| Molecular Graph Convolutions: Moving Beyond Fingerprints S Kearnes, K McCloskey, M Berndl, V Pande, P Riley Journal of Computer-Aided Molecular Design 30 (8), 595-608, 2016 | 1878 | 2016 |
| Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds N Thomas, T Smidt, S Kearnes, L Yang, L Li, K Kohlhoff, P Riley arXiv preprint arXiv:1802.08219, 2018 | 1110 | 2018 |
| Prediction errors of molecular machine learning models lower than hybrid DFT error FA Faber, L Hutchison, B Huang, J Gilmer, SS Schoenholz, GE Dahl, ... Journal of chemical theory and computation 13 (11), 5255-5264, 2017 | 741* | 2017 |
| Optimization of molecules via deep reinforcement learning Z Zhou, S Kearnes, L Li, RN Zare, P Riley Scientific reports 9 (1), 1-10, 2019 | 698 | 2019 |
| Massively multitask networks for drug discovery B Ramsundar, S Kearnes, P Riley, D Webster, D Konerding, V Pande arXiv preprint arXiv:1502.02072, 2015 | 631 | 2015 |
| The open reaction database SM Kearnes, MR Maser, M Wleklinski, A Kast, AG Doyle, SD Dreher, ... Journal of the American Chemical Society 143 (45), 18820-18826, 2021 | 230 | 2021 |
| Machine learning on DNA-encoded libraries: a new paradigm for hit finding K McCloskey, EA Sigel, S Kearnes, L Xue, X Tian, D Moccia, D Gikunju, ... Journal of Medicinal Chemistry 63 (16), 8857-8866, 2020 | 145 | 2020 |
| Modeling industrial ADMET data with multitask networks S Kearnes, B Goldman, V Pande arXiv preprint arXiv:1606.08793, 2016 | 81 | 2016 |
| Towards understanding retrosynthesis by energy-based models R Sun, H Dai, L Li, S Kearnes, B Dai Advances in Neural Information Processing Systems 34, 10186-10194, 2021 | 71* | 2021 |
| Artificial intelligence in drug discovery: into the great wide open J Bajorath, S Kearnes, WP Walters, NA Meanwell, GI Georg, S Wang Journal of medicinal chemistry 63 (16), 8651-8652, 2020 | 66 | 2020 |
| Osprey: Hyperparameter optimization for machine learning RT McGibbon, CX Hernández, MP Harrigan, S Kearnes, MM Sultan, ... J. Open Source Software 1 (5), 00034, 2016 | 46 | 2016 |
| Defining levels of automated chemical design B Goldman, S Kearnes, T Kramer, P Riley, WP Walters Journal of medicinal chemistry 65 (10), 7073-7087, 2022 | 38 | 2022 |
| ROCS-Derived Features for Virtual Screening S Kearnes, V Pande Journal of Computer-Aided Molecular Design 30 (8), 609-617, 2016 | 37 | 2016 |
| Pursuing a prospective perspective S Kearnes Trends in Chemistry 3 (2), 77-79, 2021 | 28 | 2021 |
| Decoding molecular graph embeddings with reinforcement learning S Kearnes, L Li, P Riley arXiv preprint arXiv:1904.08915, 2019 | 20 | 2019 |
| Data sharing in chemistry: lessons learned and a case for mandating structured reaction data R Mercado, SM Kearnes, CW Coley Journal of Chemical Information and Modeling 63 (14), 4253-4265, 2023 | 19 | 2023 |
| The future is now: artificial intelligence in drug discovery J Bajorath, S Kearnes, WP Walters, GI Georg, S Wang Journal of medicinal chemistry 62 (11), 5249-5249, 2019 | 7 | 2019 |
| A targeted computational screen of the SWEETLEAD database reveals FDA-approved compounds with anti-dengue viral activity J Moshiri, DA Constant, B Liu, R Mateo, S Kearnes, P Novick, R Prasad, ... bioRxiv, 2020 | 6 | 2020 |
| SCISSORS: Practical Considerations SM Kearnes, IS Haque, VS Pande Journal of chemical information and modeling 54 (1), 5-15, 2013 | 6 | 2013 |
| A Target Class Ligandability Evaluation of WD40 Repeat-Containing Proteins S Ackloo, F Li, M Szewczyk, A Seitova, P Loppnau, H Zeng, J Xu, ... Journal of Medicinal Chemistry 68 (2), 1092-1112, 2024 | 2 | 2024 |
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