دورية أكاديمية
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QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery.

التفاصيل البيبلوغرافية
العنوان: QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery.
المؤلفون: Neves BJ; LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, Brazil.; Laboratory of Cheminformatics, Centro Universitário de Anápolis (UniEVANGÉLICA), Anápolis, Brazil., Braga RC; LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, Brazil., Melo-Filho CC; LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, Brazil., Moreira-Filho JT; LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, Brazil., Muratov EN; Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.; Department of Chemical Technology, Odessa National Polytechnic University, Odessa, Ukraine., Andrade CH; LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, Brazil.
المصدر: Frontiers in pharmacology [Front Pharmacol] 2018 Nov 13; Vol. 9, pp. 1275. Date of Electronic Publication: 2018 Nov 13 (Print Publication: 2018).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Frontiers Media] Country of Publication: Switzerland NLM ID: 101548923 Publication Model: eCollection Cited Medium: Print ISSN: 1663-9812 (Print) Linking ISSN: 16639812 NLM ISO Abbreviation: Front Pharmacol Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: [Lausanne : Frontiers Media]
مستخلص: Virtual screening (VS) has emerged in drug discovery as a powerful computational approach to screen large libraries of small molecules for new hits with desired properties that can then be tested experimentally. Similar to other computational approaches, VS intention is not to replace in vitro or in vivo assays, but to speed up the discovery process, to reduce the number of candidates to be tested experimentally, and to rationalize their choice. Moreover, VS has become very popular in pharmaceutical companies and academic organizations due to its time-, cost-, resources-, and labor-saving. Among the VS approaches, quantitative structure-activity relationship (QSAR) analysis is the most powerful method due to its high and fast throughput and good hit rate. As the first preliminary step of a QSAR model development, relevant chemogenomics data are collected from databases and the literature. Then, chemical descriptors are calculated on different levels of representation of molecular structure, ranging from 1D to n D, and then correlated with the biological property using machine learning techniques. Once developed and validated, QSAR models are applied to predict the biological property of novel compounds. Although the experimental testing of computational hits is not an inherent part of QSAR methodology, it is highly desired and should be performed as an ultimate validation of developed models. In this mini-review, we summarize and critically analyze the recent trends of QSAR-based VS in drug discovery and demonstrate successful applications in identifying perspective compounds with desired properties. Moreover, we provide some recommendations about the best practices for QSAR-based VS along with the future perspectives of this approach.
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فهرسة مساهمة: Keywords: cheminformatics; computer-assisted drug design; machine learning; molecular descriptors; virtual screening
تواريخ الأحداث: Date Created: 20181208 Latest Revision: 20200930
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC6262347
DOI: 10.3389/fphar.2018.01275
PMID: 30524275
قاعدة البيانات: MEDLINE
الوصف
تدمد:1663-9812
DOI:10.3389/fphar.2018.01275