| Machine learning for molecular and materials science KT Butler, DW Davies, H Cartwright, O Isayev, A Walsh Nature 559 (7715), 547-555, 2018 | 3860 | 2018 |
| ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost JS Smith, O Isayev, AE Roitberg Chemical science 8 (4), 3192-3203, 2017 | 1898 | 2017 |
| Deep reinforcement learning for de novo drug design M Popova, O Isayev, A Tropsha Science advances 4 (7), eaap7885, 2018 | 1310 | 2018 |
| Less is more: Sampling chemical space with active learning JS Smith, B Nebgen, N Lubbers, O Isayev, AE Roitberg The Journal of chemical physics 148 (24), 2018 | 788 | 2018 |
| QSAR without borders EN Muratov, J Bajorath, RP Sheridan, IV Tetko, D Filimonov, V Poroikov, ... Chemical Society Reviews 49 (11), 3525-3564, 2020 | 770 | 2020 |
| Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning JS Smith, BT Nebgen, R Zubatyuk, N Lubbers, C Devereux, K Barros, ... Nature communications 10 (1), 2903, 2019 | 714 | 2019 |
| Universal fragment descriptors for predicting properties of inorganic crystals O Isayev, C Oses, C Toher, E Gossett, S Curtarolo, A Tropsha Nature communications 8 (1), 15679, 2017 | 670 | 2017 |
| Best practices in machine learning for chemistry N Artrith, KT Butler, FX Coudert, S Han, O Isayev, A Jain, A Walsh Nature chemistry 13 (6), 505-508, 2021 | 404 | 2021 |
| Extending the applicability of the ANI deep learning molecular potential to sulfur and halogens C Devereux, JS Smith, KK Huddleston, K Barros, R Zubatyuk, O Isayev, ... Journal of chemical theory and computation 16 (7), 4192-4202, 2020 | 342 | 2020 |
| ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules JS Smith, O Isayev, AE Roitberg Scientific data 4 (1), 1-8, 2017 | 325 | 2017 |
| Materials cartography: representing and mining materials space using structural and electronic fingerprints O Isayev, D Fourches, EN Muratov, C Oses, K Rasch, A Tropsha, ... Chemistry of Materials 27 (3), 735-743, 2015 | 325 | 2015 |
| TorchANI: a free and open source PyTorch-based deep learning implementation of the ANI neural network potentials X Gao, F Ramezanghorbani, O Isayev, JS Smith, AE Roitberg Journal of chemical information and modeling 60 (7), 3408-3415, 2020 | 295 | 2020 |
| The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules JS Smith, R Zubatyuk, B Nebgen, N Lubbers, K Barros, AE Roitberg, ... Scientific data 7 (1), 134, 2020 | 251 | 2020 |
| Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network R Zubatyuk, JS Smith, J Leszczynski, O Isayev Science advances 5 (8), eaav6490, 2019 | 243 | 2019 |
| The transformational role of GPU computing and deep learning in drug discovery M Pandey, M Fernandez, F Gentile, O Isayev, A Tropsha, AC Stern, ... Nature Machine Intelligence 4 (3), 211-221, 2022 | 216 | 2022 |
| Generative models as an emerging paradigm in the chemical sciences DM Anstine, O Isayev Journal of the American Chemical Society 145 (16), 8736-8750, 2023 | 190 | 2023 |
| A critical overview of computational approaches employed for COVID-19 drug discovery EN Muratov, R Amaro, CH Andrade, N Brown, S Ekins, D Fourches, ... Chemical Society Reviews 50 (16), 9121-9151, 2021 | 190 | 2021 |
| MolecularRNN: Generating realistic molecular graphs with optimized properties M Popova, M Shvets, J Oliva, O Isayev arXiv preprint arXiv:1905.13372, 2019 | 188 | 2019 |
| Roadmap on machine learning in electronic structure HJ Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ... Electronic Structure 4 (2), 023004, 2022 | 154 | 2022 |
| Development of multimodal machine learning potentials: toward a physics-aware artificial intelligence T Zubatiuk, O Isayev Accounts of Chemical Research 54 (7), 1575-1585, 2021 | 145 | 2021 |
Alexander TropshaProfessor at UNC-Chapel HillE-mail confirmado em unc.edu
