Encoder API¶
Learn molecular representations and return one embedding per input SMILES with
encode(X). Trainable encoders learn from your data; the Hugging Face encoder
loads an existing checkpoint with fit().
Method |
Purpose |
|---|---|
|
Train an encoder. Supervised models need labels; SAND needs pairs and similarity scores. |
|
Return molecular embeddings as a NumPy array; use |
|
Save or restore a trained model. |
Compare all 9 encoders in the model collection. See Installation for optional pretrained-model dependencies.
Model reference¶
Class |
Purpose |
|---|---|
Motif-aware attribute masking and fingerprint reconstruction. |
|
Reconstruct masked atom attributes. |
|
Graph masked autoencoding. |
|
Predict molecular subgraph contexts. |
|
Predict whether pairs of atoms are connected. |
|
Learn agreement between graph and substructure representations. |
|
Molecular property labels or computed molecular descriptors. |
|
Pairwise similarity scores; dense or compressed molecular descriptors. |
|
Load a pretrained sequence encoder, such as ChemBERTa or ChemGPT. |