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Similarity Matrix (from Molecules)

Generates a pairwise similarity or distance matrix using binary or scaled fingerprints from two sets of molecules.

If the first set is composed of only one probe molecule the result of the calculation is a table containing the similarities of this molecule and the library in the second set.

Backend implementation

$SCHRODINGER/utilities/canvasFPGen
canvasFPGen is used to generate the fingerprints for the input molecules.
$SCHRODINGER/utilities/canvasFPMatrix
canvasFPMatrix generates the pairwise similarity or distance matrix from the fingerprints from canvasFPGen.

Options

Include Molecule

Whether the molecule should be included in the output

Include Input

Whether all columns in the input should be included in the output

Fingerprint Type

Valid methods:

linear
maccs
radial
molprint2D
torsion
pairwise
triplet
dendritic

Note: Atom type is not used to generate finger prints when the "maccs" method is selected.

Precision

Select fingerprint precision:

1024 (10-bit)
2048 (11-bit)
4096 (12-bit)
32-bit (default)
64-bit

Selecting 64-bit reduces collisions of "on" bits, but doubles the space required to store each key. Selecting 1024, 2048 and 4096 increases the chance of feature collisions.

Atom type

Atom typing scheme. Must be an integer value between 1 and 10 or C or E.

Omit bits that are set by less than percentage

Omit bits that are set by more than percentage

Omit bits only set in single molecule

Omit bits set in all molecules

Reduce precision of fingerprints by specified number of bits

Only used with 32-bit precision. Reduces precision of fingerprints by specified number of bits. It increases the chance of feature collisions. For example, a value of 22 will reduce each single precision key (32 bits) into a range of 1024 (10 bits).

Scaling

Rescale binary fingerprint data to real.

Metric types

Valid metrics:

buser
cosine
dice
dixon
euclidean
hamann
hamming
kulczynski
matching
McConnaughey
minmax
patterDifference
pearson
petke
rogersTanimoto
shape
simpson
size
soergel
tanimoto
tversky
variance
yule

Maximum number of fingerprints to load in memory at a time

Ignore any scaled fp values

Tversky alpha parameter

Tversky beta parameter

Gaussian parameter to make output matrix sparse

Parameter flow variables

Any valid option for proplister.py can be specified through flow variables. Only String variables are accepted.
Usage:
Flow variable prefix keyword: CanvasFPMatrix
Note: To specify an option as flow variable, the flow variable name should be like:
keyword-option_name for single-dash option
keyword--option_name for double-dash option

To add a new option with value, specify the option_name and the corresponding value through flow variable.

To add a new option without value, specify the option_name and the value as _on_ through flow variable.

To override an existing option's value in the command line, just specify the option_name and the new value through the flow variable.

To remove an existing option without a value, specify the option_name and the value as _off_ through the flow variable.

To remove an existing option with a value, specify the option_name and the value as _rm_ through the flow variable.

Input Ports

: First set of molecules in Maestro, SMILES or SD format
: Second set of molecules in Maestro, SMILES or SD format

Output Ports

: Similarities Matrix in Table format

Popular Predecessors

Popular Successors

Views

Std output/error of FPGen 1: Std output/error of FPGen 1
Std output/error of FPGen 2: Std output/error of FPGen 2
Std output/error of FPMatrix: Std output/error of FPMatrix

Workflows

Developers

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Installation

To use this node in KNIME, install the extension Schrödinger Extensions for KNIME from the below update site following our NodePit Product and Node Installation Guide:

v5.2

A zipped version of the software site can be downloaded here.

Plugin provider: Schrödinger

Plugin version: 24.3.138.v202407300410

On NodePit since: 2023-12-06

Last update: 2024-12-18

KNIME versions: v5.2, v5.1, v4.7, v4.6, v4.5, v4.4, v4.3, v4.2, v4.1, v4.0, v3.7, v3.6

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