Insilico 'pulls back the curtain' on AI drug discovery process in hope of convincing skeptics

A new paper in Nature Biotechnology published March 8 offers the first in-depth look at how AI-driven biotech Insilico Medicine used artificial intelligence to discover, develop and test its lead drug candidate. 

Insilico founder Alex Zhavoronkov, Ph.D., called the paper an “essential blueprint for the pharma and biotech industry” on how to use AI in drug development. 

“We hope this will serve as a tipping point and motivation to accelerate the pace of AI drug discovery and increase pharma partnerships on our AI-designed therapeutics, particularly in the preclinical stages,” Zhavoronkov said in an email to Fierce Biotech Research.

In the paper, Insilico describes how AI was used to find a potent target for fibrosis, then a therapeutic and then a drug to match, using 13 different experiments and three clinical trials.

“In order to advance a new era of generative AI-driven drug discovery, it’s important to set the benchmark for what that means and to prove those results in a peer-reviewed scientific journal,” Zhavoronkov said.

The article goes step-by-step through the process that Insilico undertook to develop INS018_055, a small-molecule TNIK inhibitor that is currently in phase 2 trials for the treatment of the lung disease idiopathic pulmonary fibrosis (IPF). That includes how Insilico used its proprietary AI platform, PandaOmics, to both identify a target and come up with a drug candidate to treat the disease. The paper also discusses additional potential indications for INS018_055 beyond IPF, including skin and kidney fibrosis as well as diseases of aging. 

The team discovered that INS018_055 might have potential as a treatment for age-related disease by combining disease analysis with the company’s “Hallmarks of Aging Assessment,” a complex algorithm that takes into account the various biological mechanisms involved in aging to identify drug targets. 

“TNIK emerged as one of the most promising dual-purpose targets related to anti-fibrosis and aging in this assessment, and we are very interested in further research in this direction,” Zhavoronkov said. He added that metabolic regulation may be another direction to explore, as a recent study published in Science Advances shows that the enzyme regulates lipid metabolism in obesity. 

At the same time the paper was published, Insilico also released an interactive GPT that answers questions about TNIK and the company’s AI process. 

By pulling back the curtain on AI-driven drug development at Insilico, Zhavoronkov hopes the paper will give any remaining skeptics of the company’s technology and process “a clear understanding of Insilico’s unique capabilities, and the quality of our AI-discovered and designed drugs, backed by scientific data,” he said.