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Geometric Deep Learning for Drug Discovery

IEEE Signal Processing Society via YouTube

Overview

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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 2 Class Central reviews

Start your review of Geometric Deep Learning for Drug Discovery

  • Profile image for Betsegaw Desealegn Zeleke
    Betsegaw Desealegn Zeleke
    Geometric Deep Learning (GDL) represents a transformative advancement in the application of artificial intelligence to drug discovery, as it extends traditional deep learning methods to non-Euclidean data structures such as graphs, manifolds, and three-dimensional molecular geometries. Through this online course, I gained a structured understanding of how GDL enables more biologically and chemically meaningful modeling of drugs, targets, and their interactions
  • 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.

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