YoVDO

Geometric Deep Learning for Drug Discovery

Offered By: IEEE Signal Processing Society via YouTube

Tags

Drug Discovery Courses Diversity Courses Causality Courses Mutual Information Courses Geometric Deep Learning Courses

Course Description

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

Related Courses

Graph Attention Networks - GNN Paper Explained
Aleksa Gordić - The AI Epiphany via YouTube
Detection of Objects in Cryo-Electron Micrographs Using Geometric Deep Learning
Institute for Pure & Applied Mathematics (IPAM) via YouTube
Physics-Inspired Learning on Graph - Michael Bronstein, PhD
Open Data Science via YouTube
Inverse Problems on Graphs with Geometric Deep Learning
APS Physics via YouTube
A Sheaf-based Approach to Graph Neural Networks
Applied Algebraic Topology Network via YouTube