Virtual Screening of Kinase Based Drugs: Statistical Learning Towards Drug Repositioning
نویسندگان
چکیده
Kinases are phosphate catalysing enzymes that have traditionally proved difficult to target against ligands,and hence inefficacious in drug development. There two colluding reasons for this. First is the issue of specificity. The homogeneity exists between kinase ATP-binding pockets makes it a non-realisable developcompounds would inhibit only one out 538 protein kinases encoded by human genome, without inhibitingsome others. Second, producing compounds with required efficacy rival millimolar ATP concentrations present cells stoichiometrically inefficient. This study uses recently propounded computational strategy based onStructure Based Virtual Screening (SBVS) was previously benchmarked on 999 DUD-E decoys(Chattopadhyay et al, Int Sc. Comp. Life Sciences 2022), rank potential ligands, or extension kinase-ligand pairs, identifying best matching ligand:kinase docking pairs. results SBVS campaign employing severalcomputational algorithms reveal variations preferred top hits. To address this, we introduce novel consensusscoring algorithm sampling statistics across four independent statistical universality classes, statistically combining scores from ten programs (DOCK, Quick Vina-W, Vina Carb, PLANTS, Autodock, QuickVina2,QuickVina21, Smina, Autodock and VinaXB) create holistic formulation can identify active ligandsfor any target. Our demonstrate CS provides improved fidelity when compared individual platforms, requiring small number combinations, serve as viable andthrifty alternative expensive platforms.
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ژورنال
عنوان ژورنال: Journal of nanotechnology in diagnosis and treatment
سال: 2022
ISSN: ['2311-8792']
DOI: https://doi.org/10.12974/2311-8792.2022.08.03