A 256-dimensional float vector per reaction, matches ranked by cosine similarity, and the score bands from very similar to no meaningful similarity.

Fingerprinting returns one vector per reaction, and its shape follows the fingerprint_type you asked for: 256 floats for rxnfp, 2048 binary 0/1 values for drfp. Similarity search returns your query's fingerprint plus a ranked list of matching reactions. Use fingerprints as ML features or for your own downstream comparison; use similarity search to find known reactions like a novel one.

⛔ Record which fingerprint_type produced a stored vector or score. The two are not interchangeable and their similarity scores are not comparable, so a vector of unknown type is not reusable.

Fingerprinting output

Per reaction:

Field	Type	Description
`reaction_smiles`	string	The input reaction SMILES.
`fingerprint`	number[]	The vector: 256 floats under `rxnfp`, or 2048 binary values under `drfp`.
`is_valid`	boolean	Whether the fingerprint was computed.
`error`	string \| null	Error message if `is_valid` is `false`.

The job as a whole reports total_reactions, successful, failed, and processing_time_seconds alongside the per-reaction results array.

{
  "reaction_smiles": "CC(=O)O.OCC>>CC(=O)OCC",
  "fingerprint": [0.0234, -0.1567, 0.0891, "...(256 values)"],
  "is_valid": true,
  "error": null
}

Similarity search output

Field	Type	Description
`query_smiles`	string	The query reaction SMILES.
`query_fingerprint`	number[]	The query's 256-D fingerprint.
`similar_reactions`	array	Ranked matches from the reference index.
`similar_reactions[].index`	integer	Position in the reference index.
`similar_reactions[].similarity`	number	Cosine similarity, 0–1.
`similar_reactions[].reaction_smiles`	string \| null	The matched reaction's SMILES, if available.
`similar_reactions[].reaction_name`	string \| null	Named reaction type, if available.
`similar_reactions[].classification`	string \| null	Reaction classification, if available.

Results are capped by top_k (default 10) and floored by min_similarity (default 0.7) — see inputs & parameters.

Interpreting similarity scores

Cosine similarity between two 256-D fingerprints:

Score range	Interpretation
0.90 – 1.00	Very similar transformations (same reaction type, similar substrates)
0.70 – 0.90	Related transformations (same reaction class, different substrates)
0.40 – 0.70	Moderate similarity (related functional-group chemistry)
< 0.40	Weak or no meaningful similarity

Using fingerprints downstream

Fingerprints are a plain float vector, so anything you do with them happens outside the platform:

Clustering: apply DBSCAN, k-means, or hierarchical clustering on a set of fingerprint vectors you've collected.
Visualization: project 256-D vectors to 2D with UMAP or t-SNE for a reaction-space map.
ML features: use fingerprints as input features for your own reaction-property models.

Interpreting RXNFP outputs

Fingerprinting output

Similarity search output

Interpreting similarity scores

Using fingerprints downstream

Read next

RXNFP inputs and parameters

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