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ElixirSeeker: A Machine Learning Framework Utilizing Fusion Molecular Fingerprints for the Discovery of Lifespan‐Extending Compounds.

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Název: ElixirSeeker: A Machine Learning Framework Utilizing Fusion Molecular Fingerprints for the Discovery of Lifespan‐Extending Compounds.
Autoři: Pan, Yan, Cai, Hongxia, Ye, Fang, Xu, Wentao, Huang, Zhihang, Zhu, Jingyuan, Gong, Yiwen, Li, Yutong, Ezemaduka, Anastasia Ngozi, Gao, Shan, Liu, Shunqi, Li, Guojun, Li, Hao, Yang, Jing, Ning, Junyu, Xian, Bo
Zdroj: Aging Cell; Aug2025, Vol. 24 Issue 8, p1-18, 18p
Témata: AGING prevention, DRUG discovery, CAENORHABDITIS elegans, MACHINE learning, LONGEVITY
Abstrakt: Despite the growing interest in developing anti‐aging drugs, high costs and low success rates of traditional drug discovery methods pose significant challenges. Aging is a complex biological process associated with numerous diseases, making the identification of compounds that can modulate aging mechanisms critically important. Accelerating the discovery of potential anti‐aging compounds is essential to overcome these barriers and enhance lifespan and healthspan. Here, we present ElixirSeeker, a machine learning framework designed to maximize feature capture of lifespan‐extending compounds through multi‐fingerprint fusion mechanisms. Utilizing this approach, we identified several promising candidate drugs from external compound databases. We tested the top six hits in Caenorhabditis elegans and found that four of these compounds—including Praeruptorin C, Polyphyllin VI, Thymoquinone, and Medrysone—extended the organism's lifespan. This study demonstrates that ElixirSeeker effectively accelerates the identification of viable anti‐aging compounds, potentially reducing costs and increasing the success rate of drug development in this field. [ABSTRACT FROM AUTHOR]
Copyright of Aging Cell is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: ElixirSeeker: A Machine Learning Framework Utilizing Fusion Molecular Fingerprints for the Discovery of Lifespan‐Extending Compounds.
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  Data: <searchLink fieldCode="AR" term="%22Pan%2C+Yan%22">Pan, Yan</searchLink><br /><searchLink fieldCode="AR" term="%22Cai%2C+Hongxia%22">Cai, Hongxia</searchLink><br /><searchLink fieldCode="AR" term="%22Ye%2C+Fang%22">Ye, Fang</searchLink><br /><searchLink fieldCode="AR" term="%22Xu%2C+Wentao%22">Xu, Wentao</searchLink><br /><searchLink fieldCode="AR" term="%22Huang%2C+Zhihang%22">Huang, Zhihang</searchLink><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Jingyuan%22">Zhu, Jingyuan</searchLink><br /><searchLink fieldCode="AR" term="%22Gong%2C+Yiwen%22">Gong, Yiwen</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Yutong%22">Li, Yutong</searchLink><br /><searchLink fieldCode="AR" term="%22Ezemaduka%2C+Anastasia+Ngozi%22">Ezemaduka, Anastasia Ngozi</searchLink><br /><searchLink fieldCode="AR" term="%22Gao%2C+Shan%22">Gao, Shan</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Shunqi%22">Liu, Shunqi</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Guojun%22">Li, Guojun</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Hao%22">Li, Hao</searchLink><br /><searchLink fieldCode="AR" term="%22Yang%2C+Jing%22">Yang, Jing</searchLink><br /><searchLink fieldCode="AR" term="%22Ning%2C+Junyu%22">Ning, Junyu</searchLink><br /><searchLink fieldCode="AR" term="%22Xian%2C+Bo%22">Xian, Bo</searchLink>
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  Data: Aging Cell; Aug2025, Vol. 24 Issue 8, p1-18, 18p
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  Data: <searchLink fieldCode="DE" term="%22AGING+prevention%22">AGING prevention</searchLink><br /><searchLink fieldCode="DE" term="%22DRUG+discovery%22">DRUG discovery</searchLink><br /><searchLink fieldCode="DE" term="%22CAENORHABDITIS+elegans%22">CAENORHABDITIS elegans</searchLink><br /><searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br /><searchLink fieldCode="DE" term="%22LONGEVITY%22">LONGEVITY</searchLink>
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  Label: Abstract
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  Data: Despite the growing interest in developing anti‐aging drugs, high costs and low success rates of traditional drug discovery methods pose significant challenges. Aging is a complex biological process associated with numerous diseases, making the identification of compounds that can modulate aging mechanisms critically important. Accelerating the discovery of potential anti‐aging compounds is essential to overcome these barriers and enhance lifespan and healthspan. Here, we present ElixirSeeker, a machine learning framework designed to maximize feature capture of lifespan‐extending compounds through multi‐fingerprint fusion mechanisms. Utilizing this approach, we identified several promising candidate drugs from external compound databases. We tested the top six hits in Caenorhabditis elegans and found that four of these compounds—including Praeruptorin C, Polyphyllin VI, Thymoquinone, and Medrysone—extended the organism's lifespan. This study demonstrates that ElixirSeeker effectively accelerates the identification of viable anti‐aging compounds, potentially reducing costs and increasing the success rate of drug development in this field. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Aging Cell is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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