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Small Molecular Drug Screening Based on Clinical Therapeutic Effect
Virtual screening can significantly save experimental time and costs for early drug discovery. Drug multi-classification can speed up virtual screening and quickly predict the most likely class for a drug. In this study, 1019 drug molecules with actual therapeutic effects are collected from multiple...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9369618/ https://www.ncbi.nlm.nih.gov/pubmed/35956770 http://dx.doi.org/10.3390/molecules27154807 |
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author | Zhong, Cai Ai, Jiali Yang, Yaxin Ma, Fangyuan Sun, Wei |
author_facet | Zhong, Cai Ai, Jiali Yang, Yaxin Ma, Fangyuan Sun, Wei |
author_sort | Zhong, Cai |
collection | PubMed |
description | Virtual screening can significantly save experimental time and costs for early drug discovery. Drug multi-classification can speed up virtual screening and quickly predict the most likely class for a drug. In this study, 1019 drug molecules with actual therapeutic effects are collected from multiple databases and documents, and molecular sets are grouped according to therapeutic effect and mechanism of action. Molecular descriptors and molecular fingerprints are obtained through SMILES to quantify molecular structures. After using the Kennard–Stone method to divide the data set, a better combination can be obtained by comparing the combined results of five classification algorithms and a fusion method. Furthermore, for a specific data set, the model with the best performance is used to predict the validation data set. The test set shows that prediction accuracy can reach 0.862 and kappa coefficient can reach 0.808. The highest classification accuracy of the validation set is 0.873. The more reliable molecular set has been found, which could be used to predict potential attributes of unknown drug compounds and even to discover new use for old drugs. We hope this research can provide a reference for virtual screening of multiple classes of drugs at the same time in the future. |
format | Online Article Text |
id | pubmed-9369618 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93696182022-08-12 Small Molecular Drug Screening Based on Clinical Therapeutic Effect Zhong, Cai Ai, Jiali Yang, Yaxin Ma, Fangyuan Sun, Wei Molecules Article Virtual screening can significantly save experimental time and costs for early drug discovery. Drug multi-classification can speed up virtual screening and quickly predict the most likely class for a drug. In this study, 1019 drug molecules with actual therapeutic effects are collected from multiple databases and documents, and molecular sets are grouped according to therapeutic effect and mechanism of action. Molecular descriptors and molecular fingerprints are obtained through SMILES to quantify molecular structures. After using the Kennard–Stone method to divide the data set, a better combination can be obtained by comparing the combined results of five classification algorithms and a fusion method. Furthermore, for a specific data set, the model with the best performance is used to predict the validation data set. The test set shows that prediction accuracy can reach 0.862 and kappa coefficient can reach 0.808. The highest classification accuracy of the validation set is 0.873. The more reliable molecular set has been found, which could be used to predict potential attributes of unknown drug compounds and even to discover new use for old drugs. We hope this research can provide a reference for virtual screening of multiple classes of drugs at the same time in the future. MDPI 2022-07-27 /pmc/articles/PMC9369618/ /pubmed/35956770 http://dx.doi.org/10.3390/molecules27154807 Text en © 2022 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 Zhong, Cai Ai, Jiali Yang, Yaxin Ma, Fangyuan Sun, Wei Small Molecular Drug Screening Based on Clinical Therapeutic Effect |
title | Small Molecular Drug Screening Based on Clinical Therapeutic Effect |
title_full | Small Molecular Drug Screening Based on Clinical Therapeutic Effect |
title_fullStr | Small Molecular Drug Screening Based on Clinical Therapeutic Effect |
title_full_unstemmed | Small Molecular Drug Screening Based on Clinical Therapeutic Effect |
title_short | Small Molecular Drug Screening Based on Clinical Therapeutic Effect |
title_sort | small molecular drug screening based on clinical therapeutic effect |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9369618/ https://www.ncbi.nlm.nih.gov/pubmed/35956770 http://dx.doi.org/10.3390/molecules27154807 |
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