Evidence map›Paper›PMID 39560852›Full record

ArticleInterdisciplinary sciences, computational life sciences2025

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

Sizhe Zhang, Peng Han, Haiqing Sun, Ying Su, Chen Chen, Cheng Chen, Jinyao Li, Xiaoyi Lv, Xuecong Tian, Yandan Xu

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In one paragraph

Article in Interdisciplinary sciences, computational life sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Sizhe Zhang *College of Software, Xinjiang University, Urumqi, 830046, China.
Peng Han *Xinjiang Key Laboratory of Biological Resources and Genetic Engineering, College of Life Science and Technology, Xinjiang University, Urumqi, 830017, China.
Haiqing SunCollege of Software, Xinjiang University, Urumqi, 830046, China.
Ying SuCollege of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China.
Chen ChenCollege of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China.
Cheng ChenCollege of Software, Xinjiang University, Urumqi, 830046, China.
Jinyao LiXinjiang Key Laboratory of Biological Resources and Genetic Engineering, College of Life Science and Technology, Xinjiang University, Urumqi, 830017, China.
Xiaoyi LvCollege of Software, Xinjiang University, Urumqi, 830046, China. xjuwawj01@163.com.
Xuecong TianCollege of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China. 599502625@qq.com.
Yandan XuQuzhou Kecheng People' S Hospital, Quzhou, 324000, China. flyingswordred@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Deep LearningDrugs, Chinese HerbalInflammationLiver DiseasesToll-Like Receptor 4AnimalsDendritic CellsHumansLipopolysaccharidesMedicine, Chinese TraditionalMiceDrugs, Chinese HerbalLipopolysaccharidesTLR4 protein, humanToll-Like Receptor 4Deep learningDrug discoveryLiver diseaseToll-like receptor 4Traditional Chinese medicine

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.