A Rapid Process for Senolytic Drug Discovery

In Silico Medicine specializes in the application of computational methods to speed up the process of screening small molecule drugs, while reducing the costs, an advance in infrastructure technology that is very much in favor these days. Most of the large entities in medical development still proceed exactly has they have done for decades in the matter of developing new therapies: find a molecular target, then find a small molecule that influences that target, then iterate over variations to try to increase efficacy and reduce side-effects. Thus numerous groups work in this part of the field, trying to cut presently sizable costs and improve the presently poor odds of success. In this paper, the In Silico Medicine team demonstrates that they can very rapidly identify candidate small molecule senolytic drugs, capable of clearing the senescent cells that contribute to aging and age-related disease.

A team of researchers has succeeded in using 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 researchers behind this study (documented in a paper published 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.

Researchers have 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.

Link: http://bg-rf.org.uk/press/ageing-research-to-accelerate-with-experimental-validation-in-ai-powered-drug-discovery


I do not see any demonstrations of their claim. If they can vet the senolytic potential of a molecule in 46 days they should have multiple specifics to offer by now.

Posted by: JohnD at September 18th, 2019 2:16 PM

Very good !

Now they only have dosing formulation development, PK studies, ADME studies, Cytochrome P450 studies, CEREP, hERG / purkinje fiber studies, safety pharmacology studies, dose ranging studies, 14 day repeat dose tox studies, 3 month repeat dose tox studies, and genetic tox studies before they get anywhere near a human - in other words 99.9999999% of the rest of the process before them - called drug development

This AI stuff is such hype - just like rational drug discovery in the 1990s

But it looks like InSilico's investors are finally getting disciplined and focused

"Many of our longevity projects will need to be divested."


Posted by: David Permisov at September 18th, 2019 3:56 PM

Post a comment; thoughtful, considered opinions are valued. New comments can be edited for a few minutes following submission. Comments incorporating ad hominem attacks, advertising, and other forms of inappropriate behavior are likely to be deleted.

Note that there is a comment feed for those who like to keep up with conversations.