Binding Affinity Prediction with Machine Learning-Based Docking - Lab 2
Offered By: Valence Labs via YouTube
Course Description
Overview
Explore a recorded lab session from the 2024 Machine Learning for Drug Discovery Summer School hosted at Mila, focusing on binding affinity prediction using machine learning-based docking techniques. Learn from speakers Stephan Thaler and Cristian Gabellini as they guide you through the intricacies of this cutting-edge approach in drug discovery. Gain insights into how machine learning algorithms can be applied to predict the binding affinity between molecules, a crucial step in the drug development process. Discover the potential of ML-based docking in revolutionizing the field of computational chemistry and its applications in pharmaceutical research. Connect with the speakers and engage with the content through the Valence Labs portal for a more interactive experience.
Syllabus
Lab 2 - Binding Affinity Prediction with ML Based Docking
Taught by
Valence Labs
Related Courses
Anthony Bak - Spaces of Shapes Persistent Homology for Drug DiscoveryApplied Algebraic Topology Network via YouTube Molecular Docking with AutoDock VINA - Script-Based Method for Multiple Ligands
Bioinformatics With BB via YouTube Cyclica Recursion - Innovations in Drug Discovery
Fields Institute via YouTube Methods for Computational Biology and Drug Discovery
Materials Cloud via YouTube AI-Aided Design of Novel Inhibitors Against SARS-CoV-2
Toronto Machine Learning Series (TMLS) via YouTube