Traditional approaches to drug development for diseases like Alzheimer’s, Parkinson’s, and amyotrophic lateral sclerosis (ALS) haven’t offered patients much. Alice Zhang is trying something new. Her company, Verge Genomics, uses artificial intelligence to identify promising compounds, refining the algorithms with high-quality data from patients and lab tests. She hopes this will be a more effective way to find treatments for intractable neurodegenerative diseases.
Zhang’s unorthodox method was inspired when she heard a researcher give a talk detailing how hundreds of genes interact in cancer and wondered whether this “network” approach could apply to neurodegenerative diseases. “Computational biology has provided so much insight about cancer,” she says. “The brain is about 10 years behind.”
Verge is developing machine--learning models that identify key genes within a disease network and predict which compounds might interfere with their activity. It tests these compounds in animal models and nerves grown from patient-derived stem cells. The company then feeds the results back into the machine-learning model to refine it further. Zhang says seven of Verge’s candidate compounds for ALS have slowed cell death in patient neurons in vitro.