Molecule Representation Learning: A Perspective from Topology, Geometry, and Textual Description

Explore molecule representation learning from the perspectives of topology, geometry, and textual description in this 55-minute conference talk by Shengchao Liu from Valence Labs. Delve into the core problem of AI for drug discovery, focusing on molecule pretraining techniques. Learn about three key papers: GraphMVP, GeoSSL, and MoleculeSTM. Discover the potential of zero-shot text-guided molecule editing and examine practical examples. Gain insights into molecular data structures, representations, and their applications in AI-driven drug discovery. Conclude with a comprehensive recap and engage in a Q&A session to deepen your understanding of this cutting-edge field.