Explore geometric deep learning techniques for drug discovery in this 58-minute webinar presented by Jian Tang from MILA/HEC Montreal. Delve into various types of data, research problems, and graph neural networks used in the field. Learn about infograph, mutual information, and graph AF, along with experimental results in gold-directed molecule generation, constraint optimization, and retrosynthetic prediction. Gain insights into essential ideas, intuition, and TorDrug tasks. The presentation concludes with discussions on student advisors, questions, causality, and diversity in drug discovery research.
Overview
Syllabus
Introduction
Drug Discovery
Types of Data
Geometric Different Techniques
Research Problem
Graph Neural Networks
Infograph
Mutual Information
Graph AF
Experimental Results
Golddirected molecule generation
Constraint optimization
Retrosynthetic prediction
Essential idea
Intuition
TorDrug
Tasks
Student Advisors
Questions
Causality
Diversity
Taught by
IEEE Signal Processing Society
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Reviews
5.0 rating, based on 1 Class Central review
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This course provides good knowledge about the Drug discovery and research.
It also gives immense knowledge about the molecules, structural configuration and about structural specification.
