Also in Google Scholar.

Preprints and Manuscripts

  • 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
    [Project Page] [ArXiv] [Code]

  • GraphCG: Unsupervised Discovery of Steerable Factors in Graphs
    Shengchao Liu, Chengpeng Wang, Weili Nie, Hanchen Wang, Jiarui Lu, Bolei Zhou, Jian Tang
    [Project Page] [ArXiv] [Code]
    [NeurIPS GLFrontiers Workshop 2022 Oral]

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

  • Augmenting Message Passing by Retrieving Similar Graphs
    Dingmin Wang, Shengchao Liu, Hanchen Wang, Linfeng Song, Jian Tang, Song Le, Bernardo Cuenca Grau, Qi Liu
    [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
    [ChemRxiv] [Code]

Conferences

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

  • Flaky Performances when Pretraining on Relational Databases
    Shengchao Liu, David Vazquez, Jian Tang, Pierre-Andre Noel
    AAAI-Student Abstract 2023
    [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

  • 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]

  • Practical model selection for virtual chemical screening
    Shengchao Liu*, Moayad Alnammi*, Spencer Ericksen, Andrew F. Voter, James L Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter
    Morgridge Institute for Research: Scientific Advisory Board 2018
    [Poster]

  • Practical model selection for virtual chemical screening
    Shengchao Liu*, Moayad Alnammi*, Spencer Ericksen, Andrew F. Voter, James L Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter
    Center for Predictive Computational Phenotyping, Third Annual Retreat 2018
    [Poster]

  • Comprehensive Benchmarking for Label-Free Quantitative Proteomics
    Thevaa Chandereng*, Shengchao Liu*, John Denu, Anthony Gitter, James Dowell
    US HUPO 2018
    [Poster]

  • Scrutinizing Deep Learning: A Virtual Screening Case Study
    Shengchao Liu*, Moayad Alnammi*, Scott A. Wildman, Spencer Ericksen, Haozhen Wu, Andrew F. Voter, James L Keck, F. Michael Hoffmann, Anthony Gitter
    Morgridge Institute for Research: Scientific Advisory Board 2017
    [Abstract] [Poster]

  • Scrutinizing Deep Learning: A Virtual Screening Case Study
    Shengchao Liu*, Moayad Alnammi*, Scott A. Wildman, Spencer Ericksen, Haozhen Wu, Andrew F. Voter, James L Keck, F. Michael Hoffmann, Anthony Gitter
    Center for Predictive Computational Phenotyping, Third Annual Retreat 2017
    [Abstract] [Poster] –>

* indicates equal contribution.