Sizhe Zhang, Peng Han, Haiqing Sun, Ying Su, Chen Chen, Cheng Chen, Jinyao Li, Xiaoyi Lv, Xuecong Tian, Yandan Xu
{"title":"基于深度学习方法发现靶向 TLR4 治疗肝脏炎症疾病的银翘解毒片有效成分","authors":"Sizhe Zhang, Peng Han, Haiqing Sun, Ying Su, Chen Chen, Cheng Chen, Jinyao Li, Xiaoyi Lv, Xuecong Tian, Yandan Xu","doi":"10.1007/s12539-024-00670-7","DOIUrl":null,"url":null,"abstract":"<p><p>Yinchenhao Decoction (YCHD), a classic formula in traditional Chinese medicine, is believed to have the potential to treat liver diseases by modulating the Toll-like receptor 4 (TLR4) target. Therefore, a thorough exploration of the effective components and therapeutic mechanisms targeting TLR4 in YCHD is a promising strategy for liver diseases. In this study, the AIGO-DTI deep learning framework was proposed to predict the targeting probability of major components in YCHD for TLR4. Comparative evaluations with four machine learning models (RF, SVM, KNN, XGBoost) and two deep learning models (GCN, GAT) demonstrated that the AIGO-DTI framework exhibited the best overall performance, with Recall and AUC reaching 0.968 and 0.991, respectively.This study further utilized the AIGO-DTI model to identify the potential impact of Isoscopoletin, a major component of YCHD, on TLR4. Subsequent wet experiments revealed that Isoscopoletin could influence the maturation of Dendritic Cells (DCs) induced by Lipopolysaccharide (LPS) through TLR4, suggesting its therapeutic potential for liver diseases, especially hepatitis. Additionally, based on the AIGO-DTI framework, this study established an online platform named TLR4-Predict to facilitate domain experts in discovering more compounds related to TLR4. Overall, the proposed AIGO-DTI framework accurately predicts unique compounds in YCHD that interact with TLR4, providing new insights for identifying and screening lead compounds targeting TLR4.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Discovery of Active Ingredient of Yinchenhao Decoction Targeting TLR4 for Hepatic Inflammatory Diseases Based on Deep Learning Approach.\",\"authors\":\"Sizhe Zhang, Peng Han, Haiqing Sun, Ying Su, Chen Chen, Cheng Chen, Jinyao Li, Xiaoyi Lv, Xuecong Tian, Yandan Xu\",\"doi\":\"10.1007/s12539-024-00670-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Yinchenhao Decoction (YCHD), a classic formula in traditional Chinese medicine, is believed to have the potential to treat liver diseases by modulating the Toll-like receptor 4 (TLR4) target. Therefore, a thorough exploration of the effective components and therapeutic mechanisms targeting TLR4 in YCHD is a promising strategy for liver diseases. In this study, the AIGO-DTI deep learning framework was proposed to predict the targeting probability of major components in YCHD for TLR4. Comparative evaluations with four machine learning models (RF, SVM, KNN, XGBoost) and two deep learning models (GCN, GAT) demonstrated that the AIGO-DTI framework exhibited the best overall performance, with Recall and AUC reaching 0.968 and 0.991, respectively.This study further utilized the AIGO-DTI model to identify the potential impact of Isoscopoletin, a major component of YCHD, on TLR4. Subsequent wet experiments revealed that Isoscopoletin could influence the maturation of Dendritic Cells (DCs) induced by Lipopolysaccharide (LPS) through TLR4, suggesting its therapeutic potential for liver diseases, especially hepatitis. Additionally, based on the AIGO-DTI framework, this study established an online platform named TLR4-Predict to facilitate domain experts in discovering more compounds related to TLR4. 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Discovery of Active Ingredient of Yinchenhao Decoction Targeting TLR4 for Hepatic Inflammatory Diseases Based on Deep Learning Approach.
Yinchenhao Decoction (YCHD), a classic formula in traditional Chinese medicine, is believed to have the potential to treat liver diseases by modulating the Toll-like receptor 4 (TLR4) target. Therefore, a thorough exploration of the effective components and therapeutic mechanisms targeting TLR4 in YCHD is a promising strategy for liver diseases. In this study, the AIGO-DTI deep learning framework was proposed to predict the targeting probability of major components in YCHD for TLR4. Comparative evaluations with four machine learning models (RF, SVM, KNN, XGBoost) and two deep learning models (GCN, GAT) demonstrated that the AIGO-DTI framework exhibited the best overall performance, with Recall and AUC reaching 0.968 and 0.991, respectively.This study further utilized the AIGO-DTI model to identify the potential impact of Isoscopoletin, a major component of YCHD, on TLR4. Subsequent wet experiments revealed that Isoscopoletin could influence the maturation of Dendritic Cells (DCs) induced by Lipopolysaccharide (LPS) through TLR4, suggesting its therapeutic potential for liver diseases, especially hepatitis. Additionally, based on the AIGO-DTI framework, this study established an online platform named TLR4-Predict to facilitate domain experts in discovering more compounds related to TLR4. Overall, the proposed AIGO-DTI framework accurately predicts unique compounds in YCHD that interact with TLR4, providing new insights for identifying and screening lead compounds targeting TLR4.
期刊介绍:
Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology.
The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer.
The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.