Machine learning powers biobank-driven drug discovery

EN BREF ...

"Drug hunters are moving into the clinic with human-first ‘no-hypothesis’ target discovery, applying the full force of machine learning to massive collections of human omics data. Agrowing cadre of companies are betting that artificial intelligence (AI)-based algorithmic strategies can complement hypothesis-driven drug target discovery. In April BioAge Labs announced that they had dosed their first trial participant with a drug designed to treat muscle atrophy, identified through AI analysis of clinical and omics data collected from a cohort of patients in a human aging study. In traditional drug development, companies typically start out with a target and a mechanism identified and validated in preclinical studies.This forces them to make a heavy bet on whether these same genes or proteins are actually implicated in patients’ pathologies. But a rising generation of startups is applying machine learning (ML) to rich collections of clinical and molecular data without following a preconceived hypotheses. “The vast majority of what we’re doing is hypothesis-generating and hypothesis- free,” says Jeanne Magram, CSO of Celsius Therapeutics, an ML-driven drug discovery company." En bref issu de l'étude.