Geometric Deep Learning for Drug Discovery

Overview

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.

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

Reviews

5.0 rating, based on 1 Class Central review

Start your review of Geometric Deep Learning for Drug Discovery

Ayush Rajesh Ghosalkar

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.

Taught by

Geometric Deep Learning for Protein Understanding and Design

Machine Learning in Drug Discovery - Lightning Talks

Never Stop Learning.