access
Fast and Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions
JACS Au·2025Open Access
Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials
Journal of Chemical Theory and Computation·2025· 2 cited
Kolmogorov-Arnold Networks in Thermoelectric Materials Design
arXiv (Cornell University)·2025Open Access
Author response for "GEOM-Drugs Revisited: Toward More Chemically Accurate Benchmarks for 3D Molecule Generation"
2025
Deep reinforcement learning for de novo drug design
UNC Libraries·2025Open Access
Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks
UNC Libraries·2025Open Access
All That Glitters Is Not Gold: Importance of Rigorous Evaluation of Proteochemometric Models
Journal of Chemical Information and Modeling·2025Open Access
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
arXiv (Cornell University)·2025Open Access
Discovering a Transferable Charge Assignment Model Using Machine Learning
UNC Libraries·2025Open Access
MolErr2Fix: Benchmarking LLM Trustworthiness in Chemistry via Modular Error Detection, Localization, Explanation, and Revision
arXiv (Cornell University)·2025Open Access
Scalable Low-Energy Molecular Conformer Generation with Quantum Mechanical Accuracy
2025Open Access
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Frequent Co-authors

Justin S. Smith(14), Adrián E. Roitberg(14), R.I. Zubatyuk(10), Benjamin Nebgen(9), Alexander Tropsha(9), Kipton Barros(9), Nicholas Lubbers(8), Sergei Tretiak(8), Dylan M. Anstine(5), David A. Winkler(4), Artem Cherkasov(4), Christian Devereux(4), Jerzy Leszczyński(4), Eugene Muratov(3), Tudor I. Oprea(3), Alexandre Varnek(3), Denis Fourches(3), Corey Oses(3), Stefano Curtarolo(3), Leonid Gorb(3)