Preprints and Manuscripts

ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback
Shengchao Liu^*, Jiongxiao Wang^*, Yijin Yang, Chengpeng Wang, Ling Liu, Hongyu Guo, Chaowei Xiao
[Project Page] [ArXiv] [Code]
[ICML SynS and ML Workshop 2023 Oral]
A Text-guided Protein Design Framework
Shengchao Liu, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Anthony Gitter, Chaowei Xiao, Jian Tang, Hongyu Guo, Anima Anandkumar
[Project Page] [ArXiv] [Code]
Unsupervised Discovery of Steerable Factors in Graphs
Shengchao Liu, Chengpeng Wang, Jiarui Lu, Weili Nie, Hanchen Wang, Bolei Zhou, Jian Tang
[Project Page] [ArXiv] [Code]
[NeurIPS GLFrontiers Workshop 2022 Oral]

Conferences

Molecule Joint Auto-Encoding: Self-Supervised Learning of 2D and 3D Trajectories
Weitao Du, Jiujiu Chen, Xuecang Zhang, Zhiming Ma, Shengchao Liu
NeurIPS 2023
[Paper] [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
[ArXiv] [Code]
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 Datasets and Benchmarks 2023
[ArXiv] [Code]
Evaluating Self-Supervised Learned Molecular Graphs
Hanchen Wang^*, Jean Kaddour^*, Shengchao Liu, Jian Tang, Joan Lasenby, Qi Liu
NeurIPS Datasets and Benchmarks 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

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]
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.