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

Offered By: Valence Labs via YouTube

Tags

Drug Discovery Courses Artificial Intelligence Courses Machine Learning Courses Computational Chemistry Courses

Course Description

Overview

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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.

Syllabus

- Intro
- Molecular Data Structure and Representation
- Paper #1 - GraphMVP
- Paper #2 - GeoSSL
- Paper #3 - MoleculeSTM
- Zero-shot Text-Guided Molecule Editing
- Molecule Editing Examples
- Recap
- Q+A


Taught by

Valence Labs

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