Version 1.1.0 | Updated April 2026
The platform runs as a web application accessible at:
- Local: http://localhost:5051
- Remote (via Tailscale Funnel): The URL provided by your administrator
Open any modern web browser (Chrome, Firefox, Edge) and navigate to the URL.
The top navigation bar provides access to all major features: - Dashboard - Overview of all projects - BBB Predictor - Single/batch BBB prediction - BBB Optimizer - AI-powered molecule optimization - Chemical Space - UMAP visualization - Benchmarks - Model performance comparisons - New Project - Create a new drug discovery project
The dashboard shows all active drug discovery projects with key metrics: - Project cards display target name, indication, number of compounds, and pipeline progress - Aggregate statistics show total compounds, leads identified, and active campaigns - Click any project card to view detailed project information
The BBB (Blood-Brain Barrier) Predictor is the platform's primary tool. It predicts whether a molecule can cross the blood-brain barrier to reach the central nervous system.
O=C1CN=C(c2ccccc2)c2cc(Cl)ccc2N1C for Diazepam)Four independent ML models vote on the prediction: - D-MPNN: ChemProp directed message-passing neural network - RF (Morgan): Random Forest on 2048-bit Morgan fingerprints - XGBoost: Gradient boosting on 15 molecular descriptors - Attentive GNN: Graph neural network with stereochemistry awareness (CANDID-CNS architecture)
The ensemble probability is the average of all available models. High confidence means all models agree and the probability spread is small.
A radar chart and table showing molecular properties relevant to CNS penetration:
| Property | Ideal Range | Significance |
|---|---|---|
| MW | 350-450 Da | Smaller molecules cross more easily |
| TPSA (2D) | < 90 A^2 | Lower polar surface area = better permeation |
| 3D PSA | < 80 A^2 | Conformer-based PSA (more accurate than 2D) |
| HBD | <= 2 | Fewer H-bond donors reduce P-gp efflux |
| HBA | <= 8 | Fewer acceptors improve passive diffusion |
| SlogP | 1.0-3.0 | Moderate lipophilicity |
| Fsp3 | >= 0.2 | Some sp3 character improves solubility |
| RotBonds | <= 8 | Less flexibility = more rigid, better permeation |
| QED | >= 0.5 | Drug-likeness score |
When available, two explainability visualizations show WHY the model made its prediction: - XGBoost Descriptors: Bar chart showing which molecular properties most influenced the prediction. Green bars push toward BBB+, red bars push toward BBB-. - Atom BBB Attribution: Molecule image with atoms colored by their contribution. Green atoms help BBB+, red atoms hurt it.
Red/yellow flags for properties that strongly predict BBB impermeability: - TPSA > 120 (critical) - MW > 500 (critical) - HBD > 3 (critical) - Charged groups (critical) - SlogP < 0 or > 5 (warning)
Shows the most structurally similar approved CNS drug from a curated library of 20 reference compounds.
Shows how similar the query molecule is to the training data. "In domain" (similarity >= 0.3) means the prediction is more reliable.
SMILES column (one molecule per row)Click any of the example buttons (Diazepam, Acetaminophen, Ibuprofen, Testosterone) to quickly test the predictor.
The optimizer uses REINVENT 4 (deep reinforcement learning) to generate novel molecules optimized for BBB penetration starting from a seed compound.
/optimize/bbbNavigate to /chemspace/bbb to view the chemical space visualization.
/new-projectFor each project, the compound browser at /project/<name>/compounds provides:
Select 2-4 compounds and click "Compare" to see a radar chart overlay of their properties.
The scaffolds view groups compounds by Murcko scaffold for SAR analysis.
The platform provides a REST API for programmatic access.
GET /api/v1/health
Returns model versions and status.
POST /api/v1/predict
Content-Type: application/json
{
"smiles": ["CCO", "c1ccccc1"]
}
Returns full BBB prediction results for up to 100 SMILES.
POST /api/v1/optimize
Content-Type: application/json
{
"smiles": "c1ccccc1",
"n_iterations": 100
}
Launches an optimization job. Returns a job ID.
GET /api/v1/status/<job_id>
Returns optimization progress and results.
Visit /api/docs in your browser for interactive API documentation.
Navigate to /benchmarks to see how the platform's models compare against published methods:
| Method | AUC-ROC | Source |
|---|---|---|
| Arion 4-Model Ensemble | 0.934 | Validated on Spielvogel 2025 dataset |
| Arion Attentive GNN | 0.876 | Scaffold-balanced split on B3DB |
| CANDID-CNS (Collins 2025) | 0.95 | Published (unfiltered B3DB) |
| Spielvogel RF (2025) | 0.88 | Published (154 radiotracers) |
| BBBscore (Gupta 2019) | 0.83 | Validated on same data |
| CNS MPO (published) | 0.53 | Spielvogel evaluation |
The platform's ensemble consistently outperforms individual published methods by combining multiple complementary model architectures.
conda activate reinvent_envnetstat -aon | findstr 5051cd D:\Arion\Platform && python run_web.pyThe query molecule is structurally dissimilar to the training data (Tanimoto < 0.3). The prediction may be less reliable. Consider: - Checking if the molecule is valid/reasonable - Using additional scoring methods (docking, experimental)
tailscale funnel --bg 5051tailscale funnel status