Holder： Tianfan Fu（Rensselaer Polytechnic Institute）
Time：2024-05-07 10:00-11:00
Location：Siyuan Lecture Hall，Zhi Hua Building-225
Abstract:
Artificial intelligence (AI) has become woven into therapeutic discovery to accelerate drug discovery and development processes since the emergence of deep learning. For drug discovery, the goal is to identify drug molecules with desirable pharmaceutical properties. I will discuss our deep generative models that relax the discrete molecule space into a differentiable one and reformulate the combinatorial optimization problem into a differentiable optimization problem, which can be solved efficiently. On the other hand, drug development focuses on conducting clinical trials to evaluate the safety and effectiveness of the drug on human bodies. To predict clinical trial outcomes, I design deep representation learning methods to capture the interaction between multi-modal clinical trial features (e.g., drug molecules, patient information, disease information), which achieves a 0.847 F1 score in predicting phase III approval. Finally, I will present my future works in geometric deep learning for drug discovery and predictive models for drug development.
About the Speaker:
Tianfan Fu is a tenure-track assistant professor at Rensselaer Polytechnic Institute (RPI) Computer Science Department. He obtained his PhD degree at Georgia Institute of Technology in 2023. He obtained his B.S. and M.S. degrees at Shanghai Jiao Tong University in 2015 and 2018, respectively. His research interest lies in machine learning for drug discovery and development. Particularly, he is interested in generative models on both small-molecule & macro-molecule drug design and deep representation learning on drug development. The results of his research have been published in leading AI conferences, including AAAI, AISTATS, ICLR, IJCAI, KDD, NeurIPS, UAI, and top domain journals such as Nature, Cell Patterns, Nature Chemical Biology, and Bioinformatics. His work on clinical trial outcome prediction has been selected as the cover paper on Cell Patterns. In addition, Tianfan is an active community builder. He co-organized the first three AI4Science workshop on leading AI conferences (https://ai4sciencecommunity.github.io/); he co-founded Therapeutic Data Commons (TDC) initiative (https://tdcommons.ai/), an ecosystem with AI-solvable tasks, AI-ready datasets, and benchmarks in therapeutic science. Additional information is available at https://futianfan.github.io/.
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