Discovery of Active Ingredient of Yinchenhao Decoction Targeting TLR4 for Hepatic Inflammatory Diseases Based on Deep Learning Approach

被引:0
作者
Zhang, Sizhe [1 ]
Han, Peng [2 ]
Sun, Haiqing [1 ]
Su, Ying [3 ]
Chen, Chen [3 ]
Chen, Cheng [1 ]
Li, Jinyao [2 ]
Lv, Xiaoyi [1 ]
Tian, Xuecong [3 ]
Xu, Yandan [4 ]
机构
[1] Xinjiang Univ, Coll Software, Urumqi 830046, Peoples R China
[2] Xinjiang Univ, Coll Life Sci & Technol, Xinjiang Key Lab Biol Resources & Genet Engn, Urumqi 830017, Peoples R China
[3] Xinjiang Univ, Coll Informat Sci & Engn, Urumqi 830046, Peoples R China
[4] Quzhou Kecheng Peoples Hosp, Quzhou 324000, Peoples R China
关键词
Drug discovery; Traditional Chinese medicine; Toll-like receptor 4; Deep learning; Liver disease; HEPATOCELLULAR-CARCINOMA; PREDICTION;
D O I
10.1007/s12539-024-00670-7
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:293 / 305
页数:13
相关论文
共 62 条
[1]   Burden of liver diseases in the world [J].
Asrani, Sumeet K. ;
Devarbhavi, Harshad ;
Eaton, John ;
Kamath, Patrick S. .
JOURNAL OF HEPATOLOGY, 2019, 70 (01) :151-171
[2]  
Awad M., 2015, DIMACS Ser. Discrete. Math. Theor. Comput. Sci, P39, DOI [10.1007/978-1-4302-5990-9_4, DOI 10.1007/978-1-4302-5990-9]
[3]   The properties of known drugs .1. Molecular frameworks [J].
Bemis, GW ;
Murcko, MA .
JOURNAL OF MEDICINAL CHEMISTRY, 1996, 39 (15) :2887-2893
[4]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[5]   One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome [J].
Capecchi, Alice ;
Probst, Daniel ;
Reymond, Jean-Louis .
JOURNAL OF CHEMINFORMATICS, 2020, 12 (01)
[6]   Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals [J].
Chen, Chi ;
Ye, Weike ;
Zuo, Yunxing ;
Zheng, Chen ;
Ong, Shyue Ping .
CHEMISTRY OF MATERIALS, 2019, 31 (09) :3564-3572
[7]   Rutin alleviates ventilator-induced lung injury by inhibiting NLRP3 inflammasome activation [J].
Chen, Shengsong ;
Bai, Yu ;
Xia, Jingen ;
Zhang, Yi ;
Zhan, Qingyuan .
ISCIENCE, 2023, 26 (10)
[8]   XGBoost: A Scalable Tree Boosting System [J].
Chen, Tianqi ;
Guestrin, Carlos .
KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, :785-794
[9]  
Chen X, 2023, CARBON BAL MANAGE, V18, DOI [10.1186/s13021-023-00225-1, 10.1186/s13020-023-00761-5]
[10]  
CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411