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Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning

Drug-induced immune thrombocytopenia (DITP) often occurs in patients receiving many drug treatments simultaneously. However, clinicians usually fail to accurately distinguish which drugs can be plausible culprits. Despite significant advances in laboratory-based DITP testing, in vitro experimental a...

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Autores principales: Wang, Binyou, Tan, Xiaoqiu, Guo, Jianmin, Xiao, Ting, Jiao, Yan, Zhao, Junlin, Wu, Jianming, Wang, Yiwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143325/
https://www.ncbi.nlm.nih.gov/pubmed/35631529
http://dx.doi.org/10.3390/pharmaceutics14050943
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author Wang, Binyou
Tan, Xiaoqiu
Guo, Jianmin
Xiao, Ting
Jiao, Yan
Zhao, Junlin
Wu, Jianming
Wang, Yiwei
author_facet Wang, Binyou
Tan, Xiaoqiu
Guo, Jianmin
Xiao, Ting
Jiao, Yan
Zhao, Junlin
Wu, Jianming
Wang, Yiwei
author_sort Wang, Binyou
collection PubMed
description Drug-induced immune thrombocytopenia (DITP) often occurs in patients receiving many drug treatments simultaneously. However, clinicians usually fail to accurately distinguish which drugs can be plausible culprits. Despite significant advances in laboratory-based DITP testing, in vitro experimental assays have been expensive and, in certain cases, cannot provide a timely diagnosis to patients. To address these shortcomings, this paper proposes an efficient machine learning-based method for DITP toxicity prediction. A small dataset consisting of 225 molecules was constructed. The molecules were represented by six fingerprints, three descriptors, and their combinations. Seven classical machine learning-based models were examined to determine an optimal model. The results show that the RDMD + PubChem-k-NN model provides the best prediction performance among all the models, achieving an area under the curve of 76.9% and overall accuracy of 75.6% on the external validation set. The application domain (AD) analysis demonstrates the prediction reliability of the RDMD + PubChem-k-NN model. Five structural fragments related to the DITP toxicity are identified through information gain (IG) method along with fragment frequency analysis. Overall, as far as known, it is the first machine learning-based classification model for recognizing chemicals with DITP toxicity and can be used as an efficient tool in drug design and clinical therapy.
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spelling pubmed-91433252022-05-29 Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning Wang, Binyou Tan, Xiaoqiu Guo, Jianmin Xiao, Ting Jiao, Yan Zhao, Junlin Wu, Jianming Wang, Yiwei Pharmaceutics Article Drug-induced immune thrombocytopenia (DITP) often occurs in patients receiving many drug treatments simultaneously. However, clinicians usually fail to accurately distinguish which drugs can be plausible culprits. Despite significant advances in laboratory-based DITP testing, in vitro experimental assays have been expensive and, in certain cases, cannot provide a timely diagnosis to patients. To address these shortcomings, this paper proposes an efficient machine learning-based method for DITP toxicity prediction. A small dataset consisting of 225 molecules was constructed. The molecules were represented by six fingerprints, three descriptors, and their combinations. Seven classical machine learning-based models were examined to determine an optimal model. The results show that the RDMD + PubChem-k-NN model provides the best prediction performance among all the models, achieving an area under the curve of 76.9% and overall accuracy of 75.6% on the external validation set. The application domain (AD) analysis demonstrates the prediction reliability of the RDMD + PubChem-k-NN model. Five structural fragments related to the DITP toxicity are identified through information gain (IG) method along with fragment frequency analysis. Overall, as far as known, it is the first machine learning-based classification model for recognizing chemicals with DITP toxicity and can be used as an efficient tool in drug design and clinical therapy. MDPI 2022-04-26 /pmc/articles/PMC9143325/ /pubmed/35631529 http://dx.doi.org/10.3390/pharmaceutics14050943 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
Wang, Binyou
Tan, Xiaoqiu
Guo, Jianmin
Xiao, Ting
Jiao, Yan
Zhao, Junlin
Wu, Jianming
Wang, Yiwei
Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning
title Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning
title_full Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning
title_fullStr Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning
title_full_unstemmed Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning
title_short Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning
title_sort drug-induced immune thrombocytopenia toxicity prediction based on machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143325/
https://www.ncbi.nlm.nih.gov/pubmed/35631529
http://dx.doi.org/10.3390/pharmaceutics14050943
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