MoleculeKit: Machine Learning Methods for Molecular Property Prediction and Drug Discovery. - Zhengyang Wang; Meng Liu 015; Youzhi Luo; Zhao Xu 005; Yaochen Xie; Limei Wang; Lei Cai; Shuiwang Ji (CoRR, 2020)

Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence. - Qi Qi 006; Youzhi Luo; Zhao Xu 005; Shuiwang Ji; Tianbao Yang (NeurIPS, 2021)

Self-Supervised Learning of Graph Neural Networks: A Unified Review. - Yaochen Xie; Zhao Xu 005; Zhengyang Wang; Shuiwang Ji (CoRR, 2021)

Stochastic Optimization of Area Under Precision-Recall Curve for Deep Learning with Provable Convergence. - Qi Qi 006; Youzhi Luo; Zhao Xu 005; Shuiwang Ji; Tianbao Yang (CoRR, 2021)

Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks. - Meng Liu 015; Cong Fu 003; Xuan Zhang; Limei Wang; Yaochen Xie; Hao Yuan 001; Youzhi Luo; Zhao Xu 005; Shenglong Xu; Shuiwang Ji (CoRR, 2021)

Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs. - Zhao Xu 005; Youzhi Luo; Xuan Zhang; Xinyi Xu; Yaochen Xie; Meng Liu 015; Kaleb Dickerson; Cheng Deng; Maho Nakata; Shuiwang Ji (CoRR, 2021)

DIG: A Turnkey Library for Diving into Graph Deep Learning Research. - Meng Liu 015; Youzhi Luo; Limei Wang; Yaochen Xie; Hao Yuan 001; Shurui Gui; Haiyang Yu 005; Zhao Xu 005; Jingtun Zhang; Yi Liu 059; Keqiang Yan; Haoran Liu; et al. (J. Mach. Learn. Res., 2021)

Self-Supervised Representation Learning via Latent Graph Prediction. - Yaochen Xie; Zhao Xu 005; Shuiwang Ji (ICML, 2022)

Frontiers of Graph Neural Networks with DIG. - Shuiwang Ji; Meng Liu 015; Yi Liu 059; Youzhi Luo; Limei Wang; Yaochen Xie; Zhao Xu 005; Haiyang Yu 005 (KDD, 2022)

Advanced graph and sequence neural networks for molecular property prediction and drug discovery. - Zhengyang Wang; Meng Liu 015; Youzhi Luo; Zhao Xu 005; Yaochen Xie; Limei Wang; Lei Cai; Qi Qi 006; Zhuoning Yuan; Tianbao Yang; Shuiwang Ji (Bioinform., 2022)

Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian. - Haiyang Yu 005; Zhao Xu 005; Xiaofeng Qian; Xiaoning Qian; Shuiwang Ji (ICML, 2023)

A Text-guided Protein Design Framework. - Shengchao Liu; Yutao Zhu 001; Jiarui Lu; Zhao Xu 005; Weili Nie; Anthony Gitter; Chaowei Xiao; Jian Tang 005; Hongyu Guo; Anima Anandkumar (CoRR, 2023)

Self-Supervised Learning of Graph Neural Networks: A Unified Review. - Yaochen Xie; Zhao Xu 005; Jingtun Zhang; Zhengyang Wang; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

3D Molecular Geometry Analysis with 2D Graphs. - Zhao Xu 005; Yaochen Xie; Youzhi Luo; Xuan Zhang; Xinyi Xu; Meng Liu 015; Kaleb Dickerson; Cheng Deng; Maho Nakata; Shuiwang Ji (SDM, 2024)

Equivariant Graph Network Approximations of High-Degree Polynomials for Force Field Prediction. - Zhao Xu 005; Haiyang Yu 005; Montgomery Bohde; Shuiwang Ji (Trans. Mach. Learn. Res., 2024)

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. - Xuan Zhang; Limei Wang; Jacob Helwig; Youzhi Luo; Cong Fu 003; Yaochen Xie; Meng Liu 015; Yuchao Lin; Zhao Xu 005; Keqiang Yan; Keir Adams; Maurice Weiler; et al. (Found. Trends Mach. Learn., 2025)

A text-guided protein design framework. - Shengchao Liu; Yanjing Li; Zhuoxinran Li; Anthony Gitter; Yutao Zhu 001; Jiarui Lu; Zhao Xu 005; Weili Nie; Arvind Ramanathan; Chaowei Xiao; Jian Tang 005; Hongyu Guo; et al. (Nat. Mac. Intell., 2025)