Script which generate 4860 Klekota-Roth fingerprints based on structures [1]. In addition, it allows you to find a fingerprint using SMILES or highlight the fingerprint in molecules. List of fingerprints was based on PaDEL-Descriptor software [2].

Requirements:

• python3
• numpy
• pandas
• rdkit

References:

[1] Klekota J, Roth F: Chemical substructures that enrich for biological activity, Bioinformatics, 2008, 24(21):2518-2525

[2] Yap CW: PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints, J Comput Chem, 2011, 32(7):1466-1474

KRFPDictSmarts(dictionary)

Dictionary of 4860 Klekota-Roth fingerprints' structures in SMART format

KRFPDictMol(dictionary)

Dictionary of 4860 Klekota-Roth fingerprints' structures in Mol format

KRFPKeys(list)

Names of 4860 Klekota-Roth fingerprints

GenerateKRFingerprints(structures, count=False, output_type='list', verbose=True)

Generate list, dictionary or DataFrame of Klekota-Roth fingerprints for structures

• Parameters
  • structures(list[], string, rdkit.Chem.Mol) - list of structures in SMILES or Mol format
  • count(bool) - if function should count number of each fingerprint in molecule
  • output_type(string) - type of output, possible values: {'list','dictionary','dataframe'}
  • verbose=(bool) - show information about progress
• Return
  • (list, dictionary, DataFrame) - list, dictionary or DataFrame of Klekota-Roth fingerprints

ListToDictionary(krfp_list)

Transform list of 4860 Klekota-Roth fingerprints to dictionary

• Parameters
  • structures(list[]) - list of 4860 Klekota-Roth fingerprints
• Return
  • (dictionary) - dictionary of 4860 Klekota-Roth fingerprints

GenerateKRFingerprintsToDataFrame(data, structures_column, count=False, verbose=True)

Update DataFrame by generating Klekota-Roth fingerprints for structures

• Parameters
  • data(DataFrame) - DataFrame which includes structures in SMILES or Mol format
  • structures_column(string) - name of column which includes structures in SMILES or Mol format (eg. structures_column="KRFP543")
  • count(bool) - if function should count number of each fingerprint in molecule
  • output_type(string) - type of output, possible values: {'list','dictionary','dataframe'}
  • verbose=(bool) - show information about progress
• Return
  • (DataFrame) - updated DataFrame with Klekota-Roth fingerprints

DrawFingerprint(krfp)

Draw a selected Klekota-Roth fingerprints

• Parameters
  • krfp(string) - Klekota-Roth fingerprint (eg. "KRFP543")
• Return
  • (Image) - fingerprints

DrawFingerprints(krfps, molsPerRow=3)

Draw selected Klekota-Roth fingerprints

• Parameters
  • krfps(list[string]) - Klekota-Roth fingerprints (eg. ["KRFP543","KRFP674","KRFP3374"])
  • molsPerRow=3(int) - number of structures in a row
• Return
  • (Image) - fingerprints

FindKRFP(find)

Find a similar Klekota-Roth fingerprint based on SMILES

• Parameters
  • find(string) - SMILES of fragment
• Return
  • (list[tuple]) - list of tuples (krfp, tanimoto_value)
    • krfp(string) - Klekota-Roth fingerprint
    • tanimoto_value(float) - similarity calculated using TanimotoSimilarity

HighlightKRFP(mols, krfp, names=None)

Highlight a Klekota-Roth fingerprint in structures

• Parameters
  • mols(list[rdkit.Chem.Mol]) - list of structures in Mol format
  • krfp(string) - Klekota-Roth fingerprint (eg. "KRFP543")
  • names(list) - list of molecules' names
• Return
  • (Image) - molecules with highlighted fragment