TrustAffinity: Accurate, Reliable and Scalable Out-of-Distribution Protein-Ligand Binding Affinity Prediction Using Trustworthy Deep Learning

Abstract

Accurate, reliable and scalable predictions of protein-ligand binding affinity have a great potential to accelerate drug discovery. Despite considerable efforts, three challenges remain: out-of-distribution (OOD) generalizations for understudied proteins or compounds from unlabeled protein families or chemical scaffolds, uncertainty quantification of individual predictions, and scalability to billions of compounds. We propose a sequence-based deep learning framework, TrustAffinity, to address aforementioned challenges. TrustAffinity synthesizes a structure-informed protein language model, efficient uncertainty quantification based on residue-estimation and novel uncertainty regularized optimization. We extensively validate TrustAffinity in multiple OOD settings. TrustAffinity significantly outperforms state-of-the-art computational methods by a large margin. It achieves a Pearson’s correlation between predicted and actual binding affinities above 0.9 with a high confidence and at least three orders of magnitude of faster than protein-ligand docking, highlighting its potential in real-world drug discovery. We further demonstrate TrustAffinity’s practicality through an Opioid Use Disorder lead discovery case study.