| Learning from protein structure with geometric vector perceptrons B Jing, S Eismann, P Suriana, RJL Townshend, R Dror Ninth International Conference on Learning Representations (ICLR 2021), 2021 | 368 | 2021 |
| Diffdock: Diffusion steps, twists, and turns for molecular docking G Corso, H Stärk, B Jing, R Barzilay, T Jaakkola Eleventh International Conference on Learning Representations (ICLR 2023), 2022 | 325 | 2022 |
| Torsional Diffusion for Molecular Conformer Generation B Jing, G Corso, J Chang, R Barzilay, T Jaakkola Neural Information Processing Systems 2022, 2022 | 202 | 2022 |
| ATOM3D: Tasks On Molecules in Three Dimensions RJL Townshend, M Vögele, P Suriana, A Derry, A Powers, Y Laloudakis, ... NeurIPS 2021 Track on Datasets and Benchmarks, 2021 | 110 | 2021 |
| Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes S Eismann, RJL Townshend, N Thomas, M Jagota, B Jing, RO Dror Proteins: Structure, Function, and Bioinformatics 89 (5), 493-501, 2021 | 70* | 2021 |
| Equivariant Graph Neural Networks for 3D Macromolecular Structure B Jing, S Eismann, PN Soni, RO Dror ICML 2021 Workshop on Computational Biology, 2021 | 69 | 2021 |
| Subspace diffusion generative models B Jing, G Corso, R Berlinghieri, T Jaakkola European Conference on Computer Vision 2022, 2022 | 67 | 2022 |
| EigenFold: Generative Protein Structure Prediction with Diffusion Models B Jing, E Erives, P Pao-Huang, G Corso, B Berger, T Jaakkola ICLR Machine Learning for Drug Discovery Workshop 2023, 2023 | 41 | 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 | 12* | 2023 |
| AlphaFold Meets Flow Matching for Generating Protein Ensembles B Jing, B Berger, T Jaakkola arXiv preprint arXiv:2402.04845, 2024 | 7 | 2024 |
| Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design H Stärk, B Jing, R Barzilay, T Jaakkola arXiv preprint arXiv:2310.05764, 2023 | 7* | 2023 |
| Dirichlet Flow Matching with Applications to DNA Sequence Design H Stark, B Jing, C Wang, G Corso, B Berger, R Barzilay, T Jaakkola arXiv preprint arXiv:2402.05841, 2024 | 6 | 2024 |
| Rotation-invariant gait identification with quaternion convolutional neural networks (student abstract) B Jing, V Prabhu, A Gu, J Whaley Proceedings of the AAAI conference on artificial intelligence 35 (18), 15805 …, 2021 | 3 | 2021 |
| SGVAE: Sequential Graph Variational Autoencoder B Jing, EA Chi, J Tang arXiv preprint arXiv:1912.07800, 2019 | 2 | 2019 |
| Diffusion models in protein structure and docking J Yim, H Stärk, G Corso, B Jing, R Barzilay, TS Jaakkola Wiley Interdisciplinary Reviews: Computational Molecular Science 14 (2), e1711, 2024 | 1 | 2024 |
| Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms B Jing, T Jaakkola, B Berger arXiv preprint arXiv:2312.04323, 2023 | 1 | 2023 |
| Scalable Multimer Structure Prediction using Diffusion Models P Pao-Huang, B Jing, B Berger NeurIPS 2023 AI for Science Workshop, 2023 | 1 | 2023 |
| Verlet Flows: Exact-Likelihood Integrators for Flow-Based Generative Models E Erives, B Jing, T Jaakkola arXiv preprint arXiv:2405.02805, 2024 | | 2024 |
| Structured Diffusion Processes in Deep Generative Models B Jing Massachusetts Institute of Technology, 2022 | | 2022 |
| Modeling Sensorimotor Coordination as Multi-Agent Reinforcement Learning with Differentiable Communication B Jing, W Yin arXiv preprint arXiv:1909.05815, 2019 | | 2019 |
