通过可解释的人工智能平台AgeXtend发现老年保护剂
Sakshi Arora1, Aayushi Mittal1, Subhadeep Duari1
1Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), New Delhi, India.
Nature aging
|December 3, 2024
概括
研究人员开发了AgeXtend,一个AI平台,以发现新的衰老减缓化合物. 这种工具成功地识别了已知的长寿分子,并选了数十亿种化合物,在模型生物和人体细胞中验证了新型体质保护剂.
科学领域:
- 生物遗传学 生物遗传学
- 计算生物学 计算生物学
- 代谢学 代谢学 代谢学
背景情况:
- 衰老的特点是代谢变化影响细胞健康,许多代谢物的作用仍然未知.
- 研究已知促进长寿的化合物为人们提供了关于衰老机制的见解,并有助于发现新的老年保护剂.
研究的目的:
- 介绍AgeXtend,这是一个人工智能驱动的平台,用于预测老年保护性化合物.
- 验证AgeXtend在识别新型长寿分子和理解衰老途径方面的能力.
主要方法:
- 开发了AgeXtend,这是一个集成已知的老年保护体生物活性数据的AI平台,用于预测,毒性评估和机制识别.
- 使用AgeXtend.tend.选了大约11亿种化合物.
- 通过酵母和Caenorhabditis elegans的寿命测定验证了预测的老年保护剂,并研究了微生物组衍生代谢物.
- 在人类纤维细胞衰老试验中,评估了内源代谢物作为老年调节剂.
主要成果:
- AgeXtend准确地识别了已知的老年保护剂 (如甲胺和氨酸) 的益寿效应,即使是那些不用于训练的药物.
- 该平台有助于识别和实验验证许多新型潜在的老年保护剂.
- 内源代谢物被确定为潜在的老年调节剂,为衰老研究提供了新的途径.
结论:
- AgeXtend是一个强大的AI工具,用于发现老年保护剂和阐明衰老机制.
- 该平台的研究结果突显出未经探索的化合物和内源代谢物的潜力,可以调节衰老.
- 这项工作促进了对衰老中的代谢网络的理解,并为确定长寿干预措施提供了可扩展的方法.
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