Applications of machine learning in GPCR bioactive ligand discovery

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    46 Citations (Scopus)

    Abstract

    GPCRs constitute the largest druggable family having targets for 475 Food and Drug Administration (FDA) approved drugs. As GPCRs are of great interest to pharmaceutical industry, enormous efforts are being expended to find relevant and potent GPCR ligands as lead compounds. There are tens of millions of compounds present in different chemical databases. In order to scan this immense chemical space, computational methods, especially machine learning (ML) methods, are essential components of GPCR drug discovery pipelines. ML approaches have applications in both ligand-based and structure-based virtual screening. We present here a cheminformatics overview of ML applications to different stages of GPCR drug discovery. Focusing on olfactory receptors, which are the largest family of GPCRs, a case study for predicting agonists for an ectopic olfactory receptor, OR1G1, compares four classical ML methods.

    Original languageEnglish
    Pages (from-to)66-76
    Number of pages11
    JournalCurrent Opinion in Structural Biology
    Volume55
    DOIs
    Publication statusPublished - Apr 2019

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