Preprints and Manuscripts

  • Pre-training Molecular Graph Representation with 3D Geometry
    Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang
    [ArXiv] [Code]

  • An Analysis of Attentive Walk-Aggregating Graph Neural Networks
    Mehmet F. Demirel, Shengchao Liu, Siddhant Garg, Yingyu Liang
    [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]

  • Interpreting Molecular Space with Deep Generative Models
    Yuanqi Du, Xian Liu, Shengchao Liu, Bolei Zhou
    [PDF] [Code]

Conferences

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

  • Bad Global Minima Exist and SGD Can Reach Them
    Shengchao Liu, Dimitris Papailiopoulos, Dimitris Achlioptas
    NeurIPS 2020
    [PDF] [Code] [Slides] [Poster] [Video, ICML Workshop] [Video/Audio, NeurIPS]

  • 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
    [PDF][Code]

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

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

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

Journals

  • 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
    [PDF] [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
    [PDF] [Code]

Workshops

  • Pre-training Molecular Graph Representation with 3D Geometry – Rethinking Self-Supervised Learning on Structured Data
    Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang
    Self-Supervised Learning - Theory and Practice, NeurIPS 2021 Workshop
    [ArXiv] [Code]

  • Multi-task Learning with Domain Knowledge for Molecular Property Prediction
    Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang
    AI for Science: Mind the Gaps, NeurIPS 2021 Workshop
    [PDF] [Code]

  • Interpreting Molecular Space with Deep Generative Models
    Yuanqi Du, Xian Liu, Shengchao Liu, Bolei Zhou
    ELLIS Machine Learning for Molecule Discovery Workshop 2021 (Oral)
    [PDF] [Code]

  • Structured Multi-View Representations for Drug Combinations
    Shengchao Liu*, Andreea Deac*, Zhaocheng Zhu, Jian Tang
    Machine Learning for Molecules Workshop, NeurIPS 2020
    [PDF] [Poster]

  • Bad Global Minima Exist and SGD Can Reach Them
    Shengchao Liu, Dimitris Papailiopoulos, Dimitris Achlioptas
    Identifying and Understanding Deep Learning Phenomena Workshop, ICML 2019 (Oral)
    [PDF][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
    Great Lake Bioinformatics (GLBIO) 2019
    [PDF] [Code]

  • N-Gram Graph, A Novel Molecule Representation
    Shengchao Liu, Thevaa Chandereng, Yingyu Liang
    Machine Learning for Molecules and Materials Workshop, NeurIPS 2018
    [PDF][Poster]

  • A Novel Molecule Structure Learning Method for Drug Discovery
    Shengchao Liu, Thevaa Chandereng
    Midwest Biopharamceutical Statistics Workshop 2018

Symposiums

  • Loss-Balanced Task Weighting to Reduce Negative Transfer in Multi-Task Learning
    Shengchao Liu, Yingyu Liang, Anthony Gitter
    Third Midwest Machine Learning Symposium 2019
    [PDF]

  • N-Gram Graph, A Novel Molecule Representation
    Shengchao Liu, Thevaa Chandereng, Mehmet Furkan Demirel, Yingyu Liang
    Third Midwest Machine Learning Symposium 2019
    [PDF]

  • A Tool for Simulation in Adaptive Bayesian Clinical Trial
    Thevaa Chandereng, Donald Musgrove, Shengchao Liu, Tarek Haddad, Rick Chappell
    Twenty-fifth Annual Biopharmaceutical Applied Statistics Symposium 2018
    [Poster]

  • An Order Invariant Structure Learning Method for Molecule Classification
    Shengchao Liu, Thevaa Chandereng, Yingyu Liang
    Second Midwest Machine Learning Symposium 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
    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]

Presentations

Thesis

  • Master’s Thesis
    Exploration on Deep Drug Discovery: Representation and Learning
    [PDF][Code 1][Code 2][Code 3]

Awards and Honors

  • Travel Award, NeurIPS 2018
  • Travel Award, Midwest Biopharamceutical Statistics Workshop 2018

* indicates co-first authors.