Fingerprint

To calculate the learned representations (encodings) of model inputs from a pretrained model, run

chemprop fingerprint --test-path <test_path> --model-path <model_path>

where <test_path> is the path to the CSV file containing SMILES strings, and <model_path> is the location of checkpoint(s) or model file(s) to use for prediction. It can be a path to either a single pretrained model checkpoint (.ckpt) or single pretrained model file (.pt), a directory that contains these files, or a list of path(s) and directory(s). If a directory, will recursively search and predict on all found (.pt) models. By default, predictions will be saved to the same directory as the test path. If desired, a different directory can be specified by using --output <path>. The output <path> can end with either .csv or .npz, and the output will be saved to the corresponding file type.

For example:

chemprop fingerprint --test-path tests/data/smis.csv \
    --model-path tests/data/example_model_v2_regression_mol.ckpt \
    --output fps.csv

Specifying FFN encoding layer

By default, the encodings are returned from the penultimate linear layer of the model’s FFN. However, the exact layer to draw encodings from can be specified using --ffn-block-index <index>.

An index of 0 will simply return the post-aggregation representation without passing through the FFN. Here, an index of 1 will return the output of the first linear layer of the FFN, an index of 2 the second layer, and so on.

Specifying Data to Parse

fingerprint shares the same arguments for specifying SMILES columns and reaction types as predict. For more detail, see Prediction.