A new Harvard AI tool helps identify existing drugs that could treat rare diseases

Advances in biological AI models have shown promise when it comes formulating new drugs. But can the technology also find ways to treat rare diseases with existing medications? That’s what researchers at Harvard Medical School set out to learn with a new foundation model designed to do just that. The TxGNN model, described in a recent paper in Nature, is purportedly the first AI system that seeks to pinpoint already approved drugs that might specifically treat rare diseases for which there are currently no treatments on the market. These diseases might be rare on their own, but taken together, the more than 7,000 conditions classified as such affect around 300 million people worldwide, according to The Lancet. And only 5–7% of these ailments currently have drugs approved by the Food and Drug Administration (FDA). Meanwhile, nearly one-third of drugs approved by the FDA end up with more than one approved use later on, often as many as 10 or more, the authors write. The problem, according to the team, is that discovering these new uses tends to be “a serendipitous and opportunistic endeavor”—doctors more or less stumble upon them in the course of working with patients. “Predicting the efficacy of all drugs against all diseases would enable us to select drugs with fewer side effects, design more effective treatments targeting multiple points in a disease pathway, and systematically repurpose existing drugs for new therapeutic use,” the authors wrote. Breakthroughs like Google’s AlphaFold protein prediction model, which won the Nobel Prize for chemistry this month, have led to a boom in the application of generative AI to the drug discovery pipeline. Plenty of companies are also building models meant to tackle drug repurposing, according to GlobalData.