Recently, artificial intelligence (AI) for drug discovery has raised increasing interest in both the machine learning (ML) and computational chemistry/biology communities. The core task of AI for drug discovery is molecule representation learning, where the molecule knowledge can be naturally presented in different modalities: chemical formula, molecular graph, geometric conformation, knowledge base, biomedical literature, etc. In this talk, I would like to provide a perspective concentrating on molecule pretraining from topology, geometry, and textual description. Such a unified perspective paves the way for molecule representation interpretation as well as discovery tasks.
Biography
Shengchao Liu is a fourth-year Ph.D. student at Mila-UdeM. His research interests include representation learning (geometric learning, self-supervised learning, multi-task learning), deep generative modeling, and drug discovery modeling. To know more about his work, please check the website: https://chao1224.github.io/.
