Machine Learning for Discovery of GSK3β Inhibitors
Patricia A. Vignaux, Eni Minerali, Daniel H. Foil, Ana C. Puhl, Sean Ekins
Abstract
= 695.9 nM). Several other structurally diverse inhibitors were also identified. We are now applying this machine learning approach to additional AD targets to identify approved drugs or clinical trial candidates that can be repurposed as AD therapeutics. This represents a viable approach to accelerate drug discovery and do so at a fraction of the cost of traditional high throughput screening.
Topics & Concepts
GSK-3chEMBLThreonineKinaseVirtual screeningDrug discoveryDementiaSerinePhosphorylationNaive Bayes classifierComputational biologyMachine learningChemistryBiochemistryBiologyComputer scienceDiseaseMedicineInternal medicineSupport vector machineComputational Drug Discovery MethodsBioinformatics and Genomic NetworksAlzheimer's disease research and treatments