smiles2odor — POM GNN baseline (Release 1)

Model details

• Architecture: 4× GINEConv layers (256 hidden) → sum pool → 2-layer MLP head
• Inputs: SMILES string → RDKit molecular graph
• Outputs: Multi-label probabilities over the Leffingwell descriptor vocabulary
• Training data: Leffingwell PMP via Pyrfume
• Loss: Binary cross-entropy per descriptor
• Splits: Bemis–Murcko scaffold split (80 / 10 / 10)

Intended uses

• Researchers studying structure–odor relationships
• Cheminformatics pipelines that need a quick olfactory prior over a candidate set
• Educational material illustrating graph neural networks on real-world chemistry

Limitations

• Coverage is limited to the descriptor vocabulary present in Leffingwell PMP.
• Trained on roughly 3.5k molecules; novel chemistry may lie far out-of-distribution.
• Multi-label probabilities are not calibrated — use them as relative rankings rather than absolute likelihoods.
• Descriptors reflect English perfumery vocabulary and inherit the cultural assumptions baked into the source data.

Evaluation

How to use

Training reproduction

Citation

License