Identification of Novel High-Affinity Substrates of OCT1 Using Machine Learning-Guided Virtual Screening and Experimental Validation was written by Jensen, Ole;Brockmoeller, Juergen;Duecker, Christof. And the article was included in Journal of Medicinal Chemistry in 2021.Product Details of 139264-17-8 This article mentions the following:
OCT1 is the most highly expressed cation transporter in the liver and affects pharmacokinetics and pharmacodynamics. Newly marketed drugs have previously been screened as potential OCT1 substrates and verified by virtual docking. Here, we used machine learning with transport experiment data to predict OCT1 substrates based on classic mol. descriptors, pharmacophore features, and extended-connectivity fingerprints and confirmed them by in vitro uptake experiments We virtually screened a database of more than 1000 substances. Nineteen predicted substances were chosen for in vitro testing. Sixteen of the 19 newly tested substances (85%) were confirmed as, mostly strong, substrates, including edrophonium, fenpiverinium, ritodrine, and ractopamine. Even without a crystal structure of OCT1, machine learning algorithms predict substrates accurately and may contribute not only to a more focused screening in drug development but also to a better mol. understanding of OCT1 in general. In the experiment, the researchers used many compounds, for example, (S)-4-((3-(2-(Dimethylamino)ethyl)-1H-indol-5-yl)methyl)oxazolidin-2-one (cas: 139264-17-8Product Details of 139264-17-8).
(S)-4-((3-(2-(Dimethylamino)ethyl)-1H-indol-5-yl)methyl)oxazolidin-2-one (cas: 139264-17-8) belongs to oxazolidine derivatives. Oxazolidine-based compounds have started to attract attention also in the medicinal and materials chemistry fields. Oxazolidines are well known as key portions of bioactive molecules or precursors of chiral molecules, as well as established chiral auxiliaries.Product Details of 139264-17-8
Referemce:
Oxazolidine – Wikipedia,
Oxazolidine | C3H7NO – PubChem