Molecule Representation Learning and Discovery: A Perspective from Topology, Geometry, and Textual Description | Shengchao Liu
Molecular Modelling and Drug Discovery17 Helmi 2023

Molecule Representation Learning and Discovery: A Perspective from Topology, Geometry, and Textual Description | Shengchao Liu

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our YouTube channel to see the presented slides.

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Abstract: Recently, artificial intelligence (AI) for drug discovery has raised increasing interest in both the machine learning (ML) and computational chemistry communities. The core problem 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.

Speaker: Shengchao Liu

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