Using an artificial intelligence (AI) platform, an international research team has identified more than two dozen new targets with therapeutic potential for amyotrophic lateral sclerosis (ALS), according to a recent study.
ALS is a neurodegenerative disease. Patients progressively lose muscle strength, eventually becoming paralyzed and unable to speak, move, swallow or breathe. Despite being known to medicine for years, current ALS treatments are unable to halt or reverse this loss of function. Scientists have struggled to identify the molecular causes of ALS that can lead to therapies.
Researchers from Harvard Medical School, Johns Hopkins School of Medicine, China's Tsinghua University, alongside drug discovery company Insilico Medicine, used Insilico's AI-driven target discovery engine, called PandaOmics, to identify 17 high-confidence and 11 novel therapeutic targets for the disease.
In animal models, the team validated that 18 of the 28 identified gene targets were functionally correlated to the ALS and found that in eight of them, suppression could strongly reduce neurodegeneration.
The study findings were published in the journal Frontiers in Aging Neuroscience.
Co-author Lu Bai, professor of pharmaceutical sciences with Tsinghua, said the study demonstrates how AI speeds up the target discovery process, and opens up new opportunities for therapeutic interventions.
"The study represents a new trend, in which AI can reduce the cost and time but raise the success rate of drug development, especially for neurodegenerative diseases," Lu told Xinhua.