CircuitATLAS maps brain circuits to find new neurological drug targets
CircuitATLAS: Agentic reasoning over a systems neuroscience knowledge graph for target discovery in circuitopathies
Artificial Intelligence
Summary
Finding new treatments for brain diseases is hard because the molecules changed by disease aren't always the best to target. The authors created CircuitATLAS, a huge map of brain knowledge that links brain regions, circuits, cell types, and molecules but avoids shortcuts by excluding direct disease-molecule links. They built a system that can reason from disease symptoms to molecular targets that might fix brain circuit problems. They tested this by identifying a molecule, ATP1A3, that controls brain cell activity, confirmed its effect in living brains, and started developing drugs targeting it.
What this means in practice
- •For pharmaceutical research teams: Identify molecular control points from brain circuit data to develop new drugs for neurological diseases.
- •For neurotechnology developers: Use the CircuitATLAS knowledge graph to guide design of therapeutic interventions targeting brain circuit function.
Authors
Gabriel Ocana-Santero, Marko Tvrdic
Abstract
Drug discovery for neurological disease has traditionally centered on the molecules altered by disease. But the molecules that cause pathology are not necessarily the best points from which to reverse it. Here, we ask which otherwise unaltered molecular control points can be engaged to restore pathological neural circuits toward functional states. We present CircuitATLAS, a provenance-grounded systems-neuroscience knowledge graph and agentic framework for target discovery in circuitopathies. It structures literature-derived relationships across diseases, phenotypes, electrophysiology, circuits, brain regions, cell types and molecular effectors, while deliberately excluding direct disease-gene and disease-protein edges to reduce shortcut reasoning. The graph contains 3.83 million nodes and 7.66 million edges, including 5.31 million LLM-extracted relations, and incorporates structured datasets such as the Human Cell Atlas and new multimodal in vivo measurements. We then introduce an agentic workflow that reasons from measurable disease phenotypes through their circuit and cellular substrates to molecular interventions, therapeutic feasibility and clinical constraints. Finally, we introduce a human-governed in vivo lab-in-the-loop linking hypothesis generation to experimental iteration. Within this framework an agent nominated ATP1A3, the neuronal alpha3 Na+/K+-ATPase, as a control point on cortical excitability; interneuron-restricted expression of ATP1A3 abolished the beta- and gamma-band response to a focal 4-aminopyridine challenge in vivo, and the validated target was then carried into a structure-guided small-molecule campaign terminating in a defined assay to resolve the direction of modulation. CircuitATLAS thus provides a framework for discovering therapeutics based not only on what is molecularly disrupted in disease, but on what can be controlled to restore circuit function.