Ahmed, Marawan published the artcileGPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds, Computed Properties of 637-07-0, the publication is Scientific Reports (2021), 11(1), 9510, database is CAplus and MEDLINE.
The current study describes the construction of various ligand-based machine learning models to be used for drug-repurposing against the family of G-Protein Coupled Receptors (GPCRs). In building these models, we collected > 500,000 data points, encompassing exptl. measured mol. association data of > 160,000 unique ligands against > 250 GPCRs. These data points were retrieved from the GPCR-Ligand Association (GLASS) database. We have used diverse mol. featurization methods to describe the input mols. Multiple supervised ML algorithms were developed, tested and compared for their accuracy, F scores, as well as for their Matthews correlation coefficient scores (MCC). Our data suggest that combined with mol. fingerprinting, ensemble decision trees and gradient boosted trees ML algorithms are on the accuracy border of the rather sophisticated deep neural nets (DNNs)-based algorithms. On a test dataset, these models displayed an excellent performance, reaching a â?90% classification accuracy. Addnl., we showcase a few examples where our models were able to identify interesting connections between known drugs from the Drug-Bank database and members of the GPCR family of receptors. Our findings are in excellent agreement with previously reported exptl. observations in the literature. We hope the models presented in this paper synergize with the currently ongoing interest of applying machine learning modeling in the field of drug repurposing and computational drug discovery in general.
Scientific Reports published new progress about 637-07-0. 637-07-0 belongs to chlorides-buliding-blocks, auxiliary class Inhibitor,Cell Cycle,PPAR, name is Ethyl 2-(4-chlorophenoxy)-2-methylpropanoate, and the molecular formula is C12H15ClO3, Computed Properties of 637-07-0.
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