Open AccessA Diverse Benchmark Based on 3D Matched Molecular Pairs for Validating Scoring Functions
Author(s) -
Lena Kalinowsky,
Júlia Wéber,
Shantheya Balasupramaniam,
Knut Baumann,
Ewgenij Proschak
Publication year - 2018
Publication title -
acs omega
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.779
H-Index - 40
ISSN - 2470-1343
DOI - 10.1021/acsomega.7b01194
Subject(s) - benchmarking , benchmark (surveying) , computer science , binding affinities , data mining , set (abstract data type) , docking (animal) , affinities , artificial intelligence , computational biology , machine learning , biology , chemistry , stereochemistry , genetics , medicine , receptor , nursing , geodesy , marketing , business , programming language , geography
The prediction of protein-ligand interactions and their corresponding binding free energy is a challenging task in structure-based drug design and related applications. Docking and scoring is broadly used to propose the binding mode and underlying interactions as well as to provide a measure for ligand affinity or differentiate between active and inactive ligands. Various studies have revealed that most docking software packages reliably predict the binding mode, although scoring remains a challenge. Here, a diverse benchmark data set of 99 matched molecular pairs (3D-MMPs) with experimentally determined X-ray structures and corresponding binding affinities is introduced. This data set was used to study the predictive power of 13 commonly used scoring functions to demonstrate the applicability of the 3D-MMP data set as a valuable tool for benchmarking scoring functions.