| A guide to deep learning in healthcare A Esteva, A Robicquet, B Ramsundar, V Kuleshov, M DePristo, K Chou, ... Nature medicine 25 (1), 24-29, 2019 | 3252 | 2019 |
| MoleculeNet: a benchmark for molecular machine learning Z Wu, B Ramsundar, EN Feinberg, J Gomes, C Geniesse, AS Pappu, ... Chemical science 9 (2), 513-530, 2018 | 2701 | 2018 |
| Low data drug discovery with one-shot learning H Altae-Tran, B Ramsundar, AS Pappu, V Pande ACS central science 3 (4), 283-293, 2017 | 881 | 2017 |
| Deep learning for the life sciences: applying deep learning to genomics, microscopy, drug discovery, and more B Ramsundar, P Eastman, P Walters, V Pande " O'Reilly Media, Inc.", 2019 | 666* | 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 | 605 | 2015 |
| Scientific discovery in the age of artificial intelligence H Wang, T Fu, Y Du, W Gao, K Huang, Z Liu, P Chandak, S Liu, ... Nature 620 (7972), 47-60, 2023 | 595 | 2023 |
| Non-volatile key-value store N Talagala, S Sundararaman, B Ramsundar, A Batwara US Patent 9,075,710, 2015 | 526 | 2015 |
| Retrosynthetic reaction prediction using neural sequence-to-sequence models B Liu, B Ramsundar, P Kawthekar, J Shi, J Gomes, Q Luu Nguyen, S Ho, ... ACS central science 3 (10), 1103-1113, 2017 | 514 | 2017 |
| ChemBERTa: large-scale self-supervised pretraining for molecular property prediction S Chithrananda, G Grand, B Ramsundar arXiv preprint arXiv:2010.09885, 2020 | 441 | 2020 |
| PotentialNet for molecular property prediction EN Feinberg, D Sur, Z Wu, BE Husic, H Mai, Y Li, S Sun, J Yang, ... ACS central science 4 (11), 1520-1530, 2018 | 381 | 2018 |
| Conditional iteration for a non-volatile device B Ramsundar, N Talagala, S Sundararaman US Patent 9,519,575, 2016 | 292 | 2016 |
| Computational modeling of β-secretase 1 (BACE-1) inhibitors using ligand based approaches G Subramanian, B Ramsundar, V Pande, RA Denny Journal of chemical information and modeling 56 (10), 1936-1949, 2016 | 289 | 2016 |
| Is multitask deep learning practical for pharma? B Ramsundar, B Liu, Z Wu, A Verras, M Tudor, RP Sheridan, V Pande Journal of chemical information and modeling 57 (8), 2068-2076, 2017 | 273 | 2017 |
| Atomic convolutional networks for predicting protein-ligand binding affinity J Gomes, B Ramsundar, EN Feinberg, VS Pande arXiv preprint arXiv:1703.10603, 2017 | 250 | 2017 |
| TensorFlow for deep learning: from linear regression to reinforcement learning B Ramsundar, RB Zadeh " O'Reilly Media, Inc.", 2018 | 223 | 2018 |
| Chemberta-2: Towards chemical foundation models W Ahmad, E Simon, S Chithrananda, G Grand, B Ramsundar arXiv preprint arXiv:2209.01712, 2022 | 113 | 2022 |
| AMPL: a data-driven modeling pipeline for drug discovery AJ Minnich, K McLoughlin, M Tse, J Deng, A Weber, N Murad, BD Madej, ... Journal of chemical information and modeling 60 (4), 1955-1968, 2020 | 79 | 2020 |
| {NVMKV}: A Scalable and Lightweight Flash Aware {Key-Value} Store L Marmol, S Sundararaman, N Talagala, R Rangaswami, S Devendrappa, ... 6th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage 14), 2014 | 73 | 2014 |
| ChemBERTa: Large-scale self-supervised pretraining for molecular property prediction. arXiv 2020 S Chithrananda, G Grand, B Ramsundar arXiv preprint arXiv:2010.09885 10, 2010 | 69 | 2010 |
| Machine learning abstraction N Talagala, V Sridhar, S Sundararaman, S Ghanta, L Amar, L Khermosh, ... US Patent 11,748,653, 2023 | 59 | 2023 |
Vijay PandeAndreessen Horowitz, Stanford UniversityVerified email at stanford.edu
