Machine Learning Could Shorten the Process Of Developing Drugs That Slow Aging

In Education

According to the latest research published in the Nature Communications Journal, machine learning, a form of artificial intelligence, could revolutionize drug discovery. This technology significantly speeds up the process and reduces costs, making it a cost-effective alternative.

Senolytic drugs can slow ageing

Researchers employed advanced tech in a recent study to discover three potential senolytic drug candidates that can slow down ageing and prevent age-related illnesses. Senolytic drug work by eliminating senescent cells, which are metabolically active but unable to reproduce, often referred to as “zombie cells.”

It is vital to note that the senescent cells’ inability to replicate isn’t necessarily a bad thing. The cells with damaged DNA can halt replication to curtail the spread of damage due to external factors such as sun exposure. However, they’re not always a good thing since they can as well release inflammatory proteins that could affect nearby cells. Notably, the accumulation of such cells s associated with various diseases like type II diabetes, cancer, osteoarthritis, and pulmonary fibrosis.

Research conducted on mice has revealed that senescent cells can be effectively eliminated using analytics, resulting in improved conditions for certain diseases. Senolytics have the ability to selectively kill off these unhealthy cells while preserving the healthy ones.

Although approximately 80 senolytic drugs have been identified, only a pair consisting of quercetin and dasatinib has undergone testing in humans. Discovering additional analytics that can be utilized for various diseases would be highly beneficial.

Machine learning used in identifying senolytic drugs

In the latest study, researchers sought to understand if it is possible to train machine learning models in senolytic drug identification. Researchers fed AI models with samples of non-senolytics and senolytics, and the ML models managed to differentiate them. Therefore this could be important in predicting whether molecules they are not familiar with could be senolytics,

The researchers’ AI model managed to identify 21 molecules that had a high chance of being senolytics. If the researchers had tested the molecules in the lab, it could have taken time, with costs running upwards of £50,000 for the purchase of the molecules.

Mobile Sliding Menu