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)

GraphDF: A Discrete Flow Model for Molecular Graph Generation. - Youzhi Luo; Keqiang Yan; Shuiwang Ji (ICML, 2021)

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)

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)

An Autoregressive Flow Model for 3D Molecular Geometry Generation from Scratch. - Youzhi Luo; Shuiwang Ji (ICLR, 2022)

Generating 3D Molecules for Target Protein Binding. - Meng Liu 015; Youzhi Luo; Kanji Uchino; Koji Maruhashi; 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)

Learning Fair Graph Representations via Automated Data Augmentations. - Hongyi Ling; Zhimeng Jiang; Youzhi Luo; Shuiwang Ji; Na Zou 001 (ICLR, 2023)

Automated Data Augmentations for Graph Classification. - Youzhi Luo; Michael McThrow; Wing Yee Au 002; Tao Komikado; Kanji Uchino; Koji Maruhashi; Shuiwang Ji (ICLR, 2023)

Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction. - Yuchao Lin; Keqiang Yan; Youzhi Luo; Yi Liu 059; Xiaoning Qian; Shuiwang Ji (ICML, 2023)

Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization. - Shurui Gui; Meng Liu 015; Xiner Li; Youzhi Luo; Shuiwang Ji (NeurIPS, 2023)

Towards Symmetry-Aware Generation of Periodic Materials. - Youzhi Luo; Chengkai Liu; Shuiwang Ji (NeurIPS, 2023)

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules. - Haiyang Yu 005; Meng Liu 015; Youzhi Luo; Alex Strasser; Xiaofeng Qian; Xiaoning Qian; Shuiwang Ji (NeurIPS, 2023)

Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization. - Xiner Li; Shurui Gui; Youzhi Luo; Shuiwang Ji (CoRR, 2023)

Graph Structure Extrapolation for Out-of-Distribution Generalization. - Xiner Li; Shurui Gui; Youzhi Luo; Shuiwang Ji (ICML, 2024)

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)

Geometry Informed Tokenization of Molecules for Language Model Generation. - Xiner Li; Limei Wang; Youzhi Luo; Carl Edwards; Shurui Gui; Yuchao Lin; Heng Ji 001; Shuiwang Ji (CoRR, 2024)

Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation. - Haoran Liu; Youzhi Luo; Tianxiao Li; James Caverlee; Martin Renqiang Min (AAAI, 2025)

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)