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Protein Name
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Accepts any of these — the format is auto-detected: a protein name (ChEMBL data), a UniProt accession like P00533 (unambiguous, one protein of one organism), a PDB code like 4WKQ (also used directly for the structure-based pharmacophore), or a ChEMBL ID like CHEMBL203.
Extra crystal structures for the structure-based pharmacophore. If you entered a PDB code above, it is used automatically — leave this empty.
More compounds = better model but longer runtime.
Immediate confirmation + results-ready email with download link. Leave empty if you don't want emails.

What you get

1. QSAR Model

RandomForest vs PLS comparison, cross-validated, with applicability domain and Y-randomization.

2. Ligand Pharmacophore

Consensus features from active compounds (3D) with labeled feature images.

3. Structure Pharmacophore

Features from crystal structure binding site (if PDB available).

4. 3D Hypothesis Ranking

Multiple hypotheses ranked by enrichment (AUC-ROC, EF, BEDROC).

5. Library Screening

All compounds screened against the best hypothesis using 3D Kabsch alignment.

6. Interactive 3D Viewer

View the pharmacophore in your browser with labeled feature spheres.

How to read the metrics

AUC-ROC

0.5 = random, >0.7 = good, >0.9 = excellent discrimination of actives from decoys.

EF(1%)

Enrichment Factor at 1% — how many more actives in the top 1% vs random (EF=5 means 5x better).

Q² (CV)

Cross-validated R² of the QSAR model (>0.5 acceptable, >0.7 good).

Pharm Score

3D alignment score (0-1) — fraction of features matched × alignment quality.