| DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking G Corso, H Stärk, B Jing, R Barzilay, T Jaakkola ICLR 2023: International Conference on Learning Representations (full paper)., 2023 | 463 | 2023 |
| EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction H Stärk, OE Ganea, L Pattanaik, R Barzilay, T Jaakkola ICML 2022: International Conference on Machine Learning (full paper). Also …, 2022 | 286 | 2022 |
| 3D Infomax improves GNNs for Molecular Property Prediction H Stärk, D Beaini, G Corso, P Tossou, C Dallago, S Günnemann, P Liò ICML 2022: International Conference on Machine Learning (full paper). Also …, 2022 | 233 | 2022 |
| Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery F Wong, A Krishnan, EJ Zheng, H Stärk, AL Manson, AM Earl, T Jaakkola, ... Molecular systems biology 18 (9), e11081, 2022 | 149 | 2022 |
| Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design I Igashov, H Stärk, C Vignac, VG Satorras, P Frossard, M Welling, ... Under review. arXiv preprint https://arxiv.org/abs/2210.05274, 2022 | 132 | 2022 |
| Light Attention Predicts Protein Location from the Language of Life H Stärk, C Dallago, M Heinzinger, B Rost Bioinformatics Advances. Also at 2 ICLR'21 Workshops, 1 ICML'21 Workshop …, 2021 | 108 | 2021 |
| DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models MA Ketata, C Laue, R Mammadov, H Stärk, M Wu, G Corso, C Marquet, ... arXiv preprint arXiv:2304.03889, 2023 | 41 | 2023 |
| Graph neural networks G Corso, H Stark, S Jegelka, T Jaakkola, R Barzilay Nature Reviews Methods Primers 4 (1), 17, 2024 | 39 | 2024 |
| Dirichlet Flow Matching with Applications to DNA Sequence Design H Stark, B Jing, C Wang, G Corso, B Berger, R Barzilay, T Jaakkola ICML 2024; also Spotlight at ICLR 2024 MLGenX workshop, 2024 | 28 | 2024 |
| Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design H Stärk, B Jing, R Barzilay, T Jaakkola ICML 2024; also Spotlight at NeurIPS 2023 AI4Science Workshop, 2023 | 13 | 2023 |
| Generalized laplacian positional encoding for graph representation learning S Maskey, A Parviz, M Thiessen, H Stärk, Y Sadikaj, H Maron NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations, 2022 | 9 | 2022 |
| DiffDock-Pocket: Diffusion for Pocket-Level Docking with Sidechain Flexibility M Plainer, M Toth, S Dobers, H Stark, G Corso, C Marquet, R Barzilay | 7 | 2023 |
| Blind protein-ligand docking with diffusion-based deep generative models G Corso, B Jing, H Stark, R Barzilay, T Jaakkola Biophysical Journal 122 (3), 143a, 2023 | 5 | 2023 |
| PLINDER: The protein-ligand interactions dataset and evaluation resource J Durairaj, Y Adeshina, Z Cao, X Zhang, V Oleinikovas, T Duignan, ... bioRxiv, 2024.07. 17.603955, 2024 | 4 | 2024 |
| Transition Path Sampling with Boltzmann Generator-based MCMC Moves M Plainer, H Stärk, C Bunne, S Günnemann Oral at GenBio Workshop (NeurIPS 2023), 2023 | 4 | 2023 |
| Jointly Learnable Data Augmentations for Self-Supervised GNNs ZT Kefato, S Girdzijauskas, H Stärk SSL Workshop at NeurIPS 2021, 2021 | 4 | 2021 |
| Generative Modeling of Molecular Dynamics Trajectories B Jing, H Stärk, T Jaakkola, B Berger arXiv preprint arXiv:2409.17808, 2024 | 3 | 2024 |
| Latent Space Simulator for Unveiling Molecular Free Energy Landscapes and Predicting Transition Dynamics S Dobers, H Stark, X Fu, D Beaini, S Günnemann NeurIPS 2023 AI for Science Workshop, 2023 | 1 | 2023 |
| The First Learning on Graphs Conference: Preface B Rieck, R Pascanu, Y Du, H Stärk, D Lim, CK Joshi, A Deac, I Duta, ... Learning on Graphs Conference, i-xxiii, 2022 | | 2022 |
| Graph Anisotropic Diffusion AAA Elhag, G Corso, H Stärk, MM Bronstein ICLR 2022 MLDD and GTRL workshops, 2022 | | 2022 |
Tommi JaakkolaMITVerified email at csail.mit.edu
