A Critical Assessment of Docking Programs and Scoring Functions

TLDR

Docking computationally samples small‑molecule conformations in protein binding sites, with scoring functions evaluating their complementarity. The study evaluated 10 docking programs and 37 scoring functions on eight proteins across three tasks: binding‑mode prediction, virtual‑screening for lead identification, and affinity rank‑ordering. All docking programs produced ligand conformations resembling crystal structures for at least one target, yet scoring functions poorly distinguished the crystallographic pose from docked poses; while docking programs identified active compounds from a pharmaceutically relevant decoy pool, none performed consistently across all targets, and none reliably predicted ligand binding affinity.

Abstract

Docking is a computational technique that samples conformations of small molecules in protein binding sites; scoring functions are used to assess which of these conformations best complements the protein binding site. An evaluation of 10 docking programs and 37 scoring functions was conducted against eight proteins of seven protein types for three tasks: binding mode prediction, virtual screening for lead identification, and rank-ordering by affinity for lead optimization. All of the docking programs were able to generate ligand conformations similar to crystallographically determined protein/ligand complex structures for at least one of the targets. However, scoring functions were less successful at distinguishing the crystallographic conformation from the set of docked poses. Docking programs identified active compounds from a pharmaceutically relevant pool of decoy compounds; however, no single program performed well for all of the targets. For prediction of compound affinity, none of the docking programs or scoring functions made a useful prediction of ligand binding affinity.