Tutorials

• Jian Tang, Fei Wang, Feixiong Cheng. Artificial Intelligence for Drug Discovery. KDD 2021.
• Jian Tang, Fei Wang, Feixiong Cheng. Artificial Intelligence for Drug Discovery. AAAI 2021.

Publications

• Zhaocheng Zhu, Chence Shi, Zuobai Zhang, Shengchao Liu, Minghao Xu, Xinyu Yuan, Yangtian Zhang, Junkun Chen, Huiyu Cai, Jiarui Lu, Chang Ma, Runcheng Liu, Louis-Pascal Xhonneux, Meng Qu, Jian Tang. TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery. In submission.

Pretrained Molecular Representations

• Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang. Pre-training Molecular Graph Representation with 3D Geometry. ICLR 2022.
• Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang. Multi-task Learning with Domain Knowledge for Molecular Property Prediction. AISTATS 2022.
• Minghao Xu, Hang Wang, Bingbing Ni, Hongyu Guo, Jian Tang. Self-supervised Graph-level Representation Learning with Local and Global Structure. ICML 2021.
• Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, Jian Tang. InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. ICLR 2020.

De Novo Molecule Design & Optimization

• Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Karam MJ Thomas, Simon Blackburn, Connor W Coley, Jian Tang, Sarath Chandar, Yoshua Bengio. Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning. ICML 2020.
• Chence Shi*, Minkai Xu*, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang. GraphAF: A Flow-based Autoregressive Model for Molecular Graph Generation. ICLR 2020.

Forward Reaction & Retrosynthesis Prediction

• Hangrui Bi*, Hengyi Wang*, Chence Shi, Connor Coley, Jian Tang, Hongyu Guo. Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction. ICML 2021.
• Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang. A Graph to Graphs Framework for Retrosynthesis Prediction. ICML 2020.

Molecular Conformation Prediction

• Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess Smidt, Yusu Wang, Jian Tang, Rafael Gómez-Bombarelli. Generative Coarse-Graining of Molecular Conformations. ICML 2022.
• Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang. GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation. ICLR 2022.
• Shitong Luo*, Chence Shi*, Minkai Xu, Jian Tang. Predicting Molecular Conformation via Dynamic Graph Score Matching. NeurIPS 2021.
• Chence Shi*, Shitong Luo*, Minkai Xu, Jian Tang. Learning Gradient Fields for Molecular Conformation Generation. ICML 2021.
• Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gomez-Bombarelli, Jian Tang. An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming. ICML 2021.
• Minkai Xu*, Shitong Luo*, Yoshua Bengio, Jian Peng, Jian Tang. Learning Neural Generative Dynamics for Molecular Conformation Generation. ICLR 2021.

Knowledge Graph Reasoning

• Zhaocheng Zhu, Mikhail Galkin, Zuobai Zhang, Jian Tang. Neural-Symbolic Models for Logical Queries on Knowledge Graphs. ICML 2022.
• Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux, Jian Tang. Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction. NeurIPS 2021.
• Meng Qu, Junkun Chen, Louis-Pascal Xhonneux, Yoshua Bengio, Jian Tang. RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs. ICLR 2021.
• Meng Qu, Jian Tang. Probabilistic Logic Neural Networks for Reasoning. NeurIPS 2019.
• Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang. RotatE: Knowledge graph embedding by relational rotation in complex space. ICLR 2019.