Senior Scientist Fedor Galkin from Insilico Medicine in Abu Dhabi, UAE, joins Dr. Evgeniy Galimov to discuss a research paper he co-authored in Volume 17, Issue 8 of Aging-US, titled “AI-driven toolset for IPF and aging research associates lung fibrosis with accelerated aging.”

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

Corresponding author - Alex Zhavoronkov - [email protected]

Video interview - https://www.youtube.com/watch?v=PV6DyIV7X7U

Abstract video - https://www.youtube.com/watch?v=24lX2lHbt7o

Longevity & Aging Series - https://www.aging-us.com/longevity

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.

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Keywords - aging, IPF, generative AI, transformer, proteomics

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