Comparative accuracy analysis of molecular docking scoring functions using the CASF-2016 benchmark dataset
https://doi.org/10.29235/1561-8323-2025-69-6-454-461
Abstract
In this paper, a comparative analysis of the performance of AutoDock Vina, NNScore2, RF-Score-4, CENsible, HGScore, OnionNet-2, PIGNet2, and PLANET scoring functions designed to predict the binding affinity of small molecules to a target protein based on molecular docking data was made using the CASF-2016 benchmark dataset. The study has shown that the deep learning scoring functions PLANET and OnionNet-2 demonstrate the highest accuracy, effectively predicting the affinity of the ligand to the molecular target and increasing the reliability of identifying compound candidates with high potential for inhibitory activity. The obtained data indicate that both PLANET and OnionNet-2 can be used in computational protocols of molecular docking for subsequent calculation of the exponential consensus rank for each ligand and reliable selection of the most probable inhibitors of a given therapeutic target.
Keywords
scoring functions,
binding affinity prediction,
CASF-2016,
virtual screening,
molecular docking,
proteinligand interactions,
machine learning,
drug discovery
Supporting agencies: This work was supported by grants from the Belarusian Republican Foundation for Fundamental Research (project Ф24КИ-001), The International Science and Technology Center (ISTC, project PR150), and the Consortium and the Drug Resistant Tuberculosis Portal Program (https:// tbportals.niaid.nih.gov).
About the Authors
K. V. Furs
United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus
Belarus
Furs Kanstantsin V. – Junior Researcher
6, Surganov Str., 220012, Minsk
Y. V. Laikou
United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus
Belarus
Laikou Yan V. – Junior Researcher
6, Surganov Str., 220012, Minsk
H. V. Hanchar
United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus
Belarus
Hanchar Hanna V. – Junior Researcher
6, Surganov Str., 220012, Minsk
A. V. Tuzikov
United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus
Belarus
Tuzikov Alexander V. – Corresponding Member, D. Sc. (Physics and Mathematics), Professor, Head of the Laboratory
6, Surganov Str., 220012, Minsk
A. M. Andrianov
Institute of Bioorganic Chemistry of the National Academy of Sciences of Belarus
Belarus
Belarus
Andrianov Alexander M. – D. Sc. (Chemistry), Professor, Chief Researcher
5/2, Kuprevich Str., 220084, Minsk
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