Insilico, Juvenescence joint venture to develop AI-discovered molecules into new drugs
Baltimore-based Insilico Medicine develops artificial intelligence (AI) for drug discovery and biomarker development. The company’s “deep-learned drug discovery engines” train over structural, functional, and phenotypic data in order to predict a compound’s biological activity.
Juvenescence Limited is an investment company, and following a multi-million dollar investment into Insilico, has formed Juvenescence AI, a joint venture through which the companies aim develop a pipeline of new compounds and build AI-driven tools for clinical development.
Alexander Pickett, Juvenescence Ltd Chief Operating Officer, told us the company has three goals for the collaboration, including validating Insilico’s drug discovery engine by licensing compounds and advancing them into the clinic for indications with established clinical and regulatory pathways.
The company will also work with Insilico to develop AI support systems for clinical development “that can reduce the cost and time required to recruit and run clinical trials,” Pickett explained.
Additionally, the collaboration will identify and develop compounds that target the diseases of aging – “and perhaps eventually aging itself,” he added.
Resurrecting clinical trials with AI, and more
In the near-term, Pickett expects to see AI techniques being used in pharma for two major purposes, the first of which would be as a means of identifying molecular targets and drug candidates of interest, as is being done with Juvenescence AI.
“[Insilico] has pioneered the use of generative adversarial networks to this end, and there are many players in the space with their own methods,” he said. “AI’s other major impact on drug development will be giving clinicians and researchers better insight into disease heterogeneity and patient response to drugs.”
Specifically, Pickett explained that greater AI adoption will allow clinicians to identify disease subtypes and “explore how patients of each subtype respond to particular drugs.”
In five years, Pickett said he wouldn’t be surprised if a program that previously failed in clinical trials is resurrected due to an AI-driven subgroup analysis and successfully brought to market.
Additionally, he expects the use of AI in drug development to benefit incumbent pharmaceutical players.
“New communications technologies, the expansion of both number and sophistication of CROs, and a more mature biotech investment community have made it possible for biotech companies to compete with big pharma companies toe-to-toe,” explained Pickett.
“The efficacy of any AI technique is dependent on the quality and quantity of data that it can be trained on, and the penetration of AI techniques into the drug development world will make the vast amounts of data that Big Pharma already has an incredibly valuable asset and improve the competitiveness of these companies.”
