ReviewFrontiers in aging2026
Advances in anti-aging Drug research leveraging multi-omics and artificial intelligence.
Review in Frontiers in aging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The intensifying global population aging has rendered the development of anti-aging drugs a core challenge in the life sciences domain. Traditional models struggle to address the systemic and networked nature of aging. Multi-omics technologies provide a panoramic perspective for deciphering molecular networks of aging, while Artificial Intelligence (AI) has demonstrated significant advantages in target discovery, drug screening, and clinical trial optimization. This review systematically elaborates on the pathological characteristics and molecular mechanisms of aging, analyzes the application paradigms of multi-omics in biomarker screening, mechanism elucidation, and high-throughput screening, and discusses the core value and technical bottlenecks of AI in target prediction, virtual screening, and trial design. Simultaneously, this paper addresses critical challenges including ethical controversies and data standardization currently confronting the field, and explores the prospects of precision anti-aging drug development and personalized treatment strategies driven by deep integration of multi-omics and AI. This approach offers novel theoretical frameworks and practical pathways for extending human healthspan.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.