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]   [people]  

Accessibility
You can view all the papers in reverse chronological order, sets of papers related to broad categories such as machine learning, natural language processing, chemistry, computational biology, or physics, or papers in more specific areas including inference, semi-supervised learning , information retrieval, or reinforcement learning.

Machine learning for chemistry

  • G. Corso, Y. Xu, V. De Bortoli, R. Barzilay, and T. Jaakkola.
    Particle guidance: non-i.i.d. diverse sampling with diffusion models.
    In The 12th International Conference on Learning Representations (ICLR), 2024.
    [link]
  • G. Corso, A. Deng, N. Polizzi, R. Barzilay, and T. Jaakkola.
    The discovery of binding modes requires rethinking docking generalization.
    In The 12th International Conference on Learning Representations (ICLR), 2024.
  • B. Jing, T. Jaakkola, and B. Berger.
    Learning scalar fields for molecular docking with fast fourier transforms.
    In The 12th International Conference on Learning Representations (ICLR), 2024.
    [link]
  • B. A. Koscher, R. B. Canty, M. A. McDonald, K. P. Greenman, C. J. McGill, C. L. Bilodeau, W. Jin, H. Wu, F. H. Vermeire, B. Jin, T. Hart, T. Kulesza, S-C. Li, T. S. Jaakkola, R. Barzilay, R. Gomez-Bombarelli, W. H. Green, and K. F. Jensen.
    Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back.
    Science, 382, 2023.
    [link]
  • G. Liu, D. Catacutan, K. Rathod, K. Swanson, W. Jin, J. Mohammed, A. Chiappino-Pepe, S. Syed, M. Fragis, K. Rachwalski, J. Magolan, M. Surette, B. Coombes, T. Jaakkola, R. Barzilay, J. J. Collins, and J. M. Stokes.
    Deep learning-guided discovery of an antibiotic targeting acinetobacter baumannii.
    Nature Chemical Biology, 2023.
    [link] [pdf]
  • J. Yim, B. Trippe, V. De Bortoli, E. Mathieu, A. Doucet, R. Barzilay, and T. Jaakkola.
    Se(3) diffusion model with application to protein backbone generation.
    In International Conference on Machine Learning (ICML), 2023.
    [link]
  • M. Amine Ketata, C. Laue, R. Mammadov, H. St\ärk, M. Wu, G. Corso, C. Marquet, R. Barzilay, and T. Jaakkola.
    Diffdock-pp: Rigid protein-protein docking with diffusion models.
    In Machine Learning for Drug Discovery (ICLR workshop), 2023.
    [link]
  • B. Jing, E. Erives, P. Pao-Huang, G. Corso, B. Berger, and T. Jaakkola.
    Eigenfold: Generative protein structure prediction with diffusion models.
    In Machine Learning for Drug Discovery Workshop (ICLR workshop), 2023.
    [link]
  • B. Trippe, J. Yim, D. Tischer, D. Baker, T. Broderick, R. Barzilay, and T. Jaakkola.
    Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem.
    In The 11th International Conference on Learning Representations (ICLR), 2023.
    [link]
  • G. Corso, H. St\ärk, B. Jing, R. Barzilay, and T. Jaakkola.
    Diffdock: Diffusion steps, twists, and turns for molecular docking.
    In The 11th International Conference on Learning Representations (ICLR), 2023.
    [link]
  • B. Jing, G. Corso, J. Chang, R. Barzilay, and T. Jaakkola.
    Torsional diffusion for molecular conformer generation.
    In Neural Information Processing Systems (NeurIPS), 2022.
    [link]
  • F. Wong, A. Krishnan, E. Zheng, H. St\ärk, A. Manson, A. Earl, T. Jaakkola, and J. Collins.
    Benchmarking alphafold-enabled molecular docking predictions for antibiotic discovery in molecular systems biology.
    Molecular Systems Biology, 18(9), 2022.
    [link]
  • H. St\ärk, O. Ganea, L. Pattanaik, R. Barzilay, and T. Jaakkola.
    Equibind: Geometric deep learning for drug binding structure prediction.
    In International Conference on Machine Learning (ICML), 2022.
    [link]
  • W. Jin, R. Barzilay, and T. Jaakkola.
    Antibody-antigen interface design via hierarchical structure refinement.
    In International Conference on Machine Learning (ICML), 2022.
    [link]
  • C. Bilodeau, W. Jin, T. Jaakkola, R. Barzilay, and K. F. Jensen.
    Generative models for molecular discovery: Recent advances and challenges.
    WIREs Computational Molecular Science, 2022.
    [link]
  • T. Xie, X. Fu, O. Ganea, R. Barzilay, and T. Jaakkola.
    Crystal diffusion variational autoencoder for periodic material generation.
    In The Tenth International Conference on Learning Representations (ICLR), 2022.
    [pdf]
