Uncertainty quantification enables reliable deep learning for protein-ligand binding affinity prediction.

ABSTRACT: Deep learning (DL) algorithms have increasingly been applied to predict protein-ligand binding affinity, a critical step in drug design. Yet, many models still struggle to generalize to unseen data, and when coupled with the absence of confidence estimates for predictions, they hinder effective decision-making. To address these challenges, we thoroughly compare five uncertainty quantification methods: Deep Ensemble, Monte Carlo Dropout, Laplace approximation, Bayes by Backprop, and Evidential Neural Networks. Notably, Bayes by Backprop-applied for the first time in this study area-offers a novel and promising approach to uncertainty quantification. To ensure unbiased training and validation, we leverage the Leak-Proof PDBBind dataset and rigorously evaluate performance across multiple exte

SUBMITTER: Rayka M 

PROVIDER: S-EPMC12678490 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature