| Principal Neighbourhood Aggregation for Graph Nets G Corso, L Cavalleri, D Beaini, P Liò, P Veličković Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 588 | 2020 |
| DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking G Corso, H Stärk, B Jing, R Barzilay, T Jaakkola International Conference on Learning Representations (ICLR 2023), 2023 | 273 | 2023 |
| Torsional Diffusion for Molecular Conformer Generation B Jing*, G Corso*, J Chang, R Barzilay, TS Jaakkola Conference on Neural Information Processing Systems (NeurIPS 2022), 2022 | 174 | 2022 |
| 3D Infomax improves GNNs for Molecular Property Prediction H Stärk, D Beaini, G Corso, P Tossou, C Dallago, S Günnemann, P Liò International Conference on Machine Learning (ICML 2022), 2022 | 163 | 2022 |
| Directional Graph Networks D Beaini, S Passaro, V Létourneau, WL Hamilton, G Corso, P Liò International Conference on Machine Learning (ICML 2021), 2021 | 160 | 2021 |
| Subspace Diffusion Generative Models B Jing*, G Corso*, R Berlinghieri, T Jaakkola European Conference on Computer Vision (ECCV 2022), 2022 | 61 | 2022 |
| Eigenfold: Generative protein structure prediction with diffusion models B Jing, E Erives, P Pao-Huang, G Corso, B Berger, T Jaakkola arXiv preprint arXiv:2304.02198, 2023 | 37 | 2023 |
| Neural Distance Embeddings for Biological Sequences G Corso, R Ying, M Pándy, P Veličković, J Leskovec, P Liò Conference on Neural Information Processing Systems (NeurIPS 2021), 2021 | 27 | 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 | 21 | 2023 |
| Learning Graph Search Heuristics M Pándy, R Ying, G Corso, P Velickovic, J Leskovec, P Liò Learning on Graphs Conference (LoG 2022), 2022 | 7 | 2022 |
| Modeling Molecular Structures with Intrinsic Diffusion Models G Corso Massachusetts Institute of Technology, 2023 | 5 | 2023 |
| Graph Anisotropic Diffusion AAA Elhag, G Corso, H Stärk, MM Bronstein arXiv preprint arXiv:2205.00354, 2022 | 5* | 2022 |
| Deep Confident Steps to New Pockets: Strategies for Docking Generalization G Corso, A Deng, B Fry, N Polizzi, R Barzilay, T Jaakkola International Conference on Learning Representations (ICLR 2024), 2024 | 3* | 2024 |
| DiffDock-Pocket: Diffusion for Pocket-Level Docking with Sidechain Flexibility M Plainer, M Toth, S Dobers, H Stark, G Corso, C Marquet, R Barzilay | 3 | 2023 |
| Particle Guidance: non-IID Diverse Sampling with Diffusion Models G Corso, Y Xu, V De Bortoli, R Barzilay, T Jaakkola International Conference on Learning Representations (ICLR 2024), 2024 | 2 | 2024 |
| Graph neural networks G Corso, H Stark, S Jegelka, T Jaakkola, R Barzilay Nature Reviews Methods Primers 4 (1), 17, 2024 | | 2024 |
| 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 | | 2024 |
| 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 | | 2024 |
Tommi JaakkolaMITBestätigte E-Mail-Adresse bei csail.mit.edu
