An Artificial Intelligence (AI) model which will identify chemical compounds that helps healthy aging has been built a team of chemists from the University of Surrey, in a move that would pave way for further pharmaceutical innovations with the mandate to extend a person’s life span.
The team in a paper published by Nature Communication’s Scientific Reports, built a machine learning model that would take data from the Drug Age database to know if a compound can actually extend the life of a translucent worm, ‘Caenorhabditis elegans’, which shares metabolism that is similar to that of humans. The shorter lifespan of the worms will afford the researchers the leverage to know the impact of the chemical compounds.
Three compounds that have an 80 percent capacity of elegans lifespan increment were singled out through artificial intelligence, they are:
- flavonoids (anti-oxidant pigments found in plants that promote cardiovascular health),
- fatty acids (such as omega 3), and
- Organooxygens (compounds that contain carbon to oxygen bonds, such as alcohol).
“Ageing is increasingly being recognized as a set of diseases in modern medicine, and we can apply the tools of the digital world, such as AI, to help slow down or protect against aging and age-related diseases. Our study demonstrates the revolutionary ability of AI to aid the identification of compounds with anti-aging properties.” Says Sofia Kapsiani, a co-author of the study, who is also a final year undergraduate student at the University of Surrey.
The lead author of the study and Senior Lecturer in Computational Chemistry at the University of Surrey, Dr. Brendan Howlin was emphatic when he declared:
“This research shows the power and potential of AI, which is a specialty of the University of Surrey, to drive significant benefits in human health.”
This new development is a coup in the field of medical and pharmaceutical science that has the tendency to make humans live like vampires ( aging slowly) and also bring significant human health benefits.
Reference: “Random forest classification for predicting lifespan-extending chemical compounds” by Sofia Kapsiani and Brendan J. Howlin, 5 July 2021, Scientific Reports.