Evidence map›Paper›PMID 42548907›Full record

ArticleFrontiers in pharmacology2026

Integrating machine learning, deep learning, and docking to predict aristolochic acid A carcinogenesis.

Longzhu Li, Jiacheng Liao, Xintian Chen, Zeqiong Lin, Siqiao Gong, Junmin Huang, Ziqian Bi, Tianyang Wang, Xinliang Chia, Lu Chen and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

14 authors.

Longzhu Li *Guangdong Provincial Key Laboratory of Autophagy and Major Chronic Non-Communicable Diseases, Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
Jiacheng Liao *Guangdong Provincial Key Laboratory of Autophagy and Major Chronic Non-Communicable Diseases, Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
Xintian Chen *Department of Gastroenterology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
Zeqiong LinGuangdong Provincial Key Laboratory of Autophagy and Major Chronic Non-Communicable Diseases, Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
Siqiao GongGuangdong Provincial Key Laboratory of Autophagy and Major Chronic Non-Communicable Diseases, Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
Junmin HuangNational Clinical Key Specialty Construction Program (2023), Institute of Nephrology, Department of Nephrology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
Ziqian BiDepartment of Computer and Information Technology, Purdue Polytechnic Institute, Purdue University, West Lafayette, IN, United States.
Tianyang WangSchool of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, China.
Xinliang ChiaJBT Technology Corp, Tainan City, Taiwan.
Lu ChenNational Clinical Key Specialty Construction Program (2023), Institute of Nephrology, Department of Nephrology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
Yongzhi XuNational Clinical Key Specialty Construction Program (2023), Institute of Nephrology, Department of Nephrology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
Huafeng LiuGuangdong Provincial Key Laboratory of Autophagy and Major Chronic Non-Communicable Diseases, Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
Junfeng HaoNational Clinical Key Specialty Construction Program (2023), Institute of Nephrology, Department of Nephrology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
Jiansong QiGuangdong Provincial Key Laboratory of Autophagy and Major Chronic Non-Communicable Diseases, Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study investigates the molecular mechanisms of renal clear cell carcinoma (RCC) induced by Aristolochic acid A (AAA) using machine learning, deep learning, and molecular docking approaches. Methods: To identify AAA target genes associated with RCC, differential expression analysis was performed on multiple datasets. Network toxicology, machine learning, deep learning, and molecular docking were used to explore the binding interactions between AAA and target proteins. The top candidate gene was validated using molecular dynamics simulation and Results: A total of 74 genes were identified as potential targets in AAA-induced RCC. Subsequent machine learning analysis identified seven core genes as key regulators of RCC. Deep learning classification further highlighted five of these seven genes, including PYGL, ADH1B, PTGS1, EDNRA, and AURKA. Additionally, molecular docking simulations revealed strong binding affinities between AAA and these target proteins. Molecular dynamics simulation demonstrated the binding stability of the AAA-PYGL complex, and Conclusion: By combining advanced computational methods with

Indexed as

aristolochic acid Abioinformaticsdeep learningmachine learningmolecular dockingmolecular dynamics simulationrenal cell carcinoma

Identifiers

PMID42548907
PMCPMC13429957

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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 Socratic graph.