Wengong Jin is an assistant professor at Khoury College of Computer Sciences at Northeastern University. He is also a visiting research scientist in the Eric and Wendy Schmidt Center at Broad Institute. 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 drug discovery and biology. 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.
I am recruting postdoc and PhD students who are interested in AI for drug discovery and biology, starting in Fall 2024 or Spring 2025. If you are interested, please send me an email with your CV.
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)
Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substrates
Zhenqiao Song, Yunlong Zhao, Wenxian Shi, Wengong Jin, Yang Yang, Lei Li
International Conference on Machine Learning (ICML), 2024
SurfPro: Functional Protein Design Based on Continuous Surface
Zhenqiao Song, Tinglin Huang, Lei Li, Wengong Jin
International Conference on Machine Learning (ICML), 2024
RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matching
Divya Nori, Wengong Jin
International Conference on Machine Learning (ICML), 2024
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)