Learning from protein structure with geometric vector perceptrons B Jing, S Eismann, P Suriana, RJL Townshend, R Dror International Conference on Learning Representations, 2020 | 342 | 2020 |
Tiramisu: A polyhedral compiler for expressing fast and portable code R Baghdadi, J Ray, MB Romdhane, E Del Sozzo, A Akkas, Y Zhang, ... 2019 IEEE/ACM International Symposium on Code Generation and Optimization …, 2019 | 332 | 2019 |
End-to-end learning on 3d protein structure for interface prediction R Townshend, R Bedi, P Suriana, R Dror Advances in Neural Information Processing Systems 32, 2019 | 111 | 2019 |
Atom3d: Tasks on molecules in three dimensions RJL Townshend, M Vögele, P Suriana, A Derry, A Powers, Y Laloudakis, ... arXiv preprint arXiv:2012.04035, 2020 | 108 | 2020 |
Parallel associative reductions in halide P Suriana, A Adams, S Kamil 2017 IEEE/ACM International Symposium on Code Generation and Optimization …, 2017 | 33 | 2017 |
Fragment-based ligand generation guided by geometric deep learning on protein-ligand structure AS Powers, HH Yu, P Suriana, RO Dror bioRxiv, 2022.03. 17.484653, 2022 | 25 | 2022 |
Protein model quality assessment using rotation-equivariant, hierarchical neural networks S Eismann, P Suriana, B Jing, RJL Townshend, RO Dror arXiv preprint arXiv:2011.13557, 2020 | 11 | 2020 |
Tiramisu: A code optimization framework for high performance systems R Baghdadi, J Ray, MB Romdhane, E Del Sozzo, P Suriana, S Kamil, ... arXiv preprint arXiv:1804.10694, 2018 | 11 | 2018 |
Learning from protein structure with geometric vector perceptrons (2021) B Jing, S Eismann, P Suriana, RJL Townshend, R Dror arXiv preprint arXiv:2009.01411, 2009 | 10 | 2009 |
Learning from protein structure with geometric vector perceptrons. arXiv B Jing, S Eismann, P Suriana, RJL Townshend, R Dror arXiv preprint arXiv:2009.01411, 2020 | 9 | 2020 |
Learning from Protein Structure with Geometric Vector Perceptrons. arXiv 2021 B Jing, S Eismann, P Suriana, RJL Townshend, R Dror arXiv preprint arXiv:2009.01411, 2009 | 9 | 2009 |
Geometric deep learning for structure-based ligand design AS Powers, HH Yu, P Suriana, RV Koodli, T Lu, JM Paggi, RO Dror ACS Central Science 9 (12), 2257-2267, 2023 | 5 | 2023 |
Fourier-Motzkin with non-linear symbolic constant coefficients PA Suriana Massachusetts Institute of Technology, 2016 | 5 | 2016 |
FlexVDW: A machine learning approach to account for protein flexibility in ligand docking P Suriana, JM Paggi, RO Dror arXiv preprint arXiv:2303.11494, 2023 | 3 | 2023 |
Protein model quality assessment using rotation‐equivariant transformations on point clouds S Eismann, P Suriana, B Jing, RJL Townshend, RO Dror Proteins: Structure, Function, and Bioinformatics 91 (8), 1089-1096, 2023 | 1 | 2023 |
Enhancing Ligand Pose Sampling for Molecular Docking P Suriana, RO Dror ArXiv, 2023 | | 2023 |
Systems and Methods for Generating Ligand Compounds AS Powers, H Yu, PA Suriana, RO Dror US Patent App. 18/184,600, 2023 | | 2023 |
Technical Report about Tiramisu: a Three-Layered Abstraction for Hiding Hardware Complexity from DSL Compilers R Baghdadi, J Ray, MB Romdhane, E Del Sozzo, P Suriana, S Kamil, ... arXiv preprint arXiv:1803.00419, 2018 | | 2018 |
Tiramisu: A Polyhedral Compiler with A Scheduling Language for Targeting High Performance Systems R Baghdadi, J Ray, MB Romdhane, E Del Sozzo, P Suriana, A Akkas, ... | | |