Introduction to Graph Neural Networks and Their Application in Drug Discovery

Dive into the world of Graph Neural Networks (GNNs) and their applications in drug discovery with this comprehensive workshop led by Ali Madani, Head of Machine Learning at Cyclica Incorporation. Learn how graphs serve as powerful representations for biological and chemical entities in health-related problems, including protein structures, small molecule interactions, and gene relationships in biological pathways. Explore the limitations of classical machine learning and deep learning models when applied to graphs, and discover how GNNs are specifically designed to optimize graph-based tasks. Gain insights into various GNN applications in healthcare, from classifying graph nodes to predicting edge properties. Get hands-on experience building GNN models in Python and work on a practical healthcare application. This 1-hour and 52-minute workshop, presented by the Toronto Machine Learning Series (TMLS), offers a unique opportunity to understand the intersection of graph theory, machine learning, and drug discovery from an expert in computational biology and machine learning.

Workshop Introduction to Graph Neural Network and Its Application in Drug Discovery