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2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications

A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemi...

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Detalles Bibliográficos
Autores principales: Du, Qi-Shi, Wang, Shu-Qing, Xie, Neng-Zhong, Wang, Qing-Yan, Huang, Ri-Bo, Chou, Kuo-Chen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5642577/
https://www.ncbi.nlm.nih.gov/pubmed/29050302
http://dx.doi.org/10.18632/oncotarget.19757
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author Du, Qi-Shi
Wang, Shu-Qing
Xie, Neng-Zhong
Wang, Qing-Yan
Huang, Ri-Bo
Chou, Kuo-Chen
author_facet Du, Qi-Shi
Wang, Shu-Qing
Xie, Neng-Zhong
Wang, Qing-Yan
Huang, Ri-Bo
Chou, Kuo-Chen
author_sort Du, Qi-Shi
collection PubMed
description A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural fragments. The predictor has the self-learning and feedback features to automatically improve its accuracy. It is anticipated that 2L-PCA will become a very useful tool for timely providing various useful clues during the process of drug development.
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spelling pubmed-56425772017-10-18 2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications Du, Qi-Shi Wang, Shu-Qing Xie, Neng-Zhong Wang, Qing-Yan Huang, Ri-Bo Chou, Kuo-Chen Oncotarget Research Paper A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural fragments. The predictor has the self-learning and feedback features to automatically improve its accuracy. It is anticipated that 2L-PCA will become a very useful tool for timely providing various useful clues during the process of drug development. Impact Journals LLC 2017-08-01 /pmc/articles/PMC5642577/ /pubmed/29050302 http://dx.doi.org/10.18632/oncotarget.19757 Text en Copyright: © 2017 Du et al. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Paper
Du, Qi-Shi
Wang, Shu-Qing
Xie, Neng-Zhong
Wang, Qing-Yan
Huang, Ri-Bo
Chou, Kuo-Chen
2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications
title 2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications
title_full 2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications
title_fullStr 2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications
title_full_unstemmed 2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications
title_short 2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications
title_sort 2l-pca: a two-level principal component analyzer for quantitative drug design and its applications
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5642577/
https://www.ncbi.nlm.nih.gov/pubmed/29050302
http://dx.doi.org/10.18632/oncotarget.19757
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