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Integrating machine learning, deep learning, and docking to predict aristolochic acid A carcinogenesis
Longzhu Li1, Jiacheng Liao1, Xintian Chen2
1Guangdong 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.
Frontiers in Pharmacology
|August 4, 2026
Summary
Aristolochic acid A (AAA) causes kidney cancer. This study used AI and lab tests to find AAA targets, identifying PYGL as a key gene involved in AAA-induced renal clear cell carcinoma (RCC) development.
Area of Science:
- Toxicology
- Computational Biology
- Oncology
Background:
- Renal clear cell carcinoma (RCC) is a significant health concern.
- Aristolochic acid A (AAA) is a known nephrotoxic and carcinogenic agent implicated in RCC.
- Understanding the molecular mechanisms of AAA-induced RCC is crucial for developing effective prevention and treatment strategies.
Purpose of the Study:
- To elucidate the molecular mechanisms underlying Aristolochic acid A (AAA)-induced renal clear cell carcinoma (RCC).
- To identify key target genes and pathways affected by AAA exposure using a multi-omics and computational approach.
- To validate the role of identified targets in RCC pathogenesis through in vitro experiments.
Main Methods:
- Differential gene expression analysis across multiple datasets to identify AAA-responsive genes.
- Network toxicology, machine learning, and deep learning for identifying core regulatory genes.
- Molecular docking and dynamics simulations to assess binding interactions between AAA and target proteins.
- In vitro validation using Western blot assays and cell culture models.
Main Results:
- Identified 74 potential AAA target genes in RCC, with seven identified as key regulators by machine learning.
- Deep learning highlighted PYGL, ADH1B, PTGS1, EDNRA, and AURKA as significant drivers.
- Molecular docking confirmed strong binding affinities for AAA with these targets.
- In vitro studies validated PYGL as a direct target, showing elevated expression in RCC cells and its involvement in epithelial-mesenchymal transition (EMT).
Conclusions:
- This study elucidates a key toxicity mechanism of AAA in RCC through integrated computational and experimental approaches.
- PYGL is identified as a critical molecular target in AAA-induced RCC.
- The developed framework offers an efficient approach for toxicological studies, particularly valuable when clinical samples are scarce.