Google Scholar.

Research Topics

• Representation Learning and Distribution 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 molecules
  • proteins
  • materials
  • genomes
• ML and Physical Dynamics
  • classical molecular dynamics
  • ab-initio molecular dynamcis
  • learning dynamics

Preprints and Manuscripts

• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics
  Shengchao Liu^*, Weitao Du^*, Yanjing Li, Zhuoxinran Li, Vignesh Bhethanabotla, Nakul Rampal, Omar Yaghi, Christian Borgs, Anima Anandkumar^*, Hongyu Guo^*, Jennifer Chayes^*
  [Arxiv] [Project Page] [Code]
  [ICLR AI4DifferentialEquations Workshop 2024 Oral]
  Prev version: A Multi-Grained Group Symmetric Framework for Learning Protein-Ligand Binding Dynamics

• A Quatum-Inspired Neural Network for Geometric Modeling
  Weitao Du^*, Shengchao Liu^*, Hongyu Guo
  [ArXiv] [Code]

• 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^*
  [Project Page] [ArXiv] [Code]

• ChatPathway: Conversational Large Language Models for Biology Pathway Detection
  Yanjing Li, Hannan Xu, Haiteng Zhao, Hongyu Guo, Shengchao Liu
  [ArXiv] [Code]
  [NeurIPS GLFrontiers Workshop 2023 Oral]
  

Conferences

• 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

• 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
  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
  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
  ACS, 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
  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]

^* indicates equal contribution.