| Explanations based on the missing: Towards contrastive explanations with pertinent negatives A Dhurandhar, PY Chen, R Luss, CC Tu, P Ting, K Shanmugam, P Das Advances in neural information processing systems 31, 2018 | 649 | 2018 |
| Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction P Das, M Moll, H Stamati, LE Kavraki, C Clementi Proceedings of the National Academy of Sciences 103 (26), 9885-9890, 2006 | 370 | 2006 |
| Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations P Das, T Sercu, K Wadhawan, I Padhi, S Gehrmann, F Cipcigan, ... Nature Biomedical Engineering 5 (6), 613-623, 2021 | 234 | 2021 |
| Bridging mode connectivity in loss landscapes and adversarial robustness P Zhao, PY Chen, P Das, KN Ramamurthy, X Lin arXiv preprint arXiv:2005.00060, 2020 | 185 | 2020 |
| Aggregation of γ-crystallins associated with human cataracts via domain swapping at the C-terminal β-strands P Das, JA King, R Zhou Proceedings of the National Academy of Sciences 108 (26), 10514-10519, 2011 | 142 | 2011 |
| Balancing energy and entropy: A minimalist model for the characterization of protein folding landscapes P Das, S Matysiak, C Clementi Proceedings of the National Academy of Sciences 102 (29), 10141-10146, 2005 | 130 | 2005 |
| Large-scale chemical language representations capture molecular structure and properties J Ross, B Belgodere, V Chenthamarakshan, I Padhi, Y Mroueh, P Das Nature Machine Intelligence 4 (12), 1256-1264, 2022 | 124 | 2022 |
| Protein representation learning by geometric structure pretraining Z Zhang, M Xu, A Jamasb, V Chenthamarakshan, A Lozano, P Das, ... arXiv preprint arXiv:2203.06125, 2022 | 122 | 2022 |
| Biological sequence design with gflownets M Jain, E Bengio, A Hernandez-Garcia, J Rector-Brooks, BFP Dossou, ... International Conference on Machine Learning, 9786-9801, 2022 | 102 | 2022 |
| CogMol: Target-specific and selective drug design for COVID-19 using deep generative models V Chenthamarakshan, P Das, S Hoffman, H Strobelt, I Padhi, KW Lim, ... Advances in Neural Information Processing Systems 33, 4320-4332, 2020 | 95 | 2020 |
| Single mutation induced H3N2 hemagglutinin antibody neutralization: a free energy perturbation study R Zhou, P Das, AK Royyuru The Journal of Physical Chemistry B 112 (49), 15813-15820, 2008 | 94 | 2008 |
| Free energy simulations reveal a double mutant avian H5N1 virus hemagglutinin with altered receptor binding specificity P Das, J Li, AK Royyuru, R Zhou Journal of computational chemistry 30 (11), 1654-1663, 2009 | 82 | 2009 |
| Towards better understanding of adaptive gradient algorithms in generative adversarial nets M Liu, Y Mroueh, J Ross, W Zhang, X Cui, P Das, T Yang arXiv preprint arXiv:1912.11940, 2019 | 71 | 2019 |
| A decentralized parallel algorithm for training generative adversarial nets M Liu, W Zhang, Y Mroueh, X Cui, J Ross, T Yang, P Das Advances in Neural Information Processing Systems 33, 11056-11070, 2020 | 66 | 2020 |
| Optimizing molecules using efficient queries from property evaluations SC Hoffman, V Chenthamarakshan, K Wadhawan, PY Chen, P Das Nature Machine Intelligence 4 (1), 21-31, 2022 | 63 | 2022 |
| Urea-induced drying of carbon nanotubes suggests existence of a dry globule-like transient state during chemical denaturation of proteins P Das, R Zhou The Journal of Physical Chemistry B 114 (16), 5427-5430, 2010 | 60 | 2010 |
| Alzheimer’s protective A2T mutation changes the conformational landscape of the Aβ1–42 monomer differently than does the A2V mutation P Das, B Murray, G Belfort Biophysical Journal 108 (3), 738-747, 2015 | 59 | 2015 |
| Urea-induced drying of hydrophobic nanotubes: comparison of different urea models P Xiu, Z Yang, B Zhou, P Das, H Fang, R Zhou The Journal of Physical Chemistry B 115 (12), 2988-2994, 2011 | 57 | 2011 |
| Characterization of the folding landscape of monomeric lactose repressor: Quantitative comparison of theory and experiment P Das, CJ Wilson, G Fossati, P Wittung-Stafshede, KS Matthews, ... Proceedings of the National Academy of Sciences 102 (41), 14569-14574, 2005 | 57 | 2005 |
| Pepcvae: Semi-supervised targeted design of antimicrobial peptide sequences P Das, K Wadhawan, O Chang, T Sercu, CD Santos, M Riemer, ... arXiv preprint arXiv:1810.07743, 2018 | 56 | 2018 |
