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Ageing Res Rev. 2019 Jan;49:49-66. doi: 10.1016/j.arr.2018.11.003. Epub 2018 Nov 22.

Artificial intelligence for aging and longevity research: Recent advances and perspectives.

Author information

1
Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Baltimore, MD, United States; Biogerontology Research Foundation, London, United Kingdom; Buck Institute for Research on Aging, Novato, CA, United States.
2
Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Baltimore, MD, United States; Department of Computer Science, University of Oxford, Oxford, United Kingdom.
3
Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Baltimore, MD, United States. Electronic address: vanhaelen@insilicomedicine.com.
4
Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Denmark.
5
George Mason University, Fairfax, VA, United States.
6
Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Baltimore, MD, United States.

Abstract

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.

KEYWORDS:

Aging biomarker; Artificial intelligence; Deep learning; Drug discovery; Generative adversarial networks; Metalearning; Reinforcement learning; Symbolic learning

PMID:
30472217
DOI:
10.1016/j.arr.2018.11.003
[Indexed for MEDLINE]
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