  • W. Jin, J. Wohlwend, R. Barzilay, and T. Jaakkola.
    Iterative refinement graph neural network for antibody sequence-structure co-design.
    In The Tenth International Conference on Learning Representations (ICLR), 2022.
    [pdf]
  • O. Ganea, X. Huang, C. Bunne, Y. Bian, R. Barzilay, T. Jaakkola, and A. Krause.
    Independent se(3)-equivariant models for end-to-end rigid protein docking.
    In The Tenth International Conference on Learning Representations (ICLR), 2022.
    [pdf]
  • O. Ganea, L. Pattanaik, C.W. Coley, R. Barzilay, K. Jensen, W. Green, and T. Jaakkola.
    Geomol: Torsional geometric generation of molecular 3d conformer ensembles.
    In Neural Information Processing Systems (NeurIPS), 2021.
    [link]
  • W. Jin, J. Stokes, T. Eastman, Z. Itkin, A. V. Zakharov, J. J. Collins, T. Jaakkola, and R. 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]
  • W. Jin, R. Barzilay, and T. Jaakkola.
    Discovering synergistic drug combinations for covid with biological bottleneck models.
    In NeurIPS Machine Learning for Molecules Workshop, 2020.
    [link]
  • W. Jin, R. Barzilay, and T. Jaakkola.
    Hierarchical generation of molecular graphs using structural motifs.
    In International Conference on Machine Learning (ICML), 2020.
    [pdf]
  • W. Jin, R. Barzilay, and T. Jaakkola.
    Multi-objective molecule generation using interpretable substructures.
    In International Conference on Machine Learning (ICML), 2020.
    [pdf]
  • K. Yang, K. Swanson, W. Jin, R. Barzilay, and T. Jaakkola.
    Improving molecular design by stochastic iterative target augmentation.
    In International Conference on Machine Learning (ICML), 2020.
    [link]
  • J. Stokes, K. Yang, K. Swanson, W. Jin, A. Cubillos-Ruiz, N. Donghia, C. MacNair, S. French, L. Carfrae, Z. Bloom-Ackerman, V. Tran, A. Chiappino-Pepe, A. Badran, I. Andrews, E. Chory, G. Church, E. Brown, T. Jaakkola, R. Barzilay, and J. Collins.
    A deep learning approach to antibiotic discovery.
    Cell, 180(4), 2020.
    [pdf]
  • J. Ingraham, V. Garg, R. Barzilay, and T. Jaakkola.
    Generative models for graph-based protein design.
    In Neural Information Processing Systems (NeurIPS), 2019.
    [pdf]
  • K. Yang, K. Swanson, W. Jin, C. Coley, P. Eiden, H. Gao, A. Guzman-Perez, T. Hopper, B. Kelley, M. Miriam, A. Palmer, V. Settels, T. Jaakkola, K. Jensen, and R. Barzilay.
    Analyzing learned molecular representations for property prediction.
    Journal of Chemical Information and Modeling, 2019.
    [link]
  • B. Chen, R. Barzilay, and T. Jaakkola.
    Path-augmented graph transformer network.
    In Learning and Reasoning with Graph-Structured Representations (ICML workshop), 2019.
    [link]
  • J. Ingraham, V. Garg, R. Barzilay, and T. Jaakkola.
    Generative models for graph-based protein design.
    In Deep Generative Models for Highly Structured Data (ICLR workshop), 2019.
    [pdf]
  • C. Coley, W. Jin, L. Rogers, T. Jamison, T. Jaakkola, W. Green, R. Barzilay, and K. F. Jensen.
    A graph-convolutional neural network model for the prediction of chemical reactivity.
    Chemical Science, 10(2):370--377, 2019.
    [link]
  • W. Jin, K. Yang, R. Barzilay, and T. Jaakkola.
    Learning multimodal graph-to-graph translation for molecule optimization.
    In International Conference on Learning Representations (ICLR), 2019.
    [link]
  • W. Jin, R. Barzilay, and T. Jaakkola.
    Junction tree variational autoencoder for molecular graph generation.
    In International Conference on Machine Learning (ICML), 2018.
    [link]
  • W. Jin, C. W. Coley, R. Barzilay, and T. Jaakkola.
    Predicting organic reaction outcomes with weisfeiler-lehman network.
    In Advances in Neural Information Processing Systems (NIPS), 2017.
    [link]
  • T. Lei, W. Jin, R. Barzilay, and T. Jaakkola.
    Deriving neural architectures from sequence and graph kernels.
    In International Conference on Machine Learning (ICML), 2017.
    [pdf]
  • C. W. Coley, R. Barzilay, T. Jaakkola, W. H. Green, and K. F. Jensen.
    Prediction of organic reaction outcomes using machine learning.
    ACS Central Science, 2017.
    [pdf]
  • C. W. Coley, R. Barzilay, W. H. Green, T. Jaakkola, and K. F. Jensen.
    Convolutional embedding of attributed molecular graphs for physical property prediction.
    Journal of Chemical Information and Modeling, 57(8):1757--1772, 2017.