Paragraph vector based topic model for language model adaptation. - Wengong Jin; Tianxing He; Yanmin Qian; Kai Yu 004 (INTERSPEECH, 2015)

Deriving Neural Architectures from Sequence and Graph Kernels. - Tao Lei 001; Wengong Jin; Regina Barzilay; Tommi S. Jaakkola (ICML, 2017)

Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network. - Wengong Jin; Connor W. Coley; Regina Barzilay; Tommi S. Jaakkola (NIPS, 2017)

Junction Tree Variational Autoencoder for Molecular Graph Generation. - Wengong Jin; Regina Barzilay; Tommi S. Jaakkola (ICML, 2018)

Learning Multimodal Graph-to-Graph Translation for Molecular Optimization. - Wengong Jin; Kevin Yang; Regina Barzilay; Tommi S. Jaakkola (CoRR, 2018)

Learning Multimodal Graph-to-Graph Translation for Molecule Optimization. - Wengong Jin; Kevin Yang; Regina Barzilay; Tommi S. Jaakkola (ICLR (Poster), 2019)

Functional Transparency for Structured Data: a Game-Theoretic Approach. - Guang-He Lee; Wengong Jin; David Alvarez-Melis; Tommi S. Jaakkola (ICML, 2019)

Are Learned Molecular Representations Ready For Prime Time? - Kevin Yang; Kyle Swanson; Wengong Jin; Connor W. Coley; Philipp Eiden; Hua Gao; Angel Guzman-Perez; Timothy Hopper; Brian Kelley; Miriam Mathea; Andrew Palmer; Volker Settels; et al. (CoRR, 2019)

Multi-resolution Autoregressive Graph-to-Graph Translation for Molecules. - Wengong Jin; Regina Barzilay; Tommi S. Jaakkola (CoRR, 2019)

Analyzing Learned Molecular Representations for Property Prediction. - Kevin Yang; Kyle Swanson; Wengong Jin; Connor W. Coley; Philipp Eiden; Hua Gao; Angel Guzman-Perez; Timothy Hopper; Brian Kelley; Miriam Mathea; Andrew Palmer; Volker Settels; et al. (J. Chem. Inf. Model., 2019)

Correction to Analyzing Learned Molecular Representations for Property Prediction. - Kevin Yang; Kyle Swanson; Wengong Jin; Connor W. Coley; Philipp Eiden; Hua Gao; Angel Guzman-Perez; Timothy Hopper; Brian Kelley; Miriam Mathea; Andrew Palmer; Volker Settels; et al. (J. Chem. Inf. Model., 2019)

Hierarchical Generation of Molecular Graphs using Structural Motifs. - Wengong Jin; Regina Barzilay; Tommi S. Jaakkola (ICML, 2020)

Multi-Objective Molecule Generation using Interpretable Substructures. - Wengong Jin; Regina Barzilay; Tommi S. Jaakkola (ICML, 2020)

Improving Molecular Design by Stochastic Iterative Target Augmentation. - Kevin Yang; Wengong Jin; Kyle Swanson; Regina Barzilay; Tommi S. Jaakkola (ICML, 2020)

Composing Molecules with Multiple Property Constraints. - Wengong Jin; Regina Barzilay; Tommi S. Jaakkola (CoRR, 2020)

Adaptive Invariance for Molecule Property Prediction. - Wengong Jin; Regina Barzilay; Tommi S. Jaakkola (CoRR, 2020)

Domain Extrapolation via Regret Minimization. - Wengong Jin; Regina Barzilay; Tommi S. Jaakkola (CoRR, 2020)

Improved Conditional Flow Models for Molecule to Image Synthesis. - Karren D. Yang; Samuel Goldman; Wengong Jin; Alex Lu 002; Regina Barzilay; Tommi S. Jaakkola; Caroline Uhler (CoRR, 2020)

Discovering Synergistic Drug Combinations for COVID with Biological Bottleneck Models. - Wengong Jin; Regina Barzilay; Tommi S. Jaakkola (CoRR, 2020)

Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis. - Karren D. Yang; Samuel Goldman; Wengong Jin; Alex X. Lu; Regina Barzilay; Tommi S. Jaakkola; Caroline Uhler (CVPR, 2021)

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 (Proc. Natl. Acad. Sci. USA, 2021)

Graph Representation Learning for Drug Discovery. - Wengong Jin (2021)

Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design. - Wengong Jin; Jeremy Wohlwend; Regina Barzilay; Tommi S. Jaakkola (ICLR, 2022)

Antibody-Antigen Docking and Design via Hierarchical Structure Refinement. - Wengong Jin; Regina Barzilay; Tommi S. Jaakkola (ICML, 2022)

Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement. - Wengong Jin; Regina Barzilay; Tommi S. Jaakkola (CoRR, 2022)

Graph and Geometry Generative Modeling for Drug Discovery. - Minkai Xu; Meng Liu 015; Wengong Jin; Shuiwang Ji; Jure Leskovec; Stefano Ermon (KDD, 2023)

Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation. - Wengong Jin; Siranush Sarkizova; Xun Chen; Nir Hacohen; Caroline Uhler (NeurIPS, 2023)

RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matching. - Divya Nori; Wengong Jin (ICML, 2024)

SurfPro: Functional Protein Design Based on Continuous Surface. - Zhenqiao Song; Tinglin Huang; Lei Li 005; Wengong Jin (ICML, 2024)

Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substrates. - Zhenqiao Song; Yunlong Zhao 002; Wenxian Shi; Wengong Jin; Yang Yang 059; Lei Li 005 (ICML, 2024)

Protein-Nucleic Acid Complex Modeling with Frame Averaging Transformer. - Tinglin Huang; Zhenqiao Song; Rex Ying; Wengong Jin (NeurIPS, 2024)

Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions. - Xiaoran Jiao; Weian Mao; Wengong Jin; Peiyuan Yang; Hao Cheng 012; Chunhua Shen (CoRR, 2024)

Reaction-conditioned De Novo Enzyme Design with GENzyme. - Chenqing Hua; Jiarui Lu; Yong Liu; Odin Zhang; Jian Tang 005; Rex Ying; Wengong Jin; Guy Wolf; Doina Precup; Shuangjia Zheng (CoRR, 2024)

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; et al. (Nat., 2024)

Deep generative design of RNA aptamers using structural predictions. - Felix Wong; Dongchen He; Aarti Krishnan; Liang Hong; Alexander Z. Wang; Jiuming Wang; Zhihang Hu; Satotaka Omori; Alicia Li; Jiahua Rao; Qinze Yu; Wengong Jin; et al. (Nat. Comput. Sci., 2024)

Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions. - Xiaoran Jiao; Weian Mao; Wengong Jin; Peiyuan Yang; Hao Chen 012; Chunhua Shen (ICLR, 2025)

MiniFold: Simple, Fast, and Accurate Protein Structure Prediction. - Jeremy Wohlwend; Mateo Reveiz; Matt McPartlon; Axel Feldmann; Wengong Jin; Regina Barzilay (Trans. Mach. Learn. Res., 2025)

BindEnergyCraft: Casting Protein Structure Predictors as Energy-Based Models for Binder Design. - Divya Nori; Anisha Parsan; Caroline Uhler; Wengong Jin (CoRR, 2025)

Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching. - Tinglin Huang; Tianyu Liu 005; Mehrtash Babadi; Wengong Jin; Rex Ying (CoRR, 2025)