| Literature DB >> 31770722 |
Daniela Bakula1, Andrea Ablasser2, Adriano Aguzzi3, Adam Antebi4,5, Nir Barzilai6, Martin-Immanuel Bittner7, Martin Borch Jensen8, Cornelis F Calkhoven9, Danica Chen10, Aubrey D N J de Grey11, Jerome N Feige12,13, Anastasia Georgievskaya14, Vadim N Gladyshev15, Tyler Golato16, Andrei V Gudkov17, Thorsten Hoppe18, Matt Kaeberlein19,20, Pekka Katajisto21,22, Brian K Kennedy23,24,25, Unmesh Lal26, Ana Martin-Villalba27, Alexey A Moskalev28,29,30, Ivan Ozerov31, Michael A Petr1, David C Rubinsztein32,33, Alexander Tyshkovskiy15,34, Quentin Vanhaelen31, Alex Zhavoronkov31, Morten Scheibye-Knudsen1.
Abstract
An increasing aging population poses a significant challenge to societies worldwide. A better understanding of the molecular, cellular, organ, tissue, physiological, psychological, and even sociological changes that occur with aging is needed in order to treat age-associated diseases. The field of aging research is rapidly expanding with multiple advances transpiring in many previously disconnected areas. Several major pharmaceutical, biotechnology, and consumer companies made aging research a priority and are building internal expertise, integrating aging research into traditional business models and exploring new go-to-market strategies. Many of these efforts are spearheaded by the latest advances in artificial intelligence, namely deep learning, including generative and reinforcement learning. To facilitate these trends, the Center for Healthy Aging at the University of Copenhagen and Insilico Medicine are building a community of Key Opinion Leaders (KOLs) in these areas and launched the annual conference series titled "Aging Research and Drug Discovery (ARDD)" held in the capital of the pharmaceutical industry, Basel, Switzerland (www.agingpharma.org). This ARDD collection contains summaries from the 6th annual meeting that explored aging mechanisms and new interventions in age-associated diseases. The 7th annual ARDD exhibition will transpire 2nd-4th of September, 2020, in Basel.Entities:
Keywords: aging; artificial intelligence; drug discovery
Mesh:
Year: 2019 PMID: 31770722 PMCID: PMC6914421 DOI: 10.18632/aging.102487
Source DB: PubMed Journal: Aging (Albany NY) ISSN: 1945-4589 Impact factor: 5.682