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QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6262347/ https://www.ncbi.nlm.nih.gov/pubmed/30524275 http://dx.doi.org/10.3389/fphar.2018.01275 |
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author | Neves, Bruno J. Braga, Rodolpho C. Melo-Filho, Cleber C. Moreira-Filho, José Teófilo Muratov, Eugene N. Andrade, Carolina Horta |
author_facet | Neves, Bruno J. Braga, Rodolpho C. Melo-Filho, Cleber C. Moreira-Filho, José Teófilo Muratov, Eugene N. Andrade, Carolina Horta |
author_sort | Neves, Bruno J. |
collection | PubMed |
description | 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 nD, 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. |
format | Online Article Text |
id | pubmed-6262347 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-62623472018-12-06 QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery Neves, Bruno J. Braga, Rodolpho C. Melo-Filho, Cleber C. Moreira-Filho, José Teófilo Muratov, Eugene N. Andrade, Carolina Horta Front Pharmacol Pharmacology 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 nD, 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. Frontiers Media S.A. 2018-11-13 /pmc/articles/PMC6262347/ /pubmed/30524275 http://dx.doi.org/10.3389/fphar.2018.01275 Text en Copyright © 2018 Neves, Braga, Melo-Filho, Moreira-Filho, Muratov and Andrade. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pharmacology Neves, Bruno J. Braga, Rodolpho C. Melo-Filho, Cleber C. Moreira-Filho, José Teófilo Muratov, Eugene N. Andrade, Carolina Horta QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery |
title | QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery |
title_full | QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery |
title_fullStr | QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery |
title_full_unstemmed | QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery |
title_short | QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery |
title_sort | qsar-based virtual screening: advances and applications in drug discovery |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6262347/ https://www.ncbi.nlm.nih.gov/pubmed/30524275 http://dx.doi.org/10.3389/fphar.2018.01275 |
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