ChemDescriptors 0.0.7

Chemical descriptors is a powerful Python package facilitating calculation of fingerprints for CSV files

Python Library: ChemDescriptors

This library provides various functions for calculating molecular descriptors and fingerprints in cheminformatics. It supports the calculation of a wide range of molecular descriptors and fingerprints, such as RDKit, Lipinski, Morgan, Mordred, and more.

Importance of Fingerprint Types:

• Distinct Representation: Different fingerprint types capture various aspects of a molecule’s structure, allowing for versatile molecular comparisons.
• Diverse Applications: Depending on the task (such as similarity searching, classification, or clustering), choosing the right fingerprint type ensures better performance in chemical analysis and predictive modeling.
• Accuracy in Modeling: The right fingerprint type can significantly improve the accuracy of machine learning models and predictions based on molecular features.

Number of Fingerprints:

The library supports a wide variety of fingerprint types, enabling a range of analyses for molecular datasets.

Tutorial & Example visit the Chemical Descriptors Repository. GitHub Repository & GitHub Repository

Functions

add_rdkit_descriptor(input_file,smiles_column)

Description:
This function calculates RDKit molecular descriptors for molecules specified in a CSV file (input_file) using SMILES strings from a specified column (smiles_column). The calculated descriptors are appended as additional columns to the original data and saved in a new CSV file named <input_file_name>_rdkit_descriptor.csv.

Parameters:

• input_file (str): Path to the input CSV file containing molecular data in SMILES format.
• smiles_column (str): The name of the column in the input CSV file that contains the SMILES strings.

Output:
The output will be saved as a CSV file named <input_file_name>_rdkit_descriptor.csv.

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add_lipinski_descriptors(file_path, smiles_column, verbose=False)

Description:
This function calculates Lipinski descriptors for molecules specified in a CSV file (file_path) using SMILES strings from a specified column (smiles_column). It automatically saves the calculated descriptors to an output file named <input_file_name>_lipinski_descriptors.csv.

Parameters:

• file_path (str): Path to the input CSV file containing molecular data in SMILES format.
• smiles_column (str): The name of the column in the input CSV file that contains the SMILES strings.
• verbose (bool, optional): If True, the function will print additional processing details. Default is False.

Output:
The output will be saved as a CSV file named <input_file_name>_lipinski_descriptors.csv.

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add_morgan_fp(input_file, smiles_column)

Description:
Calculates Morgan fingerprints for molecules specified in a CSV file (input_file) using SMILES strings from a specified column (smiles_column). The function saves the calculated fingerprints to an output file named <input_file_name>_calculate_morgan_fpts.csv.

Parameters:

• input_file (str): Path to the input CSV file containing molecular data in SMILES format.
• smiles_column (str): The name of the column in the input CSV file that contains the SMILES strings.

Output:
The output will be saved as a CSV file named <input_file_name>_calculate_morgan_fpts.csv.

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add_mordred_descriptors(input_file, smiles_column)

Description:
Computes Mordred descriptors for molecules specified in a CSV file (input_file) using SMILES strings from a specified column (smiles_column). The function saves the computed descriptors to an output file named <input_file_name>_mordred_descriptors.csv.

Parameters:

• input_file (str): Path to the input CSV file containing molecular data in SMILES format.
• smiles_column (str): The name of the column in the input CSV file that contains the SMILES strings.

Output:
The output will be saved as a CSV file named <input_file_name>_mordred_descriptors.csv.

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**add_WienerIndex_ZagrebIndex(filename, smiles_column):

add_WienerIndex_ZagrebIndex(input_file, smiles_column)

Description:
Computes WienerIndex and ZagrebIndex descriptors for molecules specified in a CSV file (input_file) using SMILES strings from a specified column (smiles_column). The function saves the computed descriptors to an output file named add_WienerIndex_ZagrebIndex_<input_file_name>_.csv.

Parameters:

• input_file (str): Path to the input CSV file containing molecular data in SMILES format.
• smiles_column (str): The name of the column in the input CSV file that contains the SMILES strings.

Output:
The output will be saved as a CSV file named <input_file_name>_mordred_descriptors.csv.

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add_padelpy_fps(input_file, smiles_column)

Description:
This function allow to user to add 12 different types of molecular fingerprints by using padelpy Library.. The supported fingerprints include:

• AtomPairs2DCount
• AtomPairs2D
• EState
• CDKextended
• CDK
• CDKgraphonly
• KlekotaRothCount
• KlekotaRoth
• MACCS
• PubChem
• SubstructureCount
• Substructure

Each enhanced dataset with fingerprints is saved as separate CSV files, appended with the respective fingerprint type name.

Parameters:

• input_file (str): Path to the input CSV file containing molecular data in SMILES format.
• smiles_column (str): The name of the column in the input CSV file that contains the SMILES strings.
• Then run the code: You find list of fingerpints you can select one or more to add them in your file

Output:
Each fingerprint type will be saved as a separate CSV file with the respective fingerprint type name appended.
For example: <input_file_name>_AtomPairs2DCount.csv.

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add_molfeat_fps(filename, smiles_column)

Description:
This function allow to user to add 19 different types of molecular fingerprints by using molfeat Library. The supported fingerprints include:

• maccs
• avalon
• pattern
• layered
• map4
• secfp
• erg
• estate
• avalon-count
• ecfp
• fcfp
• topological
• atompair
• rdkit
• ecfp-count
• fcfp-count
• topological-count
• atompair-count
• rdkit-count

Parameters:

• filename (str): Path to the input CSV file containing molecular data in SMILES format.
• smiles_column (str): The name of the column in the input CSV file that contains the SMILES strings.
• Then run the code: You find list of fingerpints you can select one or more to add them in your file

Output:

The output will be saved as a CSV file named <input_file_name>_<fp_type>.csv depending on the fingerprint type chosen.

Refernce:

1. Emmanuel Noutahi, Cas Wognum, Hadrien Mary, Honoré Hounwanou, Kyle M. Kovary, Desmond Gilmour, thibaultvarin-r, Jackson Burns, Julien St-Laurent, t, DomInvivo, Saurav Maheshkar, & rbyrne-momatx. (2023). datamol-io/molfeat: 0.9.4 (0.9.4). Zenodo. https://