Explore a series of lightning talks from selected poster presenters at the Broad Institute, covering cutting-edge applications of machine learning in drug discovery. Delve into Joshua Meier's discussion on antibody optimization using ML predictions for binding affinity and naturalness. Learn about Divya Nori's research on de novo PROTAC design utilizing graph-based deep generative models. Discover RocÃo Mercado's insights on meta-learning for optimizing small molecule binders. Examine Xuetao Shi's presentation on the Protein-Fragment Interaction Graph Database. Conclude with a Q&A session to gain further understanding of these innovative approaches in computational biology and drug development.
Machine Learning in Drug Discovery - Lightning Talks
Overview
Syllabus
Talk 1: Antibody optimization enabled by ML predictions of binding affinity and naturalness; Joshua Meier, Absci
Talk 2: De novo PROTAC design using graph-based deep generative models; Divya Nori, MIT
Talk 3: Meta-learning for optimization of small molecule binders; RocÃo Mercado, Department of Chemical Engineering, MIT
Talk 4: Protein-Fragment Interaction Graph Database;
Q&A
Taught by
Broad Institute
