Multimodal out-of-distribution individual uncertainty quantification enhances binding affinity prediction for polypharmacology.

ABSTRACT: Polypharmacology, a single drug that targets multiple proteins, holds promise for addressing unmet medical needs. Achieving accurate, reliable and scalable predictions of protein-ligand binding affinity across multiple proteins is crucial to realizing the potential of polypharmacology. Machine learning offers a powerful tool for multitarget binding affinity prediction. However, three major challenges remain: generalizing predictions to out-of-distribution compounds that are structurally different from those in the training data; quantifying the uncertainty of predictions in out-of-distribution scenarios where the assumption underlying existing methods does not hold; and scaling to billions of compounds, which remains unattainable for current structure-based methods. Here, to overcome these

SUBMITTER: Badkul A 

PROVIDER: S-EPMC12798713 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature