World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
50
Citations
18912
World Ranking
3992
National Ranking
1152

Overview

Olexandr Isayev is affiliated with the University of North Carolina at Chapel Hill in the United States. Their research spans multiple domains including Materials Science, Computer Science, and Biochemistry, Genetics, and Molecular Biology. The primary subfields of study encompass Materials Chemistry, Computational Theory and Mathematics, Molecular Biology, Physical and Theoretical Chemistry, and Electrical and Electronic Engineering.

The main topics covered by their work include Computational Drug Discovery Methods, Machine Learning in Materials Science, Protein Structure and Dynamics, Various Chemistry Research Topics, Chemical Synthesis and Analysis, Catalysis and Oxidation Reactions, and RNA and protein synthesis mechanisms.

Isayev has contributed to several scientific papers across respected journals. Notable recent publications include:

  • "QSAR without borders" (2020) in Chemical Society Reviews
  • "Best practices in machine learning for chemistry" (2021) in Nature Chemistry
  • "Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens" (2020) in Journal of Chemical Theory and Computation
  • "TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials" (2020) in Journal of Chemical Information and Modeling
  • "Generative Models as an Emerging Paradigm in the Chemical Sciences" (2023) in Journal of the American Chemical Society

Their frequent collaborators include Adrián E. Roitberg, R.I. Zubatyuk, Filipp Gusev, Artem Cherkasov, and Justin S. Smith. Across their career, Isayev has published extensively in venues such as UNC Libraries, Journal of Chemical Information and Modeling, arXiv (Cornell University), Chemical Science, and Journal of Chemical Theory and Computation.

Isayev's work involves integrating machine learning techniques with chemistry and materials science to advance computational models for molecular simulations and drug discovery. This interdisciplinary approach reflects a combination of chemical theory, computer science methodologies, and biological sciences.

Best Publications

  • Machine learning for molecular and materials science.

    Keith T. Butler;Daniel W. Davies;Hugh Cartwright;Olexandr Isayev

  • ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

    Justin S Smith;Olexandr Isayev;Adrian E Roitberg

  • Deep reinforcement learning for de novo drug design

    Mariya Popova;Mariya Popova;Mariya Popova;Olexandr Isayev;Alexander E Tropsha

  • Less is more: Sampling chemical space with active learning

    Justin Steven Smith;Benjamin Tyler Nebgen;Nicholas Edward Lubbers;Olexandr Isayev

  • QSAR without borders

    Eugene N. Muratov;Eugene N. Muratov;Jürgen Bajorath;Robert P. Sheridan;Igor V. Tetko

  • Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning.

    Justin S. Smith;Justin S. Smith;Benjamin T. Nebgen;Roman Zubatyuk;Roman Zubatyuk;Nicholas Lubbers

  • Universal fragment descriptors for predicting properties of inorganic crystals

    Olexandr Isayev;Corey Oses;Cormac Toher;Eric Gossett

  • Best practices in machine learning for chemistry.

    Nongnuch Artrith;Keith T. Butler;François Xavier Coudert;Seungwu Han

  • Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints

    Olexandr Isayev;Denis Fourches;Eugene N. Muratov;Corey Oses

  • Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens.

    Christian Devereux;Justin S. Smith;Kate K. Davis;Kipton Barros

  • ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

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

  • 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

  • Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network.

    Roman Zubatyuk;Roman Zubatyuk;Roman Zubatyuk;Justin S. Smith;Jerzy Leszczynski;Olexandr Isayev

  • The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

    Justin S. Smith;Roman Zubatyuk;Roman Zubatyuk;Benjamin Nebgen;Nicholas Lubbers

  • Materials Cartography: Representing and Mining Material Space Using Structural and Electronic Fingerprints

    Olexandr Isayev;Denis Fourches;Eugene N. Muratov;Corey Oses

  • A critical overview of computational approaches employed for COVID-19 drug discovery.

    Eugene N Muratov;Rommie Amaro;Carolina H Andrade;Nathan Brown

  • Ab initio molecular dynamics study on the initial chemical events in nitramines: thermal decomposition of CL-20.

    Olexandr Isayev;Leonid Gorb;Mo Qasim;Jerzy Leszczynski

  • Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial Intelligence.

    Tetiana Zubatiuk;Olexandr Isayev

  • Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis.

    Marcus Reis;Filipp Gusev;Nicholas G Taylor;Sang Hun Chung

  • Discovering a Transferable Charge Assignment Model Using Machine Learning.

    Andrew E Sifain;Andrew E Sifain;Nicholas Lubbers;Benjamin T Nebgen;Justin S Smith;Justin S Smith

  • Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World

    Farshad Firouzi;Bahar Farahani;Mahmoud Daneshmand;Kathy Grise

  • Effect of solvation on the vertical ionization energy of thymine: from microhydration to bulk.

    Debashree Ghosh;Olexandr Isayev;Lyudmila V. Slipchenko;Anna I. Krylov

  • Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks.

    Benjamin Nebgen;Nicholas Lubbers;Justin S. Smith;Justin S. Smith;Andrew E. Sifain;Andrew E. Sifain

  • MolecularRNN: Generating realistic molecular graphs with optimized properties.

    Mariya Popova;Mykhailo Shvets;Junier Oliva;Olexandr Isayev

Frequent Co-Authors

Adrian E. Roitberg
Adrian E. Roitberg University of Florida
Jerzy Leszczynski
Jerzy Leszczynski Jackson State University
Leonid Gorb
Leonid Gorb Jackson State University
Alexander Tropsha
Alexander Tropsha University of North Carolina at Chapel Hill
David A. Winkler
David A. Winkler La Trobe University
Stefano Curtarolo
Stefano Curtarolo Duke University
Sergei Tretiak
Sergei Tretiak Los Alamos National Laboratory
Joseph G. Shapter
Joseph G. Shapter University of Queensland
Eugene N. Muratov
Eugene N. Muratov University of North Carolina at Chapel Hill
Aron Walsh
Aron Walsh Imperial College London

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