Paper
Graph AI generates neurological hypotheses validated in molecular, organoid, and clinical systems
Authors
Ayush Noori, Joaquín Polonuer, Katharina Meyer, Bogdan Budnik, Shad Morton, Xinyuan Wang, Sumaiya Nazeen, Yingnan He, Iñaki Arango, Lucas Vittor, Matthew Woodworth, Richard C. Krolewski, Michelle M. Li, Ninning Liu, Tushar Kamath, Evan Macosko, Dylan Ritter, Jalwa Afroz, Alexander B. H. Henderson, Lorenz Studer, Samuel G. Rodriques, Andrew White, Noa Dagan, David A. Clifton, George M. Church, Sudeshna Das, Jenny M. Tam, Vikram Khurana, Marinka Zitnik
Abstract
Neurological diseases are the leading global cause of disability, yet most lack disease-modifying treatments. We present PROTON, a heterogeneous graph transformer that generates testable hypotheses across molecular, organoid, and clinical systems. To evaluate PROTON, we apply it to Parkinson's disease (PD), bipolar disorder (BD), and Alzheimer's disease (AD). In PD, PROTON linked genetic risk loci to genes essential for dopaminergic neuron survival and predicted pesticides toxic to patient-derived neurons, including the insecticide endosulfan, which ranked within the top 1.29% of predictions. In silico screens performed by PROTON reproduced six genome-wide -synuclein experiments, including a split-ubiquitin yeast two-hybrid system (normalized enrichment score [NES] = 2.30, FDR-adjusted ), an ascorbate peroxidase proximity labeling assay (NES = 2.16, FDR ), and a high-depth targeted exome sequencing study in 496 synucleinopathy patients (NES = 2.13, FDR ). In BD, PROTON predicted calcitriol as a candidate drug that reversed proteomic alterations observed in cortical organoids derived from BD patients. In AD, we evaluated PROTON predictions in health records from patients at Mass General Brigham, confirming that five PROTON-predicted drugs were associated with reduced seven-year dementia risk (minimum hazard ratio = 0.63, 95% CI: 0.53-0.75, ). PROTON generated neurological hypotheses that were evaluated across molecular, organoid, and clinical systems, defining a path for AI-driven discovery in neurological disease.