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

Deciphering Molecular Aspects of Potential α-Glucosidase Inhibitors within Aspergillus terreus: A Computational Odyssey of Molecular Docking-Coupled Dynamics Simulations and Pharmacokinetic Profiling

“…In these regards, we decided to perform three sequential docking protocols at respective exhaustiveness values of 8, 32, or 50 while adopting 35% top-docking score selection criteria at each stage for obtaining the final best-docked compounds of the best-predicted biological activity at the most consistent docking findings. The selection percentage was suggested as rational regarding the starting number of compounds for screening, as well as the reported sole success rate with AutoDock Vina [ 32 , 66 , 67 ]. Out of the initial 265 compounds in the first phase, 94 resulted compounds were passed to the next screening stage.…”

Quest for Discovering Novel CDK12 Inhibitor by Leveraging High-Throughput Virtual Screening

“…Consensus Molecular Docking approaches use the integration of numerous scoring systems to provide a consensus score, thereby mitigating the in uence of individual biases and restrictions [42,43]. Virtual screening campaigns are enhanced by their ability to enhance the enrichment of active compounds, improve the discovery of drug candidates, and mitigate the occurrence of false positives and false negatives [44]. Five different molecular docking programs : Idock [45], QVina-W [46], SMINA [47,48], AutoDock Tools 4.2 [49], LeDock [50] have been used in Consensus Molecular Docking.…”