Evidence map›Paper›PMID 42185665›Full record

ArticleArchives of toxicology2026

Structure-based machine learning model for discovering pregnane X receptor (PXR) agonists and biological activity validation.

Fang-Fang Huang, Ying Luo, Hao Chen, Yu-Huan Meng, Wei Li, Hong Liang, Chun-Zhi Ai

Abstract read
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Article in Archives of toxicology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Fang-Fang HuangKey Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Guangxi Key Laboratory of Chemistry and Molecular Engineering of Medicinal Resources, University Engineering Research Center for Chemistry of Characteristic Medicinal Resources (Guangxi), School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, 15 Yucai Road, Guilin, 541004, People's Republic of China.
Ying LuoKey Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Guangxi Key Laboratory of Chemistry and Molecular Engineering of Medicinal Resources, University Engineering Research Center for Chemistry of Characteristic Medicinal Resources (Guangxi), School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, 15 Yucai Road, Guilin, 541004, People's Republic of China.
Hao ChenKey Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Guangxi Key Laboratory of Chemistry and Molecular Engineering of Medicinal Resources, University Engineering Research Center for Chemistry of Characteristic Medicinal Resources (Guangxi), School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, 15 Yucai Road, Guilin, 541004, People's Republic of China.
Yu-Huan MengKey Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Guangxi Key Laboratory of Chemistry and Molecular Engineering of Medicinal Resources, University Engineering Research Center for Chemistry of Characteristic Medicinal Resources (Guangxi), School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, 15 Yucai Road, Guilin, 541004, People's Republic of China.
Wei LiTranslational Medicine Research Institute, College of Medicine, Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, Yangzhou University, 136 Jiangyangzhong Road, Yangzhou, 225001, People's Republic of China. weili@yzu.edu.cn.
Hong LiangKey Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Guangxi Key Laboratory of Chemistry and Molecular Engineering of Medicinal Resources, University Engineering Research Center for Chemistry of Characteristic Medicinal Resources (Guangxi), School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, 15 Yucai Road, Guilin, 541004, People's Republic of China. hliang@gxnu.edu.cn.
Chun-Zhi AiKey Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Guangxi Key Laboratory of Chemistry and Molecular Engineering of Medicinal Resources, University Engineering Research Center for Chemistry of Characteristic Medicinal Resources (Guangxi), School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, 15 Yucai Road, Guilin, 541004, People's Republic of China. angelina_ai@163.com.ORCID 0000-0002-4194-6690

Funding

Natural Science Foundation of Guangxi Province of China 2024JJA140936State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources CMEMR2020-B09the Joint Funds of the National Natural Science Foundation of China U25A20598the National Natural Science Foundation of China the National Natural Science Foundation of China
6 · The paper itself

Abstract

Pregnane X receptor (PXR), a nuclear receptor superfamily member, maintains bile acid homeostasis by regulating metabolic enzymes [e.g., cytochrome P450 3A4 (CYP3A4), uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1)] and multidrug resistance protein 1 transporter, and alleviates liver/intestinal inflammation via inhibiting the nuclear factor kappa-B pathway, serving as a critical therapeutic target for cholestatic liver diseases and inflammatory bowel disease. In this study, we established a novel structure-based machine learning strategy to identify PXR agonists from a natural product database. With generated bioactive conformations binding with PXR, we integrated ligand-based and structure-based features to construct a comprehensive machine learning model using light gradient boosting machine. This model illustrated an R

Indexed as

Drug DiscoveryMachine LearningPregnane X ReceptorBoosting Machine Learning AlgorithmsHep G2 CellsHumansLigandsPredictive Learning ModelsStructure-Activity RelationshipLigandsPregnane X ReceptorAgonistsIn vitro assayPregnane X receptorStructure-based machine learning

Identifiers

PMID42185665

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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.