Virtual Screening of Kinase Based Drugs: Statistical LearningTowards Drug Repositioning

Monsuru Taiye Mustapha, Darren R. Flower, Amit K. Chattopadhyay

Research output: Contribution to journalArticlepeer-review


Kinases are phosphate catalysing enzymes that have traditionally proved difficult to target against ligands, and hence inefficacious in drug development. There are two colluding reasons for this. First is the issue of specificity. The homogeneity that exists between the kinase ATP-binding pockets makes it a non-realisable target to develop
compounds that would inhibit only one out of 538 protein kinases encoded by the human genome, without inhibiting some of the others. Second, producing compounds with the required efficacy to rival the millimolar ATP concentrations present in cells is stoichiometrically inefficient. This study uses a recently propounded computational strategy based on Structure-Based Virtual Screening (SBVS) that was previously benchmarked on 999 DUD-E protein decoys (Chattopadhyay et al, Int Sc. Comp. Life Sciences 2022), to rank potential ligands, or by extension rank kinase-ligand
pairs, identifying best matching ligand:kinase docking pairs. The results of the SBVS campaign employing several computational algorithms reveal variations in the preferred top hits. To address this, we introduce a novel consensus
scoring algorithm by sampling statistics across four independent statistical universality classes, statistically combining docking scores from ten docking programs (DOCK, Quick Vina-W, Vina Carb, PLANTS, Autodock, QuickVina2,
QuickVina21, Smina, Autodock Vina and VinaXB) to create a holistic SBVS formulation that can identify active ligands for any target. Our results demonstrate that CS provides improved ligand:kinase docking fidelity when compared to individual docking platforms, requiring only a small number of docking combinations, and can serve as a viable and thrifty alternative to expensive docking platforms.
Original languageEnglish
Pages (from-to)23-34
JournalJournal of Nanotechnology in Diagnosis and Treatment
Publication statusPublished - 12 Dec 2022

Bibliographical note

Copyright © 2022 Mustapha et al.; Licensee Savvy Science Publisher.
This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License
( which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited. Funding & Acknowledgements: AKC acknowledges partial financial support from the H2020-MSCA-RISE-2016 program, grant number 734485, entitled ‘Fracture Across Scales and Materials, Processes and Disciplines (FRAMED)’. MTM acknowledges Aston University Covid fund support for crucial financial assistance.


  • Statistical Modelling
  • Molecular Docking
  • Consensus Scoring
  • Virtual Screening
  • Multiple linear regressions


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