Tommi S. Jaakkola, Ph.D. Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society MIT Computer Science and Artificial Intelligence Laboratory Stata Center, Bldg 32-G470 Cambridge, MA 02139 tommi at csail dot mit dot edu [home]   [papers]   [research]   [courses]   [people]

The list does not include all recent preprints from arXiv or bioRxiv. For a more complete list, see my Google scholar page

Machine learning for chemistry

• Bowen Jing, Hannes Stark, Tommi Jaakkola, and Bonnie Berger.
  Generative Modeling of Molecular Dynamics Trajectories.
  Neural Information Processing Systems (NeurIPS), 2024.
  [link]
• Bowen Jing, Bonnie Berger, and Tommi Jaakkola.
  AlphaFold Meets Flow Matching for Generating Protein Ensembles.
  International Conference on Machine Learning (ICML), 2024.
  [link]
• Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, and Tommi Jaakkola.
  Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design.
  International Conference on Machine Learning (ICML), 2024.
  [link]
• Jason Yim, Hannes Stark, Gabriel Corso, Bowen Jing, Regina Barzilay, and Tommi Jaakkola.
  Diffusion models in protein structure and docking.
  WIREs Computational Molecular Science, 14(2), pp. e1711. 2024.
  [link]
• Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, and Tommi Jaakkola.
  Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models.
  The 12th International Conference on Learning Representations (ICLR), 2024.
  [link]
• Gabriele Corso, Arthur Deng, Benjamin Fry, Nicholas Polizzi, Regina Barzilay, and Tommi Jaakkola.
  Deep Confident Steps to New Pockets: Strategies for Docking Generalization.
  The 12th International Conference on Learning Representations (ICLR), 2024.
  [link]
• Bowen Jing, Tommi Jaakkola, and Bonnie Berger.
  Learning Scalar Fields for Molecular Docking with Fast Fourier Transforms.
  The 12th International Conference on Learning Representations (ICLR), 2024.
  [link]
• Brent A. Koscher, Richard B. Canty, Matthew A. McDonald, Kevin P. Greenman, Charles J. McGill, Camille L. Bilodeau, Wengong Jin, Haoyang Wu, Florence H. Vermeire, Brooke Jin, Travis Hart, Timothy Kulesza, Shih-Cheng Li, Tommi S. Jaakkola, Regina Barzilay, Rafael Gomez-Bombarelli, William H. Green, and and Klavs F. Jensen.
  Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back.
  Science, 382. 2023.
  [link]
• Gary Liu, Denise B. Catacutan, Khushi Rathod, Kyle Swanson, Wengong Jin, Jody C. Mohammed, Anush Chiappino-Pepe, Saad A. Syed, Meghan Fragis, Kenneth Rachwalski, Jakob Magolan, Michael G. Surette, Brian K. Coombes, Tommi Jaakkola, Regina Barzilay, James J. Collins, and Jonathan M. Stokes.
  Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii.
  Nature Chemical Biology, 2023.
  [link] [pdf]
• Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, and Tommi Jaakkola.
  SE(3) diffusion model with application to protein backbone generation.
  International Conference on Machine Learning (ICML), 2023.
  [link]
• Mohamed Amine Ketata, Cedrik Laue, Ruslan Mammadov, Hannes Stark, Menghua Wu, Gabriele Corso, Celine Marquet, Regina Barzilay, and Tommi S. Jaakkola.
  DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models.
  Machine Learning for Drug Discovery (ICLR workshop), 2023.
  [link]
• Bowen Jing, Ezra Erives, Peter Pao-Huang, Gabriele Corso, Bonnie Berger, and Tommi Jaakkola.
  EigenFold: Generative Protein Structure Prediction with Diffusion Models.
  Machine Learning for Drug Discovery Workshop (ICLR workshop), 2023.
  [link]
• Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, and Tommi Jaakkola.
  Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problem.
  The 11th International Conference on Learning Representations (ICLR), 2023.
  [link]
• Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, and Tommi Jaakkola.
  DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking.
  The 11th International Conference on Learning Representations (ICLR), 2023.
  [link]
• Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi Jaakkola.
  Torsional Diffusion for Molecular Conformer Generation.
  Neural Information Processing Systems (NeurIPS), 2022.
  [link]
• Felix Wong, Aarti Krishnan, Erica J Zheng, Hannes Stark, Abigail L Manson, Ashlee M Earl, Tommi Jaakkola, and James J Collins.
  Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery in Molecular Systems Biology.
  Molecular Systems Biology, 18(9). 2022.
  [link]
• Hannes Stark, Octavian-Eugen Ganea, Lagnajit Pattanaik, Regina Barzilay, and Tommi Jaakkola.
  EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction.
  International Conference on Machine Learning (ICML), 2022.
  [link]
• Wengong Jin, Regina Barzilay, and Tommi Jaakkola.
  Antibody-Antigen Interface Design via Hierarchical Structure Refinement.
  International Conference on Machine Learning (ICML), 2022.
  [link]
• Camille Bilodeau, Wengong Jin, Tommi Jaakkola, Regina Barzilay, and Klavs F. Jensen.
  Generative models for molecular discovery: Recent advances and challenges.
