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Dissimilarity Selection (from Matrix)

Schrödinger extension for KNIME Workbench version by Schrödinger

Dissimilarity Based Compound Selection

Backend implementation

canvasDBCS is used to implement this node.


Number of Subsets
Number of subsets requested. Required for selection methods maxsum and maxmin. Allowed for methods sphere and dise where it is applied as a secondary filtering step.
Distance Threshold
Threshold for selection methods "sphere" and "dise".
Selection Method Type
Choice of selection method:

  • sphere (default)
  • dise
  • maxsum
  • maxmin

Metric Type
Valid types:
  • buser
  • cosine
  • dice
  • dixon
  • euclidean
  • hamann
  • hamming
  • kulczynski
  • matching
  • mcConnaughey
  • minmax
  • modifiedTanimoto
  • patternDifference
  • pearson
  • petke
  • rogersTanimoto
  • shape
  • simpson
  • size
  • soergel(default)
  • tanimoto
  • variance
  • yule
Ignore any scaled Finger Print values
Ignore any scaled fp values in input
Initialization Method
Valid methods:
  • random (default)
  • first
  • representative
  • dissimilar
Seed for random Number generation
Seed for random number generation (defaults to clock)

Input Ports

Pairwise matrix or Molecular fingerprints (both in binary format). The matrix contains only intra-distances between one set of compounds (i.e. symmetric matrix)

Output Ports

Dissimilarity Selection as a molecule name list (1 String column)


Std output/error of Dissimilarity Selection
Std output/error of Dissimilarity Selection

Best Friends (Incoming)

Best Friends (Outgoing)



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