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

https://is1-ssl.mzstatic.com/image/thumb/Podcasts122/v4/42/4d/8b/424d8bd5-9fbd-b1e4-5461-106fbdb9f385/mza_6610988476650408252.jpg/600x600bb.jpg

Molecular Modelling and Drug Discovery

Valence Discovery

60 episodes

1 week ago

Welcome to this space dedicated to the M2D2 Talks co-organized by Valence Discovery and Mila - Quebec AI Institute. From applied research papers to open source projects, we're hoping to use these talks to help demystify AI for drug discovery and make the field more accessible for newcomers. M2D2 will bring our vibrant AI & drug discovery communities together and spark new perspectives, provoke discussions, and offer a safe space to share new ideas. For the best experience, please visit our YouTube channel where slides and video presentations can be referenced.

Science

RSS

All content for Molecular Modelling and Drug Discovery is the property of Valence Discovery and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.

Science

https://d3t3ozftmdmh3i.cloudfront.net/production/podcast_uploaded_nologo/27286797/27286797-1656636766469-f7c96f4311f76.jpg

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

Molecular Modelling and Drug Discovery

54 minutes 45 seconds

2 years ago

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.

If you enjoyed this talk, consider joining the Molecular Modeling and Drug Discovery (M2D2) talks live.

Also, consider joining the M2D2 Slack.

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

Twitter - Prudencio

Twitter - Therence

Twitter - Jonny

Twitter - Valence Discovery