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Machine Learning to Unravel Anti-Ageing Compounds

机器学习揭示抗衰老化合物

基本信息

批准号：
BB/V010123/2
负责人：
Joao Magalhaes
金额：
$ 41.05万
依托单位：
University of Birmingham
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FV010123%2F2
关键词：
Machine Learning Unravel Anti Ageing

项目摘要

The ageing process is one of the greatest scientific mysteries of our time. Ageing of the population is also a major biomedical, social and economic challenge. In the past two decades, a number of drugs have been identified that can modulate ageing and preserve health in model organisms, from invertebrates like worms to mammals such as mice. In spite of this recent progress, understanding the best strategies to develop human anti-ageing interventions and preserve health in old age is still very incomplete. Besides, although a few anti-ageing drugs are being clinically tested as treatments for age-related diseases, including drugs initially associated with ageing in worms, new methods are necessary to identify and prioritize drugs that can be suitable for human applications. Indeed, identifying new longevity drugs is of widespread interest. In this project, we will develop new computational methods that take advantage of large amounts of multi-omics data available on the web, and our own compilations of the effects of hundreds of ageing-related drugs, to predict new drugs with anti-ageing properties. Specifically, methods will be developed that determine which attributes make some drugs extend animal lifespan. Furthermore, once we determine which attributes these drugs have in common, we can predict novel pro-longevity, health-promoting drugs. Worms will be employed for the experimental validation of the machine learning findings in this project because of their short lifespans and high fecundity. All methods developed will be made available to the scientific community to help guide experiments, including in other organisms. Moreover, we will develop a webserver for predicting life-extending compounds that will be made freely available online. Overall, this project will provide a significant impetus to employing predictive biology in ageing studies.

衰老过程是我们这个时代最大的科学谜团之一。人口老龄化也是一个重大的生物医学、社会和经济挑战。在过去的二十年里，从蠕虫等无脊椎动物到老鼠等哺乳动物，已经发现了许多可以调节模式生物衰老和保持健康的药物。尽管最近取得了这一进展，但了解制定人类抗衰老干预措施和保持老年健康的最佳战略仍然非常不完整。此外，尽管一些抗衰老药物正在进行临床试验，作为治疗与年龄有关的疾病，包括最初与蠕虫衰老有关的药物，但需要新的方法来确定和优先考虑适合人类应用的药物。事实上，发现新的长寿药物引起了广泛的兴趣。在这个项目中，我们将开发新的计算方法，利用网络上大量可用的多组学数据，以及我们自己对数百种与衰老有关的药物效果的汇编，来预测具有抗衰老特性的新药。具体来说，将开发方法来确定哪些属性使某些药物延长动物寿命。此外，一旦我们确定了这些药物的共同属性，我们就可以预测出新的延长寿命、促进健康的药物。由于蠕虫寿命短，繁殖力高，因此将在本项目中用于机器学习结果的实验验证。所有开发的方法都将提供给科学界，以帮助指导实验，包括在其他生物体中进行的实验。此外，我们将开发一个网络服务器，用于预测延长寿命的化合物，并将在网上免费提供。总的来说，这个项目将为在衰老研究中使用预测生物学提供重要的推动力。