A novel scoring function for molecular docking

Abstract

We present a novel scoring function for docking of small molecules to protein binding sites. The scoring function is based on a combination of two main approaches used in the field, the empirical and knowledge-based approaches. To calibrate the scoring function we used an iterative procedure in which a ligand's position and its score were determined self-consistently at each iteration. The scoring function demonstrated superiority in prediction of ligand positions in docking tests against the commonly used Dock, FlexX and Gold docking programs. It also demonstrated good accuracy of binding affinity prediction for the docked ligands.

LigScore: a novel scoring function for predicting binding affinities

“…Empirical scoring functions [19][20][21][22][23][24][25][26][27] consist of a set of terms characterizing various aspects of protein-ligand interactions such as energies of hydrogen bonding, steric interactions, lipophilic interactions, solvation, and entropic effects, and are therefore somewhat related to force-field based scoring functions. These descriptor terms are then usually combined into an overall scoring function by applying linear regression to the descriptor coefficients to fit binding affinity data of a set of protein-ligand complexes.…”

Neighborhood Behavior: Validation of Two‐Dimensional Molecular Similarity as a Predictor of Similar Biological Activities and Docking Scores

“…We also applied an in-house docking program with a scoring function combining both empirical and knowledge-based approaches [22,23] to obtain calculated binding energies (docking scores) for our datasets. Protein Data Bank (PDB) [24] entries 1eb2, 1ets, 1fjs, and 1gjc were used for trypsin, thrombin, fXa, and uPA docking, respectively.…”

Docking and Scoring for Structure‐based Drug Design

“…Other knowledge-based potentials were introduced [70, 81,102,133,200,213,221,306], mostly differing in the specific functional form of the atom pair potential or the size of the training dataset. Gohlke introduced the DrugScore [102] function with an additional nonpolar surface dependent single atom term to reflect the hydrophobic collapse.…”