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Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges

Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis of biological targets related to a given disease, the discovery and the development of drug candidates for these targets, performing parallel biological tests to validate the drug effectiveness and sid...

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Autores principales: Simões, Rodolfo S., Maltarollo, Vinicius G., Oliveira, Patricia R., Honorio, Kathia M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5807924/
https://www.ncbi.nlm.nih.gov/pubmed/29467659
http://dx.doi.org/10.3389/fphar.2018.00074
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author Simões, Rodolfo S.
Maltarollo, Vinicius G.
Oliveira, Patricia R.
Honorio, Kathia M.
author_facet Simões, Rodolfo S.
Maltarollo, Vinicius G.
Oliveira, Patricia R.
Honorio, Kathia M.
author_sort Simões, Rodolfo S.
collection PubMed
description Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis of biological targets related to a given disease, the discovery and the development of drug candidates for these targets, performing parallel biological tests to validate the drug effectiveness and side effects. Approaches as quantitative study of activity-structure relationships (QSAR) involve the construction of predictive models that relate a set of descriptors of a chemical compound series and its biological activities with respect to one or more targets in the human body. Datasets used to perform QSAR analyses are generally characterized by a small number of samples and this makes them more complex to build accurate predictive models. In this context, transfer and multi-task learning techniques are very suitable since they take information from other QSAR models to the same biological target, reducing efforts and costs for generating new chemical compounds. Therefore, this review will present the main features of transfer and multi-task learning studies, as well as some applications and its potentiality in drug design projects.
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spelling pubmed-58079242018-02-21 Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges Simões, Rodolfo S. Maltarollo, Vinicius G. Oliveira, Patricia R. Honorio, Kathia M. Front Pharmacol Pharmacology Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis of biological targets related to a given disease, the discovery and the development of drug candidates for these targets, performing parallel biological tests to validate the drug effectiveness and side effects. Approaches as quantitative study of activity-structure relationships (QSAR) involve the construction of predictive models that relate a set of descriptors of a chemical compound series and its biological activities with respect to one or more targets in the human body. Datasets used to perform QSAR analyses are generally characterized by a small number of samples and this makes them more complex to build accurate predictive models. In this context, transfer and multi-task learning techniques are very suitable since they take information from other QSAR models to the same biological target, reducing efforts and costs for generating new chemical compounds. Therefore, this review will present the main features of transfer and multi-task learning studies, as well as some applications and its potentiality in drug design projects. Frontiers Media S.A. 2018-02-06 /pmc/articles/PMC5807924/ /pubmed/29467659 http://dx.doi.org/10.3389/fphar.2018.00074 Text en Copyright © 2018 Simões, Maltarollo, Oliveira and Honorio. 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 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
Simões, Rodolfo S.
Maltarollo, Vinicius G.
Oliveira, Patricia R.
Honorio, Kathia M.
Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges
title Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges
title_full Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges
title_fullStr Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges
title_full_unstemmed Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges
title_short Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges
title_sort transfer and multi-task learning in qsar modeling: advances and challenges
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5807924/
https://www.ncbi.nlm.nih.gov/pubmed/29467659
http://dx.doi.org/10.3389/fphar.2018.00074
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