  WIREs Computational Molecular Science, 2022.
  [link]
• Tian Xie, Xiang Fu, Octavian Ganea, Regina Barzilay, and Tommi Jaakkola.
  Crystal Diffusion Variational Autoencoder for Periodic Material Generation.
  The Tenth International Conference on Learning Representations (ICLR), 2022.
  [pdf]
• Wengong Jin, Jeremy Wohlwend, Regina Barzilay, and Tommi Jaakkola.
  Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design.
  The Tenth International Conference on Learning Representations (ICLR), 2022.
  [pdf]
• Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi Jaakkola, and Andreas Krause.
  Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking.
  The Tenth International Conference on Learning Representations (ICLR), 2022.
  [pdf]
• Octavian-Eugen Ganea, Lagnajit Pattanaik, Connor W. Coley, Regina Barzilay, Klavs F. Jensen, William H. Green, and Tommi Jaakkola.
  GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles.
  Neural Information Processing Systems (NeurIPS), 2021.
  [link]
• Wengong Jin, Jonathan M. Stokes, Richard T. Eastman, Zina Itkin, Alexey V. Zakharov, James J. Collins, Tommi S. Jaakkola, and Regina Barzilay.
  Deep learning identifies synergistic drug combinations for treating COVID-19.
  Proceedings of the National Academy of Sciences of the USA (PNAS), 118(39). 2021.
  [link]
• Wengong Jin, Regina Barzilay, and Tommi Jaakkola.
  Discovering Synergistic Drug Combinations for COVID with Biological Bottleneck Models.
  NeurIPS Machine Learning for Molecules Workshop, 2020.
  [link]
• Wengong Jin, Regina Barzilay, and Tommi Jaakkola.
  Hierarchical Generation of Molecular Graphs using Structural Motifs.
  International Conference on Machine Learning (ICML), 2020.
  [pdf]
• Wengong Jin, Regina Barzilay, and Tommi Jaakkola.
  Multi-Objective Molecule Generation using Interpretable Substructures.
  International Conference on Machine Learning (ICML), 2020.
  [pdf]
• Kevin Yang, Kyle Swanson, Wengong Jin, Regina Barzilay, and Tommi Jaakkola.
  Improving Molecular Design by Stochastic Iterative Target Augmentation.
  International Conference on Machine Learning (ICML), 2020.
  [link]
• Jonathan M. Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M. Donghia, Craig R. MacNair, Shawn French, Lindsey A. Carfrae, Zohar Bloom-Ackerman, Victoria M. Tran, Anush Chiappino-Pepe, Ahmed H. Badran, Ian W. Andrews, Emma J. Chory, George M. Church, Eric D. Brown, Tommi S. Jaakkola, Regina Barzilay, and James J. Collins.
  A Deep Learning Approach to Antibiotic Discovery.
  Cell, 180(4). 2020.
  [pdf]
• John Ingraham, Vikas K. Garg, Regina Barzilay, and Tommi Jaakkola.
  Generative Models for Graph-Based Protein Design.
  Neural Information Processing Systems (NeurIPS), 2019.
  [pdf]
• Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi Jaakkola, Klavs Jensen, and Regina Barzilay.
  Analyzing Learned Molecular Representations for Property Prediction.
  Journal of Chemical Information and Modeling, 2019.
  [link]
• Benson Chen, Regina Barzilay, and Tommi Jaakkola.
  Path-Augmented Graph Transformer Network.
  Learning and Reasoning with Graph-Structured Representations (ICML workshop), 2019.
  [link]
• John Ingraham, Vikas K. Garg, Regina Barzilay, and Tommi Jaakkola.
  Generative Models for Graph-Based Protein Design.
  Deep Generative Models for Highly Structured Data (ICLR workshop), 2019.
  [pdf]
• Connor W. Coley, Wengong Jin, Luke Rogers, Timothy F. Jamison, Tommi S. Jaakkola, William H. Green, Regina Barzilay, and Klavs F. Jensen.
  A Graph-Convolutional Neural Network Model for the Prediction of Chemical Reactivity.
  Chemical Science, 10(2), pp. 370–377. 2019.
  [link]
• Wengong Jin, Kevin Yang, Regina Barzilay, and Tommi Jaakkola.
  Learning Multimodal Graph-to-Graph Translation for Molecule Optimization.
  International Conference on Learning Representations (ICLR), 2019.
  [link]
• Wengong Jin, Regina Barzilay, and Tommi Jaakkola.
  Junction Tree Variational Autoencoder for Molecular Graph Generation.
  International Conference on Machine Learning (ICML), 2018.
  [link]
• Wengong Jin, Connor W. Coley, Regina Barzilay, and Tommi Jaakkola.
  Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network.
  Advances in Neural Information Processing Systems (NIPS), 2017.
  [link]
• Tao Lei, Wengong Jin, Regina Barzilay, and Tommi Jaakkola.
  Deriving Neural Architectures from Sequence and Graph Kernels.
  International Conference on Machine Learning (ICML), 2017.
  [pdf]
• Connor W. Coley, Regina Barzilay, Tommi S. Jaakkola, William H. Green, and Klavs F. Jensen.
  Prediction of Organic Reaction Outcomes Using Machine Learning.
  ACS Central Science, 2017.
  [pdf]
• Connor W. Coley, Regina Barzilay, William H. Green, Tommi S. Jaakkola, and Klavs F. Jensen.
  Convolutional embedding of attributed molecular graphs for physical property prediction.
  Journal of Chemical Information and Modeling, 57(8), pp. 1757-1772. 2017.
  [link]