Towards Effective Consensus Scoring in Structure-Based Virtual Screening

Do Nhat Phuong, Darren R. Flower, Subhagata Chattopadhyay, Amit K. Chattopadhyay*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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 ( 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:
Original languageEnglish
Pages (from-to)131-145
Number of pages15
JournalInterdisciplinary Sciences: Computational Life Sciences
Issue number1
Early online date23 Dec 2022
Publication statusE-pub ahead of print - 23 Dec 2022

Bibliographical note

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  • Original Research Article
  • Molecular docking
  • Machine learning
  • Consensus scoring
  • Virtual screening


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