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Novel Drug Candidate Designed, Synthesized and Validated in 46 Days Using AI

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The Biogerontology Research Foundation salutes its Founder and Chief Scientific Advisor Alex Zhavoronkov on leading a team of researchers who have succeeded to use Artificial Intelligence to design, synthesize and validate a novel drug candidate in just 46 days, compared to the typical 2-3 years required using the standard hit to lead (H2L) approach used by the majority of pharma corporations.

By using a combination of Generative Adversarial Networks (GANs) and Reinforcement Learning (RL), the team of Insilico Medicine researchers behind this study (documented in a paper published in Nature Biotechnology this month) have succeeded in validating the real power that AI has to expedite timelines in drug discovery and development, and to transform the entire process of bringing new drugs to market from a random process rife with dead ends and wrong turns to an intelligent, focused and directed process, that takes into account the specific molecular properties of a given disease target into account from the very first step.

The Biogerontology Research Foundation has collaborated with Insilico Medicine on a number of projects and studies, and has long advocated for the extreme potentials that AI has in terms of making the process of discovering and validating new drugs a faster and more efficient process, especially as it pertains to aging and longevity research and the development of drugs capable of extending human healthspan and compressing the incidence of age-related disease into the last few years of life.

While this is the newest in a long line of steps and accomplishments aiming to turn the theoretical potentials of AI for longevity research into practice, it is also the largest step made thus far, and goes a very long way in terms of proving that potential via hard science.

“This newest achievement made by Insilico Medicine, a leading AI for drug discovery and longevity company and an official partner of Ageing Research at King’s, demonstrates the truly disruptive potential that AI holds in terms of accelerating the pace of progress in drug discovery. Furthermore, this is just the latest step in a much grander agenda of applying AI for ageing and longevity R&D, and to the accelerated translation of that research into real-world therapies for human patients. It is also quite notable that the team released the code behind their algorithm in an open-source format, allowing other researchers to apply their techniques and build upon their achievements for the advancement of the entire field of AI for drug design, ageing research and longevity said Richard Siow, Ph.D., Director of Ageing Research at King’s College London and former Vice-Dean (International), Faculty of Life Sciences & Medicine, King's College London.

It is the hope of the Biogerontology Research Foundation that this study motivates additional researchers to harness the potential for AI in longevity research, and provides incentives for larger drug developers to begin on-boarding AI into their drug discovery and development programs, in order to expedite the time it takes to bring life-saving drugs into the hands of real patients.

The Biogerontology Research Foundation also salutes the team’s decision to release the code behind the GAN-RL method to the public in a freely-available open-source format, so that other researchers have the power to take this approach and apply it to their own work.

Reference: Zhavoronkov, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology. DOI: https://doi.org/10.1038/s41587-019-0224-x

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