More Accurate Binding Affinity Prediction Using Protein Homology and Ligand-Based Transfer Learning

Accurate and rapid prediction of protein-ligand binding affinities is critical for drug discovery, particularly when evaluating large chemical libraries or new drug molecules from high-throughput generative models. We present UCBbind, a hybrid framework that combines a similarity-based transfer module with a deep-learning-based prediction module, to efficiently estimate binding affinities of small molecules to target proteins. For each query protein-ligand pair, UCBbind transfers experimental data from highly similar reference pairs when available and applies the prediction module when no sufficiently similar reference exists. We benchmarked UCBbind on multiple datasets, including the CASF-2016 set, the HiQBind dataset post 2020, and the COVID Moonshot database. Our results show that UCBbind achieves state-of-the-art predictive performance, particularly for test entries with high similarity to well-characterized reference proteins and ligands, and can support downstream tasks such as binding site prediction and binder/nonbinder classification.