Google Scholar.

Research Topics

  • Representation Learning and Statistical Modeling
    • structured/geometric/graph representation learning
    • self-supervised learning
    • multi-task learning
    • deep generative modeling
    • controllable/conditional deep generative modeling
  • Large Language Model
  • Molecule Discovery
    • small molecule
    • protein
    • material
    • genomics
  • ML and Physical Dynamics
    • classical molecular dynamics
    • ab-initio molecular dynamcis
    • learning dynamics
    • statistical learning

Preprints and Manuscripts

Conferences

  • CARE: a Benchmark Suite for the Classification and Retrieval of Enzymes
    Jason Yang, Ariane Mora, Shengchao Liu, Bruce J. Wittmann, Anima Anandkumar, Frances H. Arnold, Yisong Yue
    NeurIPS 2024
    [Paper] [ArXiv] [Code]

  • ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback
    Shengchao Liu*, Jiongxiao Wang*, Yijin Yang, Chengpeng Wang, Ling Liu, Hongyu Guo*, Chaowei Xiao*
    ICLR 2024
    [Paper] [Project Page] [ArXiv] [Code]
    [ICML SynS and ML Workshop 2023 Oral]

  • Improving Out-of-Domain Generalization with Domain Relations
    Huaxiu Yao, Xinyu Yang, Xinyi Pan, Shengchao Liu, Pang Wei Koh, Chelsea Finn
    ICLR 2024 Spotlight
    [Paper]

  • Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
    Shengchao Liu, Weitao Du, Yanjing Li, Zhuoxinran Li, Zhiling Zheng, Chenru Duan, Zhiming Ma, Omar Yaghi, Anima Anandkumar, Christian Borgs, Jennifer Chayes, Hongyu Guo, Jian Tang
    NeurIPS 2023
    [Paper] [ArXiv] [Code]

  • Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion
    Weitao Du, Jiujiu Chen, Xuecang Zhang, Zhiming Ma, Shengchao Liu
    NeurIPS 2023
    [Paper] [ArXiv] [Code]

  • GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning
    Haiteng Zhao, Shengchao Liu, Chang Ma, Hannan Xu, Jie Fu, Zhi-Hong Deng, Lingpeng Kong, Qi Liu
    NeurIPS 2023
    [Paper] [ArXiv] [Code]

  • Evaluating Self-Supervised Learned Molecular Graphs
    Hanchen Wang*, Jean Kaddour*, Shengchao Liu, Jian Tang, Joan Lasenby, Qi Liu
    NeurIPS 2023
    [ArXiv] [Code]
    [ICML pretraining workshop 2022] [ICML AI for science workshop 2022]

  • A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining
    Shengchao Liu*, Weitao Du*, Zhiming Ma, Hongyu Guo, Jian Tang
    ICML 2023
    [Project Page] [Paper] [ArXiv] [Code]

  • Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching
    Shengchao Liu, Hongyu Guo, Jian Tang
    ICLR 2023
    [Project Page] [Paper] [ArXiv] [Code]

  • Augmenting Message Passing by Retrieving Similar Graphs
    Dingmin Wang, Shengchao Liu, Hanchen Wang, Linfeng Song, Jian Tang, Le Song, Bernardo C. Grau, Qi Liu
    ECAI 2023
    [ArXiv]

  • Flaky Performances when Pretraining on Relational Databases
    Shengchao Liu, David Vazquez, Jian Tang, Pierre-Andre Noel
    AAAI-Student Abstract 2023
    [Paper] [ArXiv] [Code]
    [ICML pretraining workshop 2022]

  • Pre-training Molecular Graph Representation with 3D Geometry
    Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang
    ICLR 2022
    [Project Page] [Paper] [ArXiv] [Code] [Slides] [Poster]
    [NeurIPS SSL Workshop 2021]
    [ICLR GTRL Workshop 2022 Spotlight]

  • Structured Multi-task Learning for Molecular Property Prediction
    Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang
    AISTATS 2022
    [Project Page] [Paper] [ArXiv] [Code] [Poster]
    [NeurIPS AI4Science Workshop 2021]

  • Neural Sentence Ordering Based on Constraint Graphs
    Yutao Zhu, Kun Zhou, Jian-Yun Nie, Shengchao Liu, Zhicheng Dou
    AAAI 2021
    [Paper] [Code]

  • Bad Global Minima Exist and SGD Can Reach Them
    Shengchao Liu, Dimitris Papailiopoulos, Dimitris Achlioptas
    NeurIPS 2020
    [Paper] [Code] [Poster] [Video/Audio, NeurIPS 2020]
    [ICML Deep Learning Phenomena Workshop 2019 Oral]

  • Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement Learning
    Sai Krishna Gottipati*, Boris Sattarov*, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Karam M. J. Thomas, Simon Blackburn, Connor W. Coley, Jian Tang, Sarath Chandar, Yoshua Bengio
    ICML 2020
    [Paper][Code]

  • N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
    Shengchao Liu, Mehmet Furkan Demirel, Yingyu Liang
    NeurIPS 2019 Spotlight
    [Paper] [Code] [Slides][Poster]
    [NeurIPS MLMM Workshop 2018]

  • Loss-Balanced Task Weighting to Reduce Negative Transfer in Multi-Task Learning
    Shengchao Liu, Yingyu Liang, Anthony Gitter
    AAAI-Student Abstract 2019
    [Paper] [Appendix] [Code] [Poster]

  • Atomo: Communication-efficient Learning via Atomic Sparsification
    Hongyi Wang*, Scott Sievert*, Zachary Charles, Shengchao Liu, Dimitris Papailiopoulos, Stephen Wright
    NeurIPS 2018
    [Paper] [Code]

Journals

  • A Text-guided Protein Design Framework
    Shengchao Liu, Yanjing Li, Zhuoxinran Li, Anthony Gitter, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Arvind Ramanathan, Chaowei Xiao*, Jian Tang*, Hongyu Guo*, Anima Anandkumar*
    Nature Machine Intelligence 2025
    [Paper] [Project Page] [ArXiv] [Code]

  • Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled
    Shengchao Liu, Chengpeng Wang, Jiarui Lu, Weili Nie, Hanchen Wang, Zuoxinran Li, Bolei Zhou, Jian Tang
    Transactions on Machine Learning Research (TMLR) 2024
    [Paper] [Project Page] [ArXiv] [Code]
    [NeurIPS GLFrontiers Workshop 2022 Oral]

  • Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval
    Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, Anima Anandkumar
    Nature Machine Intelligence 2023
    [Paper] [Project Page] [ArXiv] [Code]
    [NeurIPS AI4Science Workshop 2022]

  • Shaping the Water Harvesting Behavior of Metal-Organic Frameworks Aided by Fine-Tuned GPT Models
    Zhiling Zheng, Ali H. Alawadhi, Saumil Chheda, S. Ephraim Neumann, Nakul Rampal, Shengchao Liu, Ha L. Nguyen, Yen-hsu Lin, Zichao Rong, J. Ilja Siepmann, Laura Gagliardi, Anima Anandkumar, Christian Borgs, Jennifer T. Chayes, and Omar M. Yaghi
    ACS, Journal of the American Chemical Society 2023
    [Paper] [Code]

  • Evaluating scalable supervised learning for synthesize-on-demand chemical libraries
    Moayad Alnammi, Shengchao Liu, Spencer S. Ericksen, Gene E. Ananiev, Andrew F. Voter, Song Guo, James L. Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter
    Journal of Chemical Information and Modeling 2023
    [Paper] [ChemRxiv] [Code]

  • Attentive Walk-Aggregating Graph Neural Networks
    Mehmet F. Demirel, Shengchao Liu, Siddhant Garg, Zhenmei Shi, Yingyu Liang
    ACS, Transactions on Machine Learning Research (TMLR) 2022
    [Paper] [ArXiv] [Code]

  • Learning Molecule Drug Function from Structure Representations with Deep Neural Networks or Random Forests
    Jesse G. Meyer, Shengchao Liu, Ian J. Miller, Anthony Gitter, Joshua J. Coon
    ACS, Journal of Chemical Information and Modeling 2019
    [Paper] [Code]

  • Practical model selection for prospective virtual screening
    Shengchao Liu*, Moayad Alnammi*, Spencer Ericksen, Andrew F. Voter, James L Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter
    ACS, Journal of Chemical Information and Modeling 2018
    [Paper] [Code]

Workshops

* indicates equal contribution.