Graph Neural Networks for Drug Discovery
GNNs on molecular graphs are how modern pharma screens billions of compounds. This comprehensive curriculum is designed to give you hands-on experience with Graph Neural Networks for Drug Discovery.
About this Course
GNNs on molecular graphs are how modern pharma screens billions of compounds. This comprehensive curriculum is designed to give you hands-on experience with Graph Neural Networks for Drug Discovery. This Bioinformatics & ML Researchers curriculum is designed to give you hands-on experience and deep conceptual understanding.
Across 4 intensive modules, you'll tackle real-world challenges and build practical projects that reinforce your learning. By the end of this journey, you'll have the skills and proof of work to demonstrate your expertise.
What you'll learn
Master the core concepts of molecular graph representations & rdkit.
Gain hands-on experience with message passing neural networks (mpnn).
Understand the architecture behind 3d molecular conformations & equivariant gnns.
Implement production-grade virtual screening pipeline & generative design.
W1
Molecular Graph Representations & RDKit
Molecular Graph Representations & RDKit
Master the core concepts of molecular graph representations & rdkit.
3 videos•67m
3 readings
3 topics
1 homework
Topics
1.1
How to use edge features in Graph Neural Networks (and PyTorch Geometric)
SMILES Parsing & Atom Features
17 minutes
1.2
Lecture 11 - Graph Neural Networks (GNNs)
Adjacency Matrix & Graph Construction
31 minutes
1.3
Discovering New Molecules Using Graph Neural Networks by Rocío Mercado
Molecular Featurization Pipelines
19 minutes
References
Week 1: Molecular Graph Representations & RDKit
Week 2: Message Passing Neural Networks (MPNN)
Week 3: 3D Molecular Conformations & Equivariant GNNs
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