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Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds
P-glycoprotein (P-gp) is a transmembrane protein that actively transports a wide variety of chemically diverse compounds out of the cell. It is highly associated with the ADMET (absorption, distribution, metabolism, excretion and toxicity) properties of drugs/drug candidates and contributes to decre...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6571636/ https://www.ncbi.nlm.nih.gov/pubmed/31130601 http://dx.doi.org/10.3390/molecules24102006 |
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author | Mora Lagares, Liadys Minovski, Nikola Novič, Marjana |
author_facet | Mora Lagares, Liadys Minovski, Nikola Novič, Marjana |
author_sort | Mora Lagares, Liadys |
collection | PubMed |
description | P-glycoprotein (P-gp) is a transmembrane protein that actively transports a wide variety of chemically diverse compounds out of the cell. It is highly associated with the ADMET (absorption, distribution, metabolism, excretion and toxicity) properties of drugs/drug candidates and contributes to decreasing toxicity by eliminating compounds from cells, thereby preventing intracellular accumulation. Therefore, in the drug discovery and toxicological assessment process it is advisable to pay attention to whether a compound under development could be transported by P-gp or not. In this study, an in silico multiclass classification model capable of predicting the probability of a compound to interact with P-gp was developed using a counter-propagation artificial neural network (CP ANN) based on a set of 2D molecular descriptors, as well as an extensive dataset of 2512 compounds (1178 P-gp inhibitors, 477 P-gp substrates and 857 P-gp non-active compounds). The model provided a good classification performance, producing non error rate (NER) values of 0.93 for the training set and 0.85 for the test set, while the average precision (AvPr) was 0.93 for the training set and 0.87 for the test set. An external validation set of 385 compounds was used to challenge the model’s performance. On the external validation set the NER and AvPr values were 0.70 for both indices. We believe that this in silico classifier could be effectively used as a reliable virtual screening tool for identifying potential P-gp ligands. |
format | Online Article Text |
id | pubmed-6571636 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-65716362019-06-18 Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds Mora Lagares, Liadys Minovski, Nikola Novič, Marjana Molecules Article P-glycoprotein (P-gp) is a transmembrane protein that actively transports a wide variety of chemically diverse compounds out of the cell. It is highly associated with the ADMET (absorption, distribution, metabolism, excretion and toxicity) properties of drugs/drug candidates and contributes to decreasing toxicity by eliminating compounds from cells, thereby preventing intracellular accumulation. Therefore, in the drug discovery and toxicological assessment process it is advisable to pay attention to whether a compound under development could be transported by P-gp or not. In this study, an in silico multiclass classification model capable of predicting the probability of a compound to interact with P-gp was developed using a counter-propagation artificial neural network (CP ANN) based on a set of 2D molecular descriptors, as well as an extensive dataset of 2512 compounds (1178 P-gp inhibitors, 477 P-gp substrates and 857 P-gp non-active compounds). The model provided a good classification performance, producing non error rate (NER) values of 0.93 for the training set and 0.85 for the test set, while the average precision (AvPr) was 0.93 for the training set and 0.87 for the test set. An external validation set of 385 compounds was used to challenge the model’s performance. On the external validation set the NER and AvPr values were 0.70 for both indices. We believe that this in silico classifier could be effectively used as a reliable virtual screening tool for identifying potential P-gp ligands. MDPI 2019-05-25 /pmc/articles/PMC6571636/ /pubmed/31130601 http://dx.doi.org/10.3390/molecules24102006 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mora Lagares, Liadys Minovski, Nikola Novič, Marjana Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds |
title | Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds |
title_full | Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds |
title_fullStr | Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds |
title_full_unstemmed | Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds |
title_short | Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds |
title_sort | multiclass classifier for p-glycoprotein substrates, inhibitors, and non-active compounds |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6571636/ https://www.ncbi.nlm.nih.gov/pubmed/31130601 http://dx.doi.org/10.3390/molecules24102006 |
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