People

Wengong Jin


Assistant Professor of Computer Sciences
Affiliated Member of IPHI

📧 w.jin@northeastern.edu
🌐 Jin Lab
📍 Office 907, 177 Huntington Ave, Boston, MA 02115

Question & Approach

As human health faces mounting challenges from complex diseases, antimicrobial resistance, and shifts in diet and lifestyle, there is an urgent need to uncover new biological insights and develop novel therapeutics. The Jin lab tackles this problem by applying geometric and generative artificial intelligence to model, predict, and design biological systems across scales—from molecular interactions to whole-organism traits—while directing these approaches to explore their impact on plant–human interactions. Building on pioneering contributions in generative molecular design, drug discovery, and protein engineering, the lab is expanding these methods to plant biology and human health, investigating how AI can illuminate hidden mechanisms of nutrient metabolism, immune regulation, and plant-derived bioactive compounds that shape human physiology.

The Jin lab employs a multidisciplinary strategy that combines algorithmic innovation with close collaboration across biology, chemistry, and engineering. Their approach integrates state-of-the-art AI methods such as equivariant neural networks, diffusion generative models, and graph neural networks with large-scale genomics, structural biology, and multi-omics datasets. By predicting enzyme–substrate interactions, modeling plant–pathogen interfaces, and designing novel proteins or synthetic biology circuits, they aim to accelerate discovery of traits and interventions that improve nutrition, enhance immunity, and reduce disease burden. These advances will ultimately provide a computational framework for linking molecular design to both agricultural and biomedical applications, exemplifying how AI can drive transformative progress at the plant–human interface.

Bio

Dr. Wengong Jin is an Assistant Professor in the Khoury College of Computer Sciences and an affiliated member of Institute for Plant-Human Interface (IPHI) at Northeastern University. Prior to joining Northeastern, he was a Postdoctoral Fellow at the Broad Institute of MIT and Harvard, where he developed pioneering methods in geometric and generative artificial intelligence for molecular and protein design. His work established new frameworks for accelerating drug discovery and engineering biological systems, bridging the gap between computer science and the life sciences. He earned his Ph.D. in Computer Science from MIT, where his doctoral research under Prof. Regina Barzilay focused on developing deep generative models for small molecules and proteins, culminating in new approaches for AI-driven molecular generation and prediction. He previously received his B.S. in Computer Science from Shanghai Jiao Tong University. Dr. Jin has published extensively in leading venues across AI and biology, including ICML, NeurIPS, ICLR, Nature, Science, Cell, and PNAS. His research has been recognized with the BroadIgnite Award, the Dimitris N. Chorafas Prize, and the MIT EECS Outstanding Thesis Award, and has been covered by outlets such as The Guardian, BBC News, CBS Boston, and the Financial Times. In addition to his teaching and research at Northeastern, he is a Visiting Research Scientist at the Eric and Wendy Schmidt Center at the Broad Institute.

Key Publications

Krishnan A, Anahtar MN, Valeri JA, Jin W, et al. A generative deep learning approach to de novo antibiotic design. Cell. 2025;188,1-18.

Jin W, Sarkizova S, Chen X, Hacohen N, Uhler C. Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler’s Rotation Equation. Neur Info Proc Sys. 2023;36;33514-33528.

Jin W, Barzilay R, Jaakkola T. Antibody–antigen docking and design via hierarchical equivariant refinement. Proc 39th Int Conf Mach Learn. 2022;162: 10217–10227.

Jin W, Stokes JM, Eastman RT, Itkin Z, Zakharov AV, Collins JJ, et al. Deep learning identifies synergistic drug combinations for treating COVID-19. Proc Natl Acad Sci U S A. 2021;118: e2105070118.

Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, et al. A deep learning approach to antibiotic discovery. Cell. 2020;180: 688–702.

Jin W, Barzilay R, Jaakkola T. Hierarchical generation of molecular graphs using structural motifs. Proc 37th Int Conf Mach Learn. 2020;119: 4839–4848.

Jin W, Barzilay R, Jaakkola T. Junction tree variational autoencoder for molecular graph generation. Proc 35th Int Conf Mach Learn. 2018;80: 2323–2332.


Full list of publications on Google Scholar

Latest News

  • IPHI Welcomes AI Scientist Wengong Jin as Affiliated Member
    IPHI welcomes Wengong Jin as an affiliated member, bringing pioneering AI methods to accelerate discoveries in drug design, enzyme biology, and plant–human interactions, strengthening the institute’s vision for transformative science….