Learning Binding Affinities via Fine-Tuning of Protein and Ligand Language Models

Accurate in silico prediction of protein-ligand binding affinity is essential for efficient hit identification in large molecular libraries. Commonly used structure-based methods such as docking often fail to rank compounds effectively, and free energy-based approaches, while accurate, are too computationally intensive for large-scale screening. Existing deep learning models struggle to generalize to new targets or drugs, and current evaluation methods often do not accurately reflect real-world performance. We introduce BALM, a deep learning framework that predicts binding affinity using pretrained protein and ligand language models. We also propose improved evaluation strategies with diverse data sets and metrics to assess model performance to new targets better. Using a curated version of BindingDB, BALM shows generalization to unseen drugs, scaffolds, and targets. In few-shot learning scenarios for targets such as USP7 and Mpro, it outperforms traditional machine learning and docking methods, including AutoDock Vina. Adoption of our target-based evaluation methods will allow a more stringent evaluation of machine learning-based scoring tools. Our binding affinity prediction framework shows good performance, is computationally efficient, and is highly adaptable within this evaluation setting, making it practical for early-stage drug discovery screening.