Jabeen, Amara; de March, Claire A.; Matsunami, Hiroaki; Ranganathan, Shoba published the artcile< Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors>, Quality Control of 14062-30-7, the main research area is ectopic olfactory receptor agonist machine learning based model; G protein-coupled receptors; luciferase assay; machine learning; molecular descriptors; olfactory receptor; random forest; virtual ligand screening.
Olfactory receptors (ORs) constitute the largest superfamily of G protein-coupled receptors (GPCRs). ORs are involved in sensing odorants as well as in other ectopic roles in non-nasal tissues. Matching of an enormous number of the olfactory stimulation repertoire to its counterpart OR through machine learning (ML) will enable understanding of olfactory system, receptor characterization, and exploitation of their therapeutic potential. In the current study, we have selected two broadly tuned ectopic human OR proteins, OR1A1 and OR2W1, for expanding their known chem. space by using mol. descriptors. We present a scheme for selecting the optimal features required to train an ML-based model, based on which we selected the random forest (RF) as the best performer. High activity agonist prediction involved screening five databases comprising ∼23 M compounds, using the trained RF classifier. To evaluate the effectiveness of the machine learning based virtual screening and check receptor binding site compatibility, we used docking of the top target ligands to carefully develop receptor model structures. Finally, exptl. validation of selected compounds with significant docking scores through in vitro assays revealed two high activity novel agonists for OR1A1 and one for OR2W1.
International Journal of Molecular Sciences published new progress about Medicine (ectopic olfactory receptor agonist). 14062-30-7 belongs to class bromides-buliding-blocks, and the molecular formula is C10H11BrO2, Quality Control of 14062-30-7.
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