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Erlwood Knime Open Source Cheminformatics version 3.3.0.v201701271213 by Erlwood

Computes the bit vector similarity of one or multiple query fingerprints to a set of data fingerprints. If multiple queries should be used the respective checkbox should be ticked and an appropriate fusion method selected.


Query column
Column in the first input table containing the query fingerprints (must be of type DenseBitVector)
Data column
Column in the second input table containing the data fingerprints (must be of type DenseBitVector)
Similarity measure
The similarity method that should be used
Multi-query fusion
Tick the box if the similarities to all available queries should be combined into a single fused similarity. If the box is not checked only the first query is considered.
Fusion method
Mathematical approach to combine multiple similarities into one similarity score

Input Ports

Table containing the query fingerprints
Table containing the data fingerprints

Output Ports

Data table augmented with an additional numerical column containing the computed fingerprint similarity

Best Friends (Incoming)

Best Friends (Outgoing)

Update Site

To use this node in KNIME, install Erlwood Knime Open Source Cheminformatics from the following update site:

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