Scoring noncovalent protein-ligand interactions: a continuous differentiable function tuned to compute binding affinities

J Comput Aided Mol Des. 1996 Oct;10(5):427-40. doi: 10.1007/BF00124474.


Exploitation of protein structures for potential drug leads by molecular docking is critically dependent on methods for scoring putative protein-ligand interactions. An ideal function for scoring must exhibit predictive accuracy and high computational speed, and must be tolerant of variations in the relative protein-ligand molecular alignment and conformation. This paper describes the development of an empirically derived scoring function, based on the binding affinities of protein-ligand complexes coupled with their crystallographically determined structures. The function's primary terms involve hydrophobic and polar complementarity, with additional terms for entropic and solvation effects. The issue of alignment/conformation dependence was solved by constructing a continuous differentiable nonlinear function with the requirement that maxima in ligand conformation/alignment space corresponded closely to crystallographically determined structures. The expected error in the predicted affinity based on cross-validation was 1.0 log unit. The function is sufficiently fast and accurate to serve as the objective function of a molecular-docking search engine. The function is particularly well suited to the docking problem, since it has spatially narrow maxima that are broadly accessible via gradient descent.

Publication types

  • Comparative Study

MeSH terms

  • Bacterial Proteins / chemistry
  • Biotin / chemistry
  • Computer Simulation
  • Computer-Aided Design*
  • Databases, Factual
  • Drug Design*
  • Entropy
  • Ligands
  • Models, Molecular
  • Molecular Structure
  • Protein Binding
  • Protein Conformation
  • Proteins / chemistry*
  • Solvents
  • Streptavidin


  • Bacterial Proteins
  • Ligands
  • Proteins
  • Solvents
  • Biotin
  • Streptavidin