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
SN - 1568-1637
VL - 49
SP - 49
EP - 66
JO - Ageing Research Reviews
JF - Ageing Research Reviews
ER -