Revisit known compounds.
Investigate mechanisms and cognitive hypotheses beyond a compound’s familiar use. Build a stronger starting point for repurposing research.
Backed_By
Angel_Investors_From
- University of Florida
01 / Compound intelligence
Connect Molecular Mechanisms
to Brain Systems
Neurolab assembles a structured forecast spanning mechanisms, brain systems, and cognition.
// INFERENCE_PAYLOAD
{"compound_query": "ketamine","canonical_smiles":"CNC1(CCCCC1=O)C2=CC=CC=C2Cl","inference_route":"multi_head_graph_neural_network","kg_entity_id": 1227,"kg_support": {"nodes": 98, "edges": 109},"kg_version": "v43.262","status": "resolved"}COMPOUND → INFERENCE → REPORTKetamine / application capture
Multiscale Inference
from Single Genes to Cortical Regions
Find the signals that matter to your research questions.
- 01
Molecular mechanisms
Investigate receptor relationships and candidate mechanisms of action.
- 02
Brain systems
Explore outputs across circuits, regions and networks.
- 03
Cognition
Examine predicted cognitive effects and the context behind them.
02 / An explorable research workspace
The forecast is
just the beginning.
Read it. Question it. Visualize it. Take it into your next analysis. Neurolab puts the results within reach of both researchers and machines.
01 / Structured outputs
Get inside the forecast.
Open individual sections. Inspect nested outputs. Attach a finding to a question, or export the data and keep investigating.
Human-readable. Machine-readable. Exportable.03 / A new dimension for discovery
Your pipeline has a target.
Your molecules have more to tell you.
Brain effects can matter even when the brain wasn’t the original target. Bring another line of investigation into the compounds you already study—and the ones you haven’t explored yet.
Explore new chemistry.
Start with a molecular structure. Examine its forecasted profile and develop questions for your next experiment.
Add another perspective.
Bring potential brain effects into candidate discussions alongside on-target activity and the data your team already uses.
More questions worth asking.
More directions worth exploring.
04 / The science underneath
Biology is connected.
So is Neurolab.
Graph neural networks and deep learning work alongside chemical-structure retrieval and mechanism reasoning. A versioned knowledge graph connects the biological context.
8M+Directed relationships
158K+Connected entities
41Categories of biological and chemical entities.
Neurolab knowledge graph · v43.262
Start with your compound.
Enter a known compound name or provide a molecular structure as SMILES.
Run the appropriate models.
Known compounds follow the graph-model route. New structures follow a dedicated chemistry and mechanism workflow.
Put the result to work.
Explore a structured report with predictions, available evidence, source context and model metadata. Export what you need.