| 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 | 752 | 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 | 687 | 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 | 379 | 2017 |
| Massively multitask networks for drug discovery B Ramsundar, S Kearnes, P Riley, D Webster, D Konerding, V Pande arXiv preprint arXiv:1502.02072, 2015 | 376 | 2015 |
| Non-volatile key-value store N Talagala, S Sundararaman, B Ramsundar, A Batwara US Patent 9,075,710, 2015 | 273 | 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 | 199 | 2017 |
| Conditional iteration for a non-volatile device B Ramsundar, N Talagala, S Sundararaman US Patent 9,519,575, 2016 | 131 | 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 | 126 | 2017 |
| Atomic convolutional networks for predicting protein-ligand binding affinity J Gomes, B Ramsundar, EN Feinberg, VS Pande arXiv preprint arXiv:1703.10603, 2017 | 122 | 2017 |
| 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 | 114 | 2018 |
| 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 | 113 | 2019 |
| 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 | 85 | 2016 |
| TensorFlow for deep learning: from linear regression to reinforcement learning B Ramsundar, RB Zadeh " O'Reilly Media, Inc.", 2018 | 59 | 2018 |
| {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 | 59 | 2014 |
| Understanding protein dynamics with L1-regularized reversible hidden Markov models R McGibbon, B Ramsundar, M Sultan, G Kiss, V Pande International Conference on Machine Learning, 1197-1205, 2014 | 26 | 2014 |
| Solving the RNA design problem with reinforcement learning P Eastman, J Shi, B Ramsundar, VS Pande PLoS computational biology 14 (6), e1006176, 2018 | 14 | 2018 |
| 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 | 13 | 2020 |
| TensorFlow for Deep Learning RZ Bosagh, B Ramsundar O'Reilly Media, Incorporated, 2018 | 12 | 2018 |
| The extended parameter filter YB Erol, L Li, B Ramsundar, R Stuart International Conference on Machine Learning, 1103-1111, 2013 | 11 | 2013 |
| Dynamic scaled sampling for deterministic constraints L Li, B Ramsundar, S Russell Artificial Intelligence and Statistics, 397-405, 2013 | 7 | 2013 |
Vijay PandeAndreessen Horowitz, Stanford UniversityVerified email at stanford.edu
