Machine Learning for Discovery of GSK3β Inhibitors was written by Vignaux, Patricia A.;Minerali, Eni;Foil, Daniel H.;Puhl, Ana C.;Ekins, Sean. And the article was included in ACS Omega in 2020.SDS of cas: 41340-25-4 This article mentions the following:
Alzheimer’s disease (AD) is the most common cause of dementia, affecting approx. 35 million people worldwide. The current treatment options for people with AD consist of drugs designed to slow the rate of decline in memory and cognition, but these treatments are not curative, and patients eventually suffer complete cognitive injury. With the substantial amounts of published data on targets for this disease, we proposed that machine learning software could be used to find novel small-mol. treatments that can supplement the AD drugs currently on the market. In order to do this, we used publicly available data in ChEMBL to build and validate Bayesian machine learning models for AD target proteins. The first AD target that we have addressed with this method is the serine-threonine kinase glycogen synthase kinase 3 beta (GSK3β), which is a proline-directed serine-threonine kinase that phosphorylates the microtubule-stabilizing protein tau. This phosphorylation prompts tau to dissociate from the microtubule and form insoluble oligomers called paired helical filaments, which are one of the components of the neurofibrillary tangles found in AD brains. Using our Bayesian machine learning model for GSK3β consisting of 2368 mols., this model produced a five-fold cross validation ROC of 0.905. This model was also used for virtual screening of large libraries of FDA-approved drugs and clin. candidates. Subsequent testing of selected compounds revealed a selective small-mol. inhibitor, ruboxistaurin(I), with activity against GSK3β (avg IC50 = 97.3 nM) and GSK3α (IC50 = 695.9 nM). Several other structurally diverse inhibitors were also identified. We are now applying this machine learning approach to addnl. AD targets to identify approved drugs or clin. 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. In the experiment, the researchers used many compounds, for example, 2-(1,8-Diethyl-1,3,4,9-tetrahydropyrano[3,4-b]indol-1-yl)acetic acid (cas: 41340-25-4SDS of cas: 41340-25-4).
2-(1,8-Diethyl-1,3,4,9-tetrahydropyrano[3,4-b]indol-1-yl)acetic acid (cas: 41340-25-4) belongs to tetrahydropyran derivatives. Numerous natural products have tetrahydropyran skeleton as the building block for designing new natural products and their derivatives e.g. aplysiatoxins, avermectins, oscillatoxins, talaromycins, latrunculins and acutiphycins. There is large number of marine macrolide natural products that contain tetrahydropyran and tetrahydrofuran ring together. For instance, goniodomin A (actin targeting polyether), prorocentrolide (toxin halistatins), and percentotoxineSDS of cas: 41340-25-4
Referemce:
Tetrahydropyran – Wikipedia,
Tetrahydropyran – an overview | ScienceDirect Topics