Comparative evaluation of 11 scoring functions for molecular docking

J Med Chem. 2003 Jun 5;46(12):2287-303. doi: 10.1021/jm0203783.


Eleven popular scoring functions have been tested on 100 protein-ligand complexes to evaluate their abilities to reproduce experimentally determined structures and binding affinities. They include four scoring functions implemented in the LigFit module in Cerius2 (LigScore, PLP, PMF, and LUDI), four scoring functions implemented in the CScore module in SYBYL (F-Score, G-Score, D-Score, and ChemScore), the scoring function implemented in the AutoDock program, and two stand-alone scoring functions (DrugScore and X-Score). These scoring functions are not tested in the context of a particular docking program. Instead, conformational sampling and scoring are separated into two consecutive steps. First, an exhaustive conformational sampling is performed by using the AutoDock program to generate an ensemble of docked conformations for each ligand molecule. This conformational ensemble is required to cover the entire conformational space as much as possible rather than to focus on a few energy minima. Then, each scoring function is applied to score this conformational ensemble to see if it can identify the experimentally observed conformation from all of the other decoys. Among all of the scoring functions under test, six of them, i.e., PLP, F-Score, LigScore, DrugScore, LUDI, and X-Score, yield success rates higher than the AutoDock scoring function. The success rates of these six scoring functions range from 66% to 76% if using root-mean-square deviation < or =2.0 A as the criterion. Combining any two or three of these six scoring functions into a consensus scoring scheme further improves the success rate to nearly 80% or even higher. However, when applied to reproduce the experimentally determined binding affinities of the 100 protein-ligand complexes, only X-Score, PLP, DrugScore, and G-Score are able to give correlation coefficients over 0.50. All of the 11 scoring functions are further inspected by their abilities to construct a descriptive, funnel-shaped energy surface for protein-ligand complexation. The results indicate that X-Score and DrugScore perform better than the other ones at this aspect.

MeSH terms

  • Algorithms
  • Chloramphenicol / chemistry
  • Chloramphenicol O-Acetyltransferase / chemistry
  • Diiodothyronines / chemistry
  • Ligands
  • Models, Molecular
  • Molecular Conformation
  • Prealbumin / chemistry
  • Protein Binding
  • Proteins / chemistry*
  • Quantitative Structure-Activity Relationship*
  • Thermodynamics


  • Diiodothyronines
  • Ligands
  • Prealbumin
  • Proteins
  • Chloramphenicol
  • 3,3'-diiodothyronine
  • Chloramphenicol O-Acetyltransferase