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On importance of explicit account of non-complementary contacts in scoring functions

Arslan Ramilevich Shaimardanov 1
Arslan Ramilevich Shaimardanov
,  , 
Vladimir Alexandrovich Palyulin 1
Vladimir Alexandrovich Palyulin
1 Department of Chemistry, M.V. Lomonosov Moscow State University, Moscow, Russian Federation
10.1016/j.mencom.2023.10.021
Published 2023-10-18
Communication , Volume 33, Issue 6, 802-805
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Shaimardanov A. R., Shulga D. A., Palyulin V. A. On importance of explicit account of non-complementary contacts in scoring functions // Mendeleev Communications. 2023. Vol. 33. No. 6. pp. 802-805.
GOST all authors (up to 50) Copy
Shaimardanov A. R., Shulga D. A., Palyulin V. A. On importance of explicit account of non-complementary contacts in scoring functions // Mendeleev Communications. 2023. Vol. 33. No. 6. pp. 802-805.
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TY - JOUR
DO - 10.1016/j.mencom.2023.10.021
UR - https://mendcomm.colab.ws/publications/10.1016/j.mencom.2023.10.021
TI - On importance of explicit account of non-complementary contacts in scoring functions
T2 - Mendeleev Communications
AU - Shaimardanov, Arslan Ramilevich
AU - Shulga, Dmitry Alexandrovich
AU - Palyulin, Vladimir Alexandrovich
PY - 2023
DA - 2023/10/18
PB - Mendeleev Communications
SP - 802-805
IS - 6
VL - 33
ER -
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@article{2023_Shaimardanov,
author = {Arslan Ramilevich Shaimardanov and Dmitry Alexandrovich Shulga and Vladimir Alexandrovich Palyulin},
title = {On importance of explicit account of non-complementary contacts in scoring functions},
journal = {Mendeleev Communications},
year = {2023},
volume = {33},
publisher = {Mendeleev Communications},
month = {Oct},
url = {https://mendcomm.colab.ws/publications/10.1016/j.mencom.2023.10.021},
number = {6},
pages = {802--805},
doi = {10.1016/j.mencom.2023.10.021}
}
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Shaimardanov, Arslan Ramilevich, et al. “On importance of explicit account of non-complementary contacts in scoring functions.” Mendeleev Communications, vol. 33, no. 6, Oct. 2023, pp. 802-805. https://mendcomm.colab.ws/publications/10.1016/j.mencom.2023.10.021.

Keywords

binding free energy
molecular docking
molecular modeling
non-complementary contacts
protein–ligand interactions.
scoring functions
unfavorable contacts

Abstract

Based both on the practice of post-processing by a human expert and on the higher values of the accuracy metrics of machine learning scoring functions, it is suggested that when estimating the free energy of binding in a ligand-receptor complex, a significant part of intermolecular interactions is still not explicitly taken into account. An assessment is made of how explicit consideration of non-complementary ligand-receptor interactions could improve the accuracy of the description of contemporary classical scoring functions, which tend to use only terms of complementary/favorable interactions.

References

.
Comparative Assessment of Scoring Functions: The CASF-2016 Update
Su M., Yang Q., Du Y., Feng G., Liu Z., Li Y., Wang R.
Journal of Chemical Information and Modeling, 2018
.
Forging the Basis for Developing Protein–Ligand Interaction Scoring Functions
Liu Z., Su M., Han L., Liu J., Yang Q., Li Y., Wang R.
Accounts of Chemical Research, 2017
.
A Medicinal Chemist’s Guide to Molecular Interactions
Bissantz C., Kuhn B., Stahl M.
Journal of Medicinal Chemistry, 2010
.
Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review
Cheng T., Li Q., Zhou Z., Wang Y., Bryant S.H.
AAPS Journal, 2012
.
Molecular Docking: Shifting Paradigms in Drug Discovery
Pinzi L., Rastelli G.
International Journal of Molecular Sciences, 2019
.
Comparison of Shape-Matching and Docking as Virtual Screening Tools
Hawkins P.C., Skillman A.G., Nicholls A.
Journal of Medicinal Chemistry, 2006
.
Rationalizing Tight Ligand Binding through Cooperative Interaction Networks
Kuhn B., Fuchs J.E., Reutlinger M., Stahl M., Taylor N.R.
Journal of Chemical Information and Modeling, 2011
.
Classification of Current Scoring Functions
Liu J., Wang R.
Journal of Chemical Information and Modeling, 2015
.
Lipophilic Efficiency as an Important Metric in Drug Design
Johnson T.W., Gallego R.A., Edwards M.P.
Journal of Medicinal Chemistry, 2018
.
Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results
Fischer A., Smieško M., Sellner M., Lill M.A.
Journal of Medicinal Chemistry, 2021
.
On the Frustration to Predict Binding Affinities from Protein–Ligand Structures with Deep Neural Networks
Volkov M., Turk J., Drizard N., Martin N., Hoffmann B., Gaston-Mathé Y., Rognan D.
Journal of Medicinal Chemistry, 2022
.
Evaluation guidelines for machine learning tools in the chemical sciences
Bender A., Schneider N., Segler M., Patrick Walters W., Engkvist O., Rodrigues T.
Nature Reviews Chemistry, 2022
.
Exploring different approaches to improve the success of drug discovery and development projects: a review
Kiriiri G.K., Njogu P.M., Mwangi A.N.
Future Journal of Pharmaceutical Sciences, 2020
.
An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking
Li J., Fu A., Zhang L.
Interdisciplinary sciences, computational life sciences, 2019
.
PDB-wide collection of binding data: current status of the PDBbind database
Liu Z., Li Y., Han L., Li J., Liu J., Zhao Z., Nie W., Liu Y., Wang R.
Bioinformatics, 2014