Normalized Protein-Ligand Distance Likelihood Score for End-to-End Blind Docking and Virtual Screening.

ABSTRACT: Molecular Docking is a critical task in structure-based virtual screening. Recent advancements have showcased the efficacy of diffusion-based generative models for blind docking tasks. However, these models do not inherently estimate protein-ligand binding strength thus cannot be directly applied to virtual screening tasks. Protein-ligand scoring functions serve as fast and approximate computational methods to evaluate the binding strength between the protein and ligand. In this work, we introduce normalized mixture density network (NMDN) score, a deep learning (DL)-based scoring function learning the probability density distribution of distances between protein residues and ligand atoms. The NMDN score addresses limitations observed in existing DL scoring functions and performs robustly i

SUBMITTER: Xia S 

PROVIDER: S-EPMC11815853 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature