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MATH: A Deep Learning Approach in QSAR for Estrogen Receptor Alpha Inhibitors
Breast cancer ranks as the second leading cause of death among women, but early screening and self-awareness can help prevent it. Hormone therapy drugs that target estrogen levels offer potential treatments. However, conventional drug discovery entails extensive, costly processes. This study present...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10421274/ https://www.ncbi.nlm.nih.gov/pubmed/37570812 http://dx.doi.org/10.3390/molecules28155843 |
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author | Pusparini, Rizki Triyani Krisnadhi, Adila Alfa Firdayani |
author_facet | Pusparini, Rizki Triyani Krisnadhi, Adila Alfa Firdayani |
author_sort | Pusparini, Rizki Triyani |
collection | PubMed |
description | Breast cancer ranks as the second leading cause of death among women, but early screening and self-awareness can help prevent it. Hormone therapy drugs that target estrogen levels offer potential treatments. However, conventional drug discovery entails extensive, costly processes. This study presents a framework for analyzing the quantitative structure–activity relationship (QSAR) of estrogen receptor alpha inhibitors. Our approach utilizes supervised learning, integrating self-attention Transformer and molecular graph information, to predict estrogen receptor alpha inhibitors. We established five classification models for predicting these inhibitors in breast cancer. Among these models, our proposed MATH model achieved remarkable precision, recall, F1 score, and specificity, with values of 0.952, 0.972, 0.960, and 0.922, respectively, alongside an ROC AUC of 0.977. MATH exhibited robust performance, suggesting its potential to assist pharmaceutical and health researchers in identifying candidate compounds for estrogen alpha inhibitors and guiding drug discovery pathways. |
format | Online Article Text |
id | pubmed-10421274 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104212742023-08-12 MATH: A Deep Learning Approach in QSAR for Estrogen Receptor Alpha Inhibitors Pusparini, Rizki Triyani Krisnadhi, Adila Alfa Firdayani Molecules Article Breast cancer ranks as the second leading cause of death among women, but early screening and self-awareness can help prevent it. Hormone therapy drugs that target estrogen levels offer potential treatments. However, conventional drug discovery entails extensive, costly processes. This study presents a framework for analyzing the quantitative structure–activity relationship (QSAR) of estrogen receptor alpha inhibitors. Our approach utilizes supervised learning, integrating self-attention Transformer and molecular graph information, to predict estrogen receptor alpha inhibitors. We established five classification models for predicting these inhibitors in breast cancer. Among these models, our proposed MATH model achieved remarkable precision, recall, F1 score, and specificity, with values of 0.952, 0.972, 0.960, and 0.922, respectively, alongside an ROC AUC of 0.977. MATH exhibited robust performance, suggesting its potential to assist pharmaceutical and health researchers in identifying candidate compounds for estrogen alpha inhibitors and guiding drug discovery pathways. MDPI 2023-08-03 /pmc/articles/PMC10421274/ /pubmed/37570812 http://dx.doi.org/10.3390/molecules28155843 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pusparini, Rizki Triyani Krisnadhi, Adila Alfa Firdayani MATH: A Deep Learning Approach in QSAR for Estrogen Receptor Alpha Inhibitors |
title | MATH: A Deep Learning Approach in QSAR for Estrogen Receptor Alpha Inhibitors |
title_full | MATH: A Deep Learning Approach in QSAR for Estrogen Receptor Alpha Inhibitors |
title_fullStr | MATH: A Deep Learning Approach in QSAR for Estrogen Receptor Alpha Inhibitors |
title_full_unstemmed | MATH: A Deep Learning Approach in QSAR for Estrogen Receptor Alpha Inhibitors |
title_short | MATH: A Deep Learning Approach in QSAR for Estrogen Receptor Alpha Inhibitors |
title_sort | math: a deep learning approach in qsar for estrogen receptor alpha inhibitors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10421274/ https://www.ncbi.nlm.nih.gov/pubmed/37570812 http://dx.doi.org/10.3390/molecules28155843 |
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