Can Artificial Intelligence Help Extend Life Span? Researchers Turn To AI To Uncover Aging Biomarkers
Insilico Medicine announced on Feb. 3 that it had signed a Memorandum of Understanding and partnered for the first collaborative research project with one of the largest research medical networks, Gachon University at Gil Medical Center. The purpose of this collaboration is to develop A.I. biomarkers of aging, along with a method to slow down the aging process.
The highlight of this collaboration is geared toward developing a way to stop age-related loss of function.
Insilico Medicine's Collaboration With Gachon University
The parties involved in this new collaboration have expressed their enthusiasm about working together on new projects.
"We are happy to collaborate with Insilico Medicine, one of the leaders in AI with a specific focus on practical aging research in the pharmaceutical and healthcare industries," noted Dr. Lee Uhn, Director of AI-based Precision Medicine at Gachon University, Gil Medical Center.
But this is not the only project of Insilico Medicine, as the company is currently testing nutraceutical products in partnership with Life Extension.
Insilico Medicine uses deep learning algorithms to calculate data points from multiple universities. The research is aimed at identifying compounds with anti-aging properties, which will then be employed in creating nutraceuticals. Nutraceuticals are anti-aging formulas administered to people for longer and better life.
As part of the company's attempt to create drugs with anti-aging properties, its pharmaceutical AI division published a paper proving the range of practical applications of its deep learning algorithms when it comes to finding drugs, aging research and biomarker development.
Insilico Medicine And Anti-Aging Research
Another paper, published by Insilico Medicine in the journal Molecular Pharmaceutics, proves the functionality of deep neural networks in the prediction of therapeutic class of molecules through transcriptional response data. The research was awarded the American Chemical Society Editors' Choice Award.
"Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles," noted the research.
Additionally, the company's AI division also published a paper in the journal Aging. The article shows the capacity to guess the subject's age with just a blood test, and it became the second most popular article in the history of the journal.
"One of the major impediments in human aging research is the absence of a comprehensive and actionable set of biomarkers that may be targeted and measured to track the effectiveness of therapeutic interventions. The ensemble approach may facilitate integration of multi-modal data linked to chronological age and sex that may lead to simple, minimally invasive, and affordable methods of tracking integrated biomarkers of aging in humans and performing cross-species feature importance analysis," noted the research.
Anti-aging research has gained popularity during the last years, and more scientific researchers have oriented their efforts toward slowing down the process of aging. One of the most recent breakthroughs in this area was made public in December 2016.
Scientists from the Salk Institute in the United States have managed to reverse the aging process in mice cells, by manipulating four genes called the "Yamanaka Factors." The cells were converted back to their embryonic state, and this reversion of the aging process has opened a new line of possibilities in aging research.