Publications

Yuanqi Du*, Arian R. Jamasb*, Jeff Guo*, Tianfan Fu, Charles Harris, Yingheng Wang, Chenru Duan, Pietro Liò, Philippe Schwaller, Tom L. Blundell. Machine Learning-Aided Generative Molecular Design. Accepted by Nature Machine Intelligence (NMI), 2024.
Yingzhou Lu*, Tianyi Chen*, Nan Hao, Capucine Van Rechem, Jintai Chen, Tianfan Fu. Uncertainty Quantification and Interpretability for Clinical Trial Approval Prediction. Health Data Science, 2024.

Hanchen Wang*, Tianfan Fu*, Yuanqi Du*, Wenhao Gao+, Kexin Huang+, Ziming Liu+, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, Anima Anandkumar, Karianne Bergen, Carla P. Gomez, Shirley Ho, Pushmeet Kohli, Joan Lasenby, Jure Leskovec, Tie-Yan Liu, Arjun Manrai, Debora Marks, Bharath Ramsundar, Le Song, Jimeng Sun, Jian Tang, Petar Velickovic, Max Welling, Linfeng Zhang, Connor Coley, Yoshua Bengio, Marinka Zitnik: Scientific Discovery in the Age of Artificial Intelligence, Nature, 2023. [paper] *: co-first author. +: co-second author, alphabetical order.
Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, YuQing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K Joshi, Simon V Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik Bekkers, Michael Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, Shuiwang Ji. Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. Preprint, 2023. [paper]
Tao Feng, Pengcheng Xu, Tianfan Fu,, Siddhartha Laghuvarapu, Jimeng Sun: Molecular De Novo Design through Transformer-based Reinforcement Learning. PrePrint [paper]
Namkyeong Lee, Heewoong Noh, Gyoung S. Na, Tianfan Fu, Jimeng Sun, Chanyoung Park: Stoichiometry Representation Learning with Polymorphic Crystal Structures. NeurIPS 2023 AI for Science Workshop. [paper]
Zifeng Wang, Brandon Theodorou, Tianfan Fu, Cao Xiao, Jimeng Sun: PyTrial: A Comprehensive Platform for Artificial Intelligence for Drug Development. [paper]

Wenhao Gao*, Tianfan Fu*, Jimeng Sun, Connor W. Coley. Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization. Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks. [paper] [code]
Tianfan Fu*, Wenhao Gao*, Connor W. Coley, Jimeng Sun. Reinforced Genetic Algorithm for Structure-based Drug Design. Neural Information Processing Systems (NeurIPS) 2022. [paper] [code]
Kexin Huang*, Tianfan Fu*, Wenhao Gao*, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun, Marinka Zitnik: Artificial intelligence foundation for therapeutic science. Nature Chemical Biology 2022. [paper] [code]
Tianfan Fu, Jimeng Sun. SIPF: Sampling Method for Inverse Protein Folding. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2022). [paper]
Tianfan Fu, Jimeng Sun. Antibody Complementarity Determining Regions (CDRs) design using Constrained Energy Model. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2022). [paper]
Tianfan Fu, Wenhao Gao*, Cao Xiao, Jacob Yasonik, Connor W. Coley, Jimeng Sun. Differentiable Scaffolding Tree for Molecular Optimization. International Conference on Learning Representation (ICLR), 2022. [paper] [code]
Tianfan Fu, Kexin Huang, Cao Xiao, Lucas M. Glass, Jimeng Sun. HINT: Hierarchical Interaction Network for Clinical Trial Outcome Prediction. Cell Patterns, 2022. [paper] [code] [patent]

Kexin Huang*, Tianfan Fu*, Wenhao Gao*, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun, Marinka Zitnik: Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development. Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks. [paper] [code]
Tianfan Fu, Cao Xiao, Cheng Qian, Lucas Glass, Jimeng Sun: Probabilistic and Dynamic Molecule-Disease Interaction Modeling for Drug Discovery. The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2021). [paper]
Tianfan Fu, Cao Xiao, Xinhao Li, Lucas Glass, Jimeng Sun: MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization. Association for the Advancement of Artificial Intelligence (AAAI) 2021. [paper] [code]
Tianfan Fu, Cao Xiao, Lucas Glass, Jimeng Sun: MOLER: Incorporate Molecule-Level Reward to Enhance Deep Generative Model for Molecule Optimization. IEEE Transactions on Knowledge and Data Engineering (TKDE) 2021. [paper]

Tianfan Fu, Cao Xiao, Jimeng Sun: CORE: Automatic Molecule Optimization Using Copy and Refine Strategy. Association for the Advancement of Artificial Intelligence (AAAI) 2020. [paper] [code]
Kexin Huang, Tianfan Fu, Lucas Glass, Marinka Zitnik, Cao Xiao, Jimeng Sun: DeepPurpose: a deep learning library for drug-target interaction prediction and applications to repurposing and screening. Bioinformatics 2020. [paper] [code]

Tianfan Fu*, Tian Gao*, Cao Xiao, Tengfei Ma, Jimeng Sun: PEARL: Prototype Learning via Rule Learning. ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB) 2019. [paper] [code]
Tianfan Fu*, Trong Nghia Hoang*, Cao Xiao, Jimeng Sun: DDL: Deep Dictionary Learning for Predictive Phenotyping. International Joint Conferences on Artificial Intelligence (IJCAI 2019). [paper] [code]

Tianfan Fu, Cheng Zhang, Stephan Mandt: Continuous Word Embedding Fusion via Spectral Decomposition. The SIGNLL Conference on Natural Language Learning (CoNLL 2018). [paper] [Supplementary] [code]

Tianfan Fu, Zhihua Zhang: CPSG-MCMC: Clustering-Based Preprocessing method for Stochastic Gradient MCMC. Artificial Intelligence and Statistics (AISTATS) 2017: 841-850. [paper] [supplementary]

Tianfan Fu, Luo Luo, Zhihua Zhang: Quasi-Newton Hamiltonian Monte Carlo. Uncertainty in Artificial Intelligence (UAI) 2016. [paper]
Wei Li, Tianfan Fu, Hanxu You, Jie Zhu, Ning Chen: Feature sparsity analysis for i-vector based speaker verification. Speech Communication 80: 60-70 (2016). [paper]

Yuan Liu, Yanmin Qian, Nanxin Chen, Tianfan Fu, Ya Zhang, Kai Yu: Deep feature for text-dependent speaker verification. Speech Communication 73: 1-13 (2015). [paper]
Wei Li, Tianfan Fu, Jie Zhu: An improved i-vector extraction algorithm for speaker verification. EURASIP J. Audio, Speech and Music Processing 2015: 18. [paper]

Yuan Liu, Tianfan Fu, Yuchen Fan, Yanmin Qian, Kai Yu: Speaker verification with deep features. International Joint Conference on Neural Networks (IJCNN) 2014: 747-753. [paper]
Tianfan Fu, Yanmin Qian, Yuan Liu, Kai Yu: Tandem deep features for text-dependent speaker verification. INTERSPEECH 2014: 1327-1331. [paper]