A facile consensus ranking approach enhances virtual screening robustness and identifies a cell-active DYRK1α inhibitor. (October 2018)

• Record Type: Journal Article
• Authors: Mavrogeni, Maria E; Pronios, Filippos; Zareifi, Danae; Vasilakaki, Sofia; Lozach, Olivier; Alexopoulos, Leonidas; Meijer, Laurent; Myrianthopoulos, Vassilios; Mikros, Emmanuel
• Is Part Of: Future medicinal chemistry. Volume 10:Number 20(2018)
• Page Start: 2411
• Page End: 2430
• Publication Date: 2018-10
• Subjects: screening enrichment -- CREB1 -- docking-scoring calculations -- fingerprint similarity -- NCI diversity set-II -- NSC379099 -- p53 -- phosphoproteomics -- analysis of residuals -- shape-based similarity; Pharmaceutical chemistry -- Periodicals; 615.19005
• DOI: 10.4155/fmc-2018-0198
• Languages: English
• ISSNs: 1756-8919

Abstract

Background: Virtual screening is vital for contemporary drug discovery but striking performance fluctuations are commonly encountered, thus hampering error-free use.Results and Methodology: A conceptual framework is suggested for combining screening algorithms characterized by orthogonality (docking-scoring calculations, 3D shape similarity, 2D fingerprint similarity) into a simple, efficient and expansible python-based consensus ranking scheme. An original experimental dataset is created for comparing individual screening methods versus the novel approach. Its utilization leads to identification and phosphoproteomic evaluation of a cell-active DYRK1α inhibitor.Conclusion: Consensus ranking considerably stabilizes screening performance at reasonable computational cost, whereas individual screens are heavily dependent on calculation settings. Results indicate that the novel approach, currently available as a free online tool, is highly suitable for prospective screening by nonexperts.