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Deep neural networks to help identify, formulate advanced antiaging supplements

RATE THIS! +43
Posted in Science on 9th Jun, 2016 11:29 AM by AlexMuller

Insilico Medicine is a big-data analytics company specializing in applying advances in deep learning to discovery of biomarkers and drugs. Life Extension, a Florida-based organization established in the early 1980s, is a dietary-supplement innovator dedicated to extending healthy human longevity.

 
Insilico Medicine will focus on applying advanced signaling pathway activation analysis techniques and deep-learning algorithms to find nutraceuticals that mimic the tissue-specific transcriptional response of many known interventions and pathways associated with health and longevity.
 
Life Extension will use this information to develop novel nutraceutical products to support health and longevity, such as “geroprotectors”, precision natural organic small-molecule formulations that slow down or even reverse age-associated conditions and damage.
 
“Senescence is accompanied by a shift in cellular signaling that initiates and promotes a system-wide degenerative condition,” William Faloon, co-founder of the Life Extension Foundation, told KurzweilAI. “By turning back ‘on’ youthful signaling pathways while suppressing those that emerge during normal maturation, we expect to gain significant control over what are now thought to be inevitable pathologies of old age.”
 
“This collaboration means a lot to our team. For many years I was buying a range of supplements from Life Extension, and saw how passionate they are about extending healthy human life and supporting cutting-edge research in the field,” said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc. and the chief science officer of the Biogerontology Research Foundation in the UK.
 
“Life Extension’s discovery research and product development teams have decades of experience in the pharmaceutical and nutraceutical research and have set very high standards for science-based nutraceutical discovery.”
 
Insilico Medicine scientists will present the results of this collaboration at RAADFest in San Diego in August, a longevity-focused conference.
 
Insilico Medicine scientists have recently authored key papers on applying deep-learning techniques to biomedical applications in influential peer-reviewed journals, including “Developing criteria for evaluation of geroprotectors as a key stage toward translation to the clinic,” published in Aging Cell.
 
Other papers include “Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data” in Molecular Pharmaceutics, a journal published by the American Chemical Society; “Applications of Deep Learning in Biomedicine“, also in Molecular Pharmaceutics; and “Deep biomarkers of human aging: Application of deep neural networks to biomarker development” in Aging, a high-impact journal in aging research.
 
“We believe that accelerating the rate of progress in novel biomarker identification, as well as precision nutrient formulation development, is critical,” said Andrew G. Swick, Ph.D., senior vice president of scientific affairs, discovery research and product development for Life Extension.
 
“Life Extension aims to improve health and human longevity by utilizing the experience of noted research scientists and engaging in academic and industry collaborations. Artificial intelligence is one of the promising areas, where years of experience in the nutraceutical industry combined with sophisticated biologically inspired algorithms and high-performance computing may produce unprecedented results.”

Tags: agingAIsoftwarehealthmedicineresearch

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Comments

Author: Guest
Posted: 2016-06-09
+1
I am not sure that we have enough information to develop novel nutraceutical products to support health and longevity such as “geroprotectors”- further research is essential Reply
Author: Guest
Posted: 2016-06-09
+0
The scientists propose to turn back ‘on’ youthful signaling pathways while suppressing those that emerge during ageing. The problem here is that many pathways have multiple functions and effects can not be easily controlled Reply


 

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