Wengong Jin

Postdoctoral Fellow at the Broad Institute

wengong [AT] csail.mit.edu

Bio

Wengong Jin is a postdoctoral fellow in the Eric and Wendy Schmidt Center at Broad Institute. Previously, he obtained his PhD at MIT CSAIL, advised by Prof. Regina Barzilay and Prof. Tommi Jaakkola. His research focuses on machine learning for drug discovery. He is particularly interested in developing geometric deep learning and generative AI models for virtual drug screening, de novo drug design, antibody design, and protein-ligand/protein binding. His work was published in leading AI conferences and biology journals like ICML, NeurIPS, ICLR, Nature, Science, Cell, and PNAS. His research received extensive media coverage including Guardian, BBC News, CBS Boston, and Financial Times. He is the recipient of the BroadIgnite Award, Dimitris N. Chorafas Prize, and MIT EECS Outstanding Thesis Award.

News

  • I will be giving a talk at NeurIPS AI for Drug Discovery and Development Workshop and Machine Learning for Structural Biology Workshop
  • Multiple papers accepted to NeurIPS 2023, Nature, and Science.
  • I received the BroadIgnite Award Link.
  • Publications

    Most recent publications on Google Scholar.
    * indicates equal contribution.

    DSMBind: a geometric deep learning framework for unsupervised binding energy prediction and nanobody design

    Wengong Jin*, Xun Chen*, Amrita Vetticaden, Siranush Sarkizova, Raktima Raychowdhury, Caroline Uhler, Nir Hacohen

    Preprint (2023)

    Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation

    Wengong Jin, Siranush Sarkizova, Xun Chen, Nir Hacohen, Caroline Uhler

    Advances in Neural Information Processing Systems (NeurIPS 2023)

    Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement

    Wengong Jin, Regina Barzilay, Tommi Jaakkola

    International Conference on Learning Representations (ICML 2022)

    Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-Design

    Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi Jaakkola

    International Conference on Learning Representations (ICLR 2022)

    Deep learning identifies synergistic drug combinations for treating COVID-19

    Wengong Jin, Jonathan M. Stokes, Richard T. Eastman, Zina Itkin, Alexey V. Zakharov, James J. Collins, Tommi S. Jaakkola, Regina Barzilay

    Proceedings of the National Academy of Sciences (PNAS 2021)

    Enforcing Predictive Invariance across Structured Biomedical Domains

    Wengong Jin, Regina Barzilay, Tommi Jaakkola

    preprint (2021)

    Multi-Objective Molecule Generation using Interpretable Substructures

    Wengong Jin, Regina Barzilay, Tommi Jaakkola

    International Conference on Machine Learning (ICML 2020)

    Hierarchical Generation of Molecular Graphs using Structural Motifs

    Wengong Jin, Regina Barzilay, Tommi Jaakkola

    International Conference on Machine Learning (ICML 2020)

    Learning Multimodal Graph-to-Graph Translation for Molecular Optimization

    Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola

    International Conference on Learning Representations (ICLR) 2019

    Junction Tree Variational Autoencoder for Molecular Graph Generation

    Wengong Jin, Regina Barzilay, Tommi Jaakkola

    International Conference on Machine Learning (ICML 2018)

    Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network

    Wengong Jin, Connor W. Coley, Regina Barzilay, Tommi Jaakkola

    Neural Information Processing Systems (NIPS) 2017

    Deriving Neural Architectures from Sequence and Graph Kernels

    Wengong Jin, Tao Lei, Regina Barzilay, Tommi Jaakkola

    International Conference on Machine Learning (ICML 2017)

    DSMBind: a geometric deep learning framework for unsupervised binding energy prediction and nanobody design

    Wengong Jin*, Xun Chen*, Amrita Vetticaden, Siranush Sarkizova, Raktima Raychowdhury, Caroline Uhler, Nir Hacohen

    Preprint (2023)

    FAFormer: Frame Averaging Transformer for Predicting Nucleic Acid-Protein Interactions

    Tinglin Huang, Zhenqiao Song, Rex Ying, and Wengong Jin

    NeurIPS Machine Learning for Structure Biology (MLSB) Workshop (2023)

    Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation

    Wengong Jin, Siranush Sarkizova, Xun Chen, Nir Hacohen, Caroline Uhler

    Advances in Neural Information Processing Systems (NeurIPS 2023)

    Discovery of a structural class of antibiotics with explainable deep learning

    Felix Wong, Erica J. Zheng, Jacqueline A. Valeri, Nina M. Donghia, Melis N. Anahtar, Satotaka Omori, Alicia Li, Andres Cubillos-Ruiz, Aarti Krishnan, Wengong Jin, Abigail L. Manson, Jens Friedrichs, Ralf Helbig, Behnoush Hajian, Dawid K. Fiejtek, Florence F. Wagner, Holly H. Soutter, Ashlee M. Earl, Jonathan M. Stokes, Lars D. Renner, James J. Collins

    Nature (2023)

    Autonomous, multi-property-driven molecular discovery: from predictions to measurements and back

    Brent 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 Gómez-Bombarelli, William H Green, Klavs F Jensen

    Science (2023)

    Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii

    Gary Liu, Denise Catacutan, Khushi Rathod, Kyle Swanson, Wengong Jin, Jody Mohammed, Anush Chiappino-Pepe, Saad Syed, Meghan Fragis, Kenneth Rachwalski, Jakob Magolan, Michael Surette, Brian Coombes, Tommi Jaakkola, Regina Barzilay, James Collins, Jonathan Stokes.

    Nature Chemical Biology (2023)

    Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement

    Wengong Jin, Regina Barzilay, Tommi Jaakkola

    International Conference on Learning Representations (ICML 2022)

    Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-Design

    Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi Jaakkola

    International Conference on Learning Representations (ICLR 2022)

    Generative models for molecular discovery: Recent advances and challenges

    Camille Bilodeau, Wengong Jin, Tommi Jaakkola, Regina Barzilay, Klavs F. Jensen

    Wiley Interdisciplinary Reviews: Computational Molecular Science (2022)

    Deep learning identifies synergistic drug combinations for treating COVID-19

    Wengong Jin, Jonathan M. Stokes, Richard T. Eastman, Zina Itkin, Alexey V. Zakharov, James J. Collins, Tommi S. Jaakkola, Regina Barzilay

    Proceedings of the National Academy of Sciences (PNAS 2021)

    Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis

    Karren Yang, Samuel Goldman, Wengong Jin, Alex X Lu, Regina Barzilay, Tommi Jaakkola, Caroline Uhler

    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021)

    Enforcing Predictive Invariance across Structured Biomedical Domains

    Wengong Jin, Regina Barzilay, Tommi Jaakkola

    preprint (2021)

    A Deep Learning Approach to Antibiotic Discovery

    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, James J. Collins

    Cell (2020)

    Multi-Objective Molecule Generation using Interpretable Substructures

    Wengong Jin, Regina Barzilay, Tommi Jaakkola

    International Conference on Machine Learning (ICML 2020)

    Hierarchical Generation of Molecular Graphs using Structural Motifs

    Wengong Jin, Regina Barzilay, Tommi Jaakkola

    International Conference on Machine Learning (ICML 2020)

    Improving Molecular Design by Stochastic Iterative Target Augmentation

    Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, Tommi Jaakkola

    International Conference on Machine Learning (ICML 2020)

    Functional Transparency for Structured Data: a Game-Theoretic Approach

    Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi Jaakkola

    International Conference on Machine Learning (ICML 2019)

    Learning Multimodal Graph-to-Graph Translation for Molecular Optimization

    Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola

    International Conference on Learning Representations (ICLR) 2019

    A graph-convolutional neural network model for the prediction of chemical reactivity

    Connor W Coley, Wengong Jin, Luke Rogers, Timothy F Jamison, Tommi S Jaakkola, William H Green, Regina Barzilay, Klavs F Jensen

    Chemical Science (2019)

    Analyzing learned molecular representations for property prediction

    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, Regina Barzilay

    Journal of chemical information and modeling (2019)

    Junction Tree Variational Autoencoder for Molecular Graph Generation

    Wengong Jin, Regina Barzilay, Tommi Jaakkola

    International Conference on Machine Learning (ICML 2018)

    Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network

    Wengong Jin, Connor W. Coley, Regina Barzilay, Tommi Jaakkola

    Neural Information Processing Systems (NIPS) 2017

    Deriving Neural Architectures from Sequence and Graph Kernels

    Wengong Jin, Tao Lei, Regina Barzilay, Tommi Jaakkola

    International Conference on Machine Learning (ICML 2017)

    Vitæ

    Acknowledgement
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