TY - JOUR

T1 - Artificial intelligence for aging and longevity research

T2 - Recent advances and perspectives

AU - Zhavoronkov, Alex

AU - Mamoshina, Polina

AU - Vanhaelen, Quentin

AU - Scheibye-Knudsen, Morten

AU - Moskalev, Alexey

AU - Aliper, Alex

PY - 2019

Y1 - 2019

N2 - The applications of modern artificial intelligence (AI) algorithms within the field of aging research offer tremendous opportunities. Aging is an almost universal unifying feature possessed by all living organisms, tissues, and cells. Modern deep learning techniques used to develop age predictors offer new possibilities for formerly incompatible dynamic and static data types. AI biomarkers of aging enable a holistic view of biological processes and allow for novel methods for building causal models—extracting the most important features and identifying biological targets and mechanisms. Recent developments in generative adversarial networks (GANs) and reinforcement learning (RL) permit the generation of diverse synthetic molecular and patient data, identification of novel biological targets, and generation of novel molecular compounds with desired properties and geroprotectors. These novel techniques can be combined into a unified, seamless end-to-end biomarker development, target identification, drug discovery and real world evidence pipeline that may help accelerate and improve pharmaceutical research and development practices. Modern AI is therefore expected to contribute to the credibility and prominence of longevity biotechnology in the healthcare and pharmaceutical industry, and to the convergence of countless areas of research.

AB - The applications of modern artificial intelligence (AI) algorithms within the field of aging research offer tremendous opportunities. Aging is an almost universal unifying feature possessed by all living organisms, tissues, and cells. Modern deep learning techniques used to develop age predictors offer new possibilities for formerly incompatible dynamic and static data types. AI biomarkers of aging enable a holistic view of biological processes and allow for novel methods for building causal models—extracting the most important features and identifying biological targets and mechanisms. Recent developments in generative adversarial networks (GANs) and reinforcement learning (RL) permit the generation of diverse synthetic molecular and patient data, identification of novel biological targets, and generation of novel molecular compounds with desired properties and geroprotectors. These novel techniques can be combined into a unified, seamless end-to-end biomarker development, target identification, drug discovery and real world evidence pipeline that may help accelerate and improve pharmaceutical research and development practices. Modern AI is therefore expected to contribute to the credibility and prominence of longevity biotechnology in the healthcare and pharmaceutical industry, and to the convergence of countless areas of research.

KW - Aging biomarker

KW - Artificial intelligence

KW - Deep learning

KW - Drug discovery

KW - Generative adversarial networks

KW - Metalearning

KW - Reinforcement learning

KW - Symbolic learning

U2 - 10.1016/j.arr.2018.11.003

DO - 10.1016/j.arr.2018.11.003

M3 - Review

C2 - 30472217

AN - SCOPUS:85057217558

VL - 49

SP - 49

EP - 66

JO - Ageing Research Reviews

JF - Ageing Research Reviews

SN - 1568-1637

ER -