FEB 27, 2019 6:00 AM PST

Biomarkers of Aging

Presented at: Drug Discovery 2019
C.E. Credits: P.A.C.E. CE Florida CE
Speaker
  • Founder and CEO, Insilico Medicine, Inc.
    Biography
      Dr. Zhavoronkov is the CEO of Insilico Medicine, a Rockville-based leader in the next-generation artificial intelligence technologies for drug discovery, biomarker development, and aging research. At Insilico he pioneered the applications of generative adversarial networks and reinforcement learning techniques for generating the novel molecular structures with the desired properties. He was the first to develop the deep-learned multi-modal predictors of age for drug and biomarker development. He set up the R&D centers in 6 countries including the United Kingdom, Korea, Russia, Hong Kong and Taiwan and launched multiple B2B and B2C research platforms including Young.AI. Prior to founding Insilico Medicine, he worked in senior roles at ATI Technologies (acquired by AMD in 2006), NeuroG Neuroinformatics, the Biogerontology Research Foundation and YLabs.AI and established AgeNet.net competitions and Diversity.AI initiative. Since 2012 he published over 120 peer-reviewed research papers and books including "The Ageless Generation: How Biomedical Advances Will Transform the Global Economy" (Palgrave Macmillan, 2013).

      He is the co-organizer of the Annual Aging Research for Drug Discovery Forum and the Artificial Intelligence and Blockchain for Healthcare Forum at the Basel Life/EMBO, one of Europe's largest industry events in drug discovery. Dr. Zhavoronkov holds two bachelor degrees from Queen's University, a master's in Biotechnology from Johns Hopkins University, and a PhD in Physics and Mathematics from Moscow State University. He is the adjunct professor of artificial intelligence at the Buck Institute for Research on Aging.

    Abstract

    Ageing is a complex process that has been observed in all biological systems at every level of organisation. Some anti-ageing interventions have demonstrated life-extending effects in model organisms. However, the translation of these interventions in human clinical practice remains limited due to the absence of comprehensive ageing biomarkers. Recent studies suggest that a set of biomarkers, rather than any individual biomarker, constitute the most effective means of assessing the health status. The presentation will cover the development of comprehensive and robust biomarkers of ageing using deep learning and blood biochemistry, transcriptomics, and even imaging data, to be able to track the effectiveness of the various interventions we are developing.

    Learning Objectives: 

    1. Different AI Approaches to aging biomarker development
    2. How AI can revolutionise aging research and how aging research can accelerate AI research


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