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However, hPSC\u2010derived lineages typically represent a fetal stage of development, and methods to expedite the transition to an aged identity to improve modeling of late\u2010onset disease are limited. In this study, we introduce RNAge, a transcriptome\u2010based computational platform designed to enable the evaluation of an induced aging or a rejuvenated state. We validated this approach across independent datasets spanning different tissues and species, and show that it can be used to evaluate the effectiveness of existing age\u2010retaining or age\u2010modulating interventions. We also used RNAge to perform an in silico compound screen using the LINCS L1000 dataset. This approach led to the identification and experimental confirmation of several novel compounds capable of inducing aging or rejuvenation in primary fibroblasts or hPSC\u2010derived neurons. 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Our study offers a robust method for quantifying age\u2010related manipulations and unveils compounds that significantly broaden the toolkit for age\u2010modifying strategies in hPSC\u2010derived lineages.<\/jats:p>","DOI":"10.1111\/acel.70075","type":"journal-article","created":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:40:36Z","timestamp":1746060036000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Identifying Age\u2010Modulating Compounds Using a Novel Computational Framework for Evaluating Transcriptional Age"],"prefix":"10.1111","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8019-7998","authenticated-orcid":false,"given":"Chao","family":"Zhang","sequence":"first","affiliation":[{"name":"Section of Computational Biomedicine Boston University School of Medicine  Boston Massachusetts USA"},{"name":"Institute for Computational Biomedicine Weill Cornell Medicine  New York New York 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