Efficient Computational Workflow for Optimizing Antibody Binding Affinity Using Active Learning and Physics-Based Modeling

An active learning workflow that efficiently trains a deep learning model to learn energy functions for specific protein targets, combining the advantages of machine learning and physics-based computations to achieve more efficient antibody development.