Screening for Aging Mechanisms
Insilico Medicine announced earlier this year that it started a collaboration with Life Extension to apply bioinformatics and deep learning to screen for naturally occurring compounds that could slow, even reverse, cellular and molecular aging mechanisms. Based on the compounds that rise to the top for efficacy in the algorithms, Life Extension is launching a nutraceutical product line, called GeroProtect, according to an Insilico press release.
“… we trained the deep neural networks to predict the age of the skin and look through multiple layers of the skin. Then, we look for the most important genes that are contributing significantly to the accuracy of the predictor,” he says. “So, we see what genes are most important in aging. Then, we construct specific age-associated pathways and see what kind of molecules — pharmaceutical-grade molecules (sometimes, nutraceuticals) — can reverse that signature of aging or specific disease. That’s how we go about drug and nutraceutical discovery.”
The AI process, he says, can reduce research time from years to weeks.
And in the near future, the proof of whether or not a nutraceutical, cosmeceutical, filler or other cosmetic procedure is working will lie with the deep neural network, according to Dr. Zhavoronkov.
“If the deep neural network is predicting somebody’s age, just by looking at their skin or a skin biopsy … the deep neural network can be trained to recognize patterns, or fixtures, but also other data types,” he says. “So, you can apply the cosmeceutical or a cosmetic or a medicinal product or an injection, a filler — basically any kind of intervention — and see the difference before and after application of that intervention. The deep neural network would be very accurate and show you whether there was a change and, also, whether this change was beneficial or even harmful to the person.”