A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery. - Lei Xie 006; Li Xie 002; Philip E. Bourne (Bioinform., 2009)

Drug Discovery Using Chemical Systems Biology: Identification of the Protein-Ligand Binding Network To Explain the Side Effects of CETP Inhibitors. - Li Xie 002; Jerry Li 004; Lei Xie 006; Philip E. Bourne (PLoS Comput. Biol., 2009)

Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model. - Roger L. Chang; Li Xie 002; Lei Xie 006; Philip E. Bourne; Bernhard Ø. Palsson (PLoS Comput. Biol., 2010)

The Mycobacterium tuberculosis Drugome and Its Polypharmacological Implications. - Sarah L. Kinnings; Li Xie 002; Kingston H. Fung; Richard M. Jackson; Lei Xie 006; Philip E. Bourne (PLoS Comput. Biol., 2010)

Drug Discovery Using Chemical Systems Biology: Weak Inhibition of Multiple Kinases May Contribute to the Anti-Cancer Effect of Nelfinavir. - Li Xie 002; Thomas Evangelidis; Lei Xie 006; Philip E. Bourne (PLoS Comput. Biol., 2011)

An integrated workflow for proteome-wide off-target identification and polypharmacology drug design. - Thomas Evangelidis; Philip E. Bourne; Li Xie 002; Lei Xie 006 (BIBM Workshops, 2012)

Towards Structural Systems Pharmacology to Study Complex Diseases and Personalized Medicine. - Lei Xie 006; Xiaoxia Ge; Hepan Tan; Li Xie 002; Yinliang Zhang; Thomas Hart; Xiaowei Yang; Philip E. Bourne (PLoS Comput. Biol., 2014)

Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology. - Tian Cai; Li Xie 002; Muge Chen; Yang Liu; Di He 003; Shuo Zhang 010; Cameron Mura; Philip E. Bourne; Lei Xie 006 (CoRR, 2021)

Learning Universal and Robust 3D Molecular Representations with Graph Convolutional Networks. - Shuo Zhang 010; Yang Liu; Li Xie 002; Lei Xie 006 (CoRR, 2023)

Elucidation of genome-wide understudied proteins targeted by PROTAC-induced degradation using interpretable machine learning. - Li Xie 002; Lei Xie 006 (PLoS Comput. Biol., 2023)

End-to-end sequence-structure-function meta-learning predicts genome-wide chemical-protein interactions for dark proteins. - Tian Cai; Li Xie 002; Shuo Zhang 010; Muge Chen; Di He 003; Amitesh Badkul; Yang Liu; Hari Krishna Namballa; Michael Dorogan; Wayne W. Harding; Cameron Mura; Philip E. Bourne; et al. (PLoS Comput. Biol., 2023)