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Olexandr Isayev

Active 2006–2026

98
Papers
18,254
Citations
49
h-index
73
i10-index

Citations

Citations per year for Olexandr Isayev1911: 1 citations1967: 1 citations1985: 1 citations1992: 2 citations1996: 1 citations1998: 2 citations1999: 1 citations2004: 3 citations2007: 3 citations2008: 4 citations2009: 3 citations2010: 2 citations2011: 8 citations2012: 5 citations2013: 6 citations2014: 6 citations2015: 2 citations2016: 13 citations2017: 36 citations2018: 177 citations2019: 479 citations2020: 809 citations2021: 949 citations2022: 945 citations2023: 1,114 citations2024: 1,370 citations2025: 1,190 citations2026: 171 citations1912–1966: no citations, so these years are not shown1968–1984: no citations, so these years are not shown1986–1991: no citations, so these years are not shown1993–1995: no citations, so these years are not shown1997: no citations, so this year is not shown2000–2003: no citations, so these years are not shown2005–2006: no citations, so these years are not shown

Citation sources

Countries

World map of the countries and regions citing this authorUnited States: 1,836 citing papers, 24.1% of this breakdownChina: 1,062 citing papers, 13.9% of this breakdownUnited Kingdom: 518 citing papers, 6.8% of this breakdownGermany: 463 citing papers, 6.1% of this breakdownIndia: 288 citing papers, 3.8% of this breakdownSwitzerland: 262 citing papers, 3.4% of this breakdownSouth Korea: 228 citing papers, 3% of this breakdownCanada: 227 citing papers, 3% of this breakdownJapan: 208 citing papers, 2.7% of this breakdownFrance: 165 citing papers, 2.2% of this breakdownSpain: 154 citing papers, 2% of this breakdownItaly: 153 citing papers, 2% of this breakdown
0%24.1%Other 27%

Institutions

Fields

  • Materials Science38.1%
  • Computer Science35.2%
  • Biochemistry, Genetics and Molecular Biology8%
  • Engineering5%
  • Medicine4.2%
  • Physics and Astronomy2.4%
  • Other7.1%

Topics

  • Computational Drug Discovery Methods19.5%
  • Machine Learning in Materials Science19%
  • Protein Structure and Dynamics6.4%
  • X-ray Diffraction in Crystallography1.9%
  • Metabolomics and Mass Spectrometry Studies1.2%
  • Chemical Synthesis and Analysis1%
  • Other51%

Coauthors

All papers

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  1. Machine learning for molecular and materials science

    Authors: Keith T. Butler, Daniel W. Davies, Hugh Cartwright, Olexandr Isayev, Aron Walsh - Nature 2018 cited by 4,786

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

    Authors: Justin S. Smith, Olexandr Isayev, Adrián E. Roitberg - Chemical Science 2017 cited by 2,028

  3. Deep Reinforcement Learning for De-Novo Drug Design

    Authors: Mariya Popova, Olexandr Isayev, Alexander Tropsha - Science Advances 2018 cited by 1,149

  4. QSAR without borders

    Authors: Eugene Muratov, Jürgen Bajorath, Robert P. Sheridan, Igor V. Tetko, Dmitry Filimonov, Vladimir Poroikov, Tudor I. Oprea, Igor I. Baskin, Alexandre Varnek, Adrián E. Roitberg, Olexandr Isayev, Stefano Curtalolo, Denis Fourches, Yoram Cohen, Alán Aspuru‐Guzik, David A. Winkler, Dimitris K. Agrafiotis, Artem Cherkasov, Alexander Tropsha - Chemical Society Reviews 2020 cited by 852

  5. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR

    Authors: Alexander Tropsha, Olexandr Isayev, Alexandre Varnek, Gisbert Schneider, Artem Cherkasov - Nature Reviews Drug Discovery 2023 cited by 336

  6. Less is more: sampling chemical space with active learning

    Authors: Justin S. Smith, Ben Nebgen, Nicholas Lubbers, Olexandr Isayev, Adrian E. Roitberg - The Journal of Chemical Physics 2018 cited by 846

  7. Generative Models as an Emerging Paradigm in the Chemical Sciences

    Authors: Dylan M. Anstine, Olexandr Isayev - Journal of the American Chemical Society 2023 cited by 302

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

    Authors: Christian Devereux, Justin S. Smith, Kate Huddleston, Kipton Barros, R.I. Zubatyuk, Olexandr Isayev, Adrián E. Roitberg - Journal of Chemical Theory and Computation 2020 cited by 403

  9. The transformational role of GPU computing and deep learning in drug discovery

    Authors: Mohit Pandey, Michael Fernández, Francesco Gentile, Olexandr Isayev, Alexander Tropsha, Abraham C. Stern, Artem Cherkasov - Nature Machine Intelligence, Nat. Mach. Intell. 2022 cited by 260

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

    Authors: Justin S. Smith, Benjamin Nebgen, R.I. Zubatyuk, Nicholas Lubbers, Christian Devereux, Kipton Barros, Sergei Tretiak, Olexandr Isayev, Adrián E. Roitberg - Nature Communications 2018 cited by 655

  11. TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials

    Authors: Xiang Gao, Farhad Ramezanghorbani, Olexandr Isayev, Justin S. Smith, Adrian E. Roitberg - Journal of Chemical Information and Modeling, J. Chem. Inf. Model. 2020 cited by 324

  12. The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules

    Authors: Justin S. Smith, R.I. Zubatyuk, Benjamin Nebgen, Nicholas Lubbers, Kipton Barros, Adrián E. Roitberg, Olexandr Isayev, Sergei Tretiak - Scientific Data 2020 cited by 275

  13. Best practices in machine learning for chemistry

    Authors: Nongnuch Artrith, Keith T. Butler, François‐Xavier Coudert, Seungwu Han, Olexandr Isayev, Anubhav Jain, Aron Walsh - Nature Chemistry 2021 cited by 477

  14. Machine Learning Interatomic Potentials and Long-Range Physics

    Authors: Dylan M. Anstine, Olexandr Isayev - The Journal of Physical Chemistry A 2023 cited by 210

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

    Authors: R.I. Zubatyuk, Justin S. Smith, Jerzy Leszczyński, Olexandr Isayev - Science Advances 2019 cited by 300

  16. Simulation Intelligence: Towards a New Generation of Scientific Methods

    Authors: Alexander Lavin, David C. Krakauer, Héctor Zenil, Justin Gottschlich, Tim Mattson, Johann Brehmer, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atılım Güneş Baydin, Carina Prunkl, Brooks Paige, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob H. Macke, K. Cranmer, Jiaxin Zhang, Haruko Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer - CoRR 2021 cited by 93

  17. Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds

    Authors: Maria Korshunova, Niles Huang, Stephen J. Capuzzi, Dmytro S. Radchenko, Olena Savych, Yuriy S. Moroz, Carrow I. Wells, Timothy M. Willson, Alexander Tropsha, Olexandr Isayev - Communications Chemistry 2022 cited by 104

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

    Authors: Justin S. Smith, Olexandr Isayev, Adrián E. Roitberg - Scientific Data 2017 cited by 333

  19. Universal fragment descriptors for predicting properties of inorganic crystals

    Authors: Olexandr Isayev, Corey Oses, Cormac Toher, Eric Gossett, Stefano Curtarolo, Alexander Tropsha - Nature Communications 2017 cited by 665

  20. Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

    Authors: Shuhao Zhang, Małgorzata Z. Makoś, Ryan B. Jadrich, Elfi Kraka, Kipton Barros, Benjamin Nebgen, Sergei Tretiak, Olexandr Isayev, Nicholas Lubbers, Richard A. Messerly, Justin S. Smith - Nature Chemistry 2024 cited by 121

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

    Authors: Tetiana Zubatiuk, Olexandr Isayev - Accounts of Chemical Research 2021 cited by 166

  22. Extending machine learning beyond interatomic potentials for predicting molecular properties

    Authors: Nikita Fedik, R.I. Zubatyuk, Maksim Kulichenko, Nicholas Lubbers, Justin S. Smith, Benjamin Nebgen, Richard A. Messerly, Ying Wai Li, Alexander I. Boldyrev, Kipton Barros, Olexandr Isayev, Sergei Tretiak - Nature Reviews Chemistry 2022 cited by 154

  23. MolecularRNN: Generating realistic molecular graphs with optimized properties

    Authors: Mariya Popova, Mykhailo Shvets, Junier Oliva, Olexandr Isayev - arXiv (Cornell University), CoRR 2019 cited by 107

  24. Machine Learning of Reactive Potentials

    Authors: Yinuo Yang, Shuhao Zhang, Kavindri Ranasinghe, Olexandr Isayev, Adrián E. Roitberg - Annual Review of Physical Chemistry 2024 cited by 71