Research Paper|Volume 17, Issue 8|pp 1999—2014

AI-driven toolset for IPF and aging research associates lung fibrosis with accelerated aging

Fedor Galkin¹, Shan Chen², Alex Aliper¹, Alex Zhavoronkov^1,^2,^3,⁴, Feng Ren²

¹Insilico Medicine AI Ltd., Abu Dhabi, UAE
²Insilico Medicine Shanghai, Ltd., Shanghai, China
³Insilico Medicine US, Inc., New York, NY 10010, USA
⁴Insilico Medicine Hong Kong, Ltd., Hong Kong, China

Received: January 17, 2025Accepted: July 15, 2025Published: August 8, 2025

https://doi.org/10.18632/aging.206295

Copyright: © 2025 Galkin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Idiopathic pulmonary fibrosis (IPF) is a condition predominantly affecting the elderly and leading to a decline in lung function. Our study investigates the aging-related mechanisms in IPF using artificial intelligence (AI) approaches. We developed a pathway-aware proteomic aging clock using UK Biobank data and applied it alongside a specialized version of Precious3GPT (ipf-P3GPT) to demonstrate an AI-driven mode of IPF research. The aging clock shows great performance in cross-validation (R²=0.84) and its utility is validated in an independent dataset to show that severe cases of COVID-19 are associated with an increased aging rate. Computational analysis using ipf-P3GPT revealed distinct but overlapping molecular signatures between aging and IPF, suggesting that IPF represents a dysregulation rather than mere acceleration of normal aging processes. Our findings establish novel connections between aging biology and IPF pathogenesis while demonstrating the potential of AI-guided approaches in therapeutic development for age-related diseases.