Towards Effective Consensus Scoring in Structure-Based Virtual Screening

Abstract

Virtual screening (VS) is a computational strategy that uses in silico automated protein docking inter alia to rank potential ligands, or by extension rank protein–ligand pairs, identifying potential drug candidates. Most docking methods use preferred sets of physicochemical descriptors (PCDs) to model the interactions between host and guest molecules. Thus, conventional VS is often data-specific, method-dependent and with demonstrably differing utility in identifying candidate drugs. This study proposes four universality classes of novel consensus scoring (CS) algorithms that combine docking scores, derived from ten docking programs (ADFR, DOCK, Gemdock, Ledock, PLANTS, PSOVina, QuickVina2, Smina, Autodock Vina and VinaXB), using decoys from the DUD-E repository (http://dude.docking.org/) against 29 MRSA-oriented targets to create a general VS formulation that can identify active ligands for any suitable protein target. Our results demonstrate that CS provides improved ligand–protein docking fidelity when compared to individual docking platforms. This approach requires only a small number of docking combinations and can serve as a viable and parsimonious alternative to more computationally expensive docking approaches. Predictions from our CS algorithm are compared against independent machine learning evaluations using the same docking data, complementing the CS outcomes. Our method is a reliable approach for identifying protein targets and high-affinity ligands that can be tested as high-probability candidates for drug repositioning. Graphical Abstract:

Publication DOI: https://doi.org/10.1007/s12539-022-00546-8
Divisions: College of Engineering & Physical Sciences
College of Business and Social Sciences > Aston Business School > Aston India Centre for Applied Research
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: Copyright © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Uncontrolled Keywords: Original Research Article,Molecular docking,Machine learning,Consensus scoring,Virtual screening
Publication ISSN: 1913-2751
Last Modified: 23 May 2024 07:17
Date Deposited: 22 Feb 2023 09:34
Full Text Link:
Related URLs: https://link.sp ... 539-022-00546-8 (Publisher URL)
PURE Output Type: Article
Published Date: 2022-12-23
Published Online Date: 2022-12-23
Accepted Date: 2022-12-12
Submitted Date: 2022-05-21
Authors: Nhat Phuong, Do
Flower, Darren R.
Chattopadhyay, Subhagata
Chattopadhyay, Amit K. (ORCID Profile 0000-0001-5499-6008)

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