AI-based strategies for diagnosis and therapeutic discovery in pulmonary fibrosis.
5 affiliations
- 1School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
- 2Department of Respiratory and Critical Care Medicine, Chengdu Integrated and Western Medical Hospital, Chengdu 610095, China.
- 3Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
- 4Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
- 5Tissue-Orientated Property of Chinese Medicine Key Laboratory of Sichuan Province, Natural and Biomimetic Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China.
Abstract
Pulmonary fibrosis (PF) is a life-threatening and therapeutically challenging disease, underscoring the urgent need for novel and effective interventions. Natural products, characterized by structural diversity, wide biological activities, and abundant availability, represent a promising reservoir for anti-fibrotic drug discovery. This review provides a comprehensive overview of the key risk factors and complex pathogenic mechanisms underlying PF, with a particular focus on the pro-fibrotic and anti-fibrotic targets that regulate disease progression. Special attention is given to high-value candidate targets identified through data-driven approaches, including members of the G protein-coupled receptor (GPCR) family. This study further summarizes the emerging roles of artificial intelligence (AI) in PF diagnosis and prognostic assessment, where deep learning models significantly enhance imaging interpretation and patient stratification. In addition, the anti-fibrotic mechanisms of natural products are systematically discussed, especially their modulation of fibroblast-to-myofibroblast transition (FMT), epithelial-mesenchymal transition (EMT), and endothelial-to-mesenchymal transition (EndMT). Building on these insights, this study proposes an AI-driven workflow for natural product-based anti-fibrotic drug discovery, integrating target identification, virtual screening, and experimental validation. This integrative strategy offers a conceptual and technical framework for accelerating the development of natural-product-derived therapeutics for PF.
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BibTeX
@article{lin2026aibased,
title = {{AI-based strategies for diagnosis and therapeutic discovery in pulmonary fibrosis.}},
author = {Yue Lin and Qianqian Liu and Xiangyi Lei and Huiyu Cao and Pengmian Feng and Yuemei Wang and Dan Sun and Guang Xin and Sanyin Zhang and Wei Chen},
journal = {Chinese herbal medicines},
year = {2026},
month = aug,
doi = {10.1016/j.chmed.2026.08.005},
url = {https://europepmc.org/article/MED/42948731},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - AI-based strategies for diagnosis and therapeutic discovery in pulmonary fibrosis.
AU - Yue Lin
AU - Qianqian Liu
AU - Xiangyi Lei
AU - Huiyu Cao
AU - Pengmian Feng
AU - Yuemei Wang
AU - Dan Sun
AU - Guang Xin
AU - Sanyin Zhang
AU - Wei Chen
PY - 2026
DA - 2026/08/25
JO - Chinese herbal medicines
DO - 10.1016/j.chmed.2026.08.005
UR - https://europepmc.org/article/MED/42948731
AB - Pulmonary fibrosis (PF) is a life-threatening and therapeutically challenging disease, underscoring the urgent need for novel and effective interventions. Natural products, characterized by structural diversity, wide biological activities, and abundant availability, represent a promising reservoir for anti-fibrotic drug discovery. This review provides a comprehensive overview of the key risk factors and complex pathogenic mechanisms underlying PF, with a particular focus on the pro-fibrotic and anti-fibrotic targets that regulate disease progression. Special attention is given to high-value candidate targets identified through data-driven approaches, including members of the G protein-coupled receptor (GPCR) family. This study further summarizes the emerging roles of artificial intelligence (AI) in PF diagnosis and prognostic assessment, where deep learning models significantly enhance imaging interpretation and patient stratification. In addition, the anti-fibrotic mechanisms of natural products are systematically discussed, especially their modulation of fibroblast-to-myofibroblast transition (FMT), epithelial-mesenchymal transition (EMT), and endothelial-to-mesenchymal transition (EndMT). Building on these insights, this study proposes an AI-driven workflow for natural product-based anti-fibrotic drug discovery, integrating target identification, virtual screening, and experimental validation. This integrative strategy offers a conceptual and technical framework for accelerating the development of natural-product-derived therapeutics for PF.
SN - 1674-6384
SN - 2589-3610
ER -