Also in Google Scholar.

Preprints and Manuscripts

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

• Flaky Performances when Pre-Training on Relational Databases with a Plan for Future Characterization Efforts
  Shengchao Liu, David Vazquez, Jian Tang, Pierre-Andre Noel
  [ArXiv] [Code]
  [ICML pretraining workshop 2022]

• 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

• 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] [Slides] [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] [Slides] [Poster] [Video/Audio, NeurIPS 2020]
  [Oral, ICML Deep Learning Phenomena Workshop 2019]

• 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-SA 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]

Workshops

• Flaky Performances when Pre-Training on Relational Databases with a Plan for Future Characterization Efforts
  Shengchao Liu, David Vazquez, Jian Tang, Pierre-Andre Noel
  [ArXiv] [Code]
  [ICML pretraining workshop 2022]

• 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]

• 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
  ICLR Workshop on Geometrical and Topological Representation Learning 2022 (Spotlight)
  [ArXiv]

• 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
  [Paper]

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

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

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

• 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]

^* indicates equal contribution.