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]   [courses]


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

  • 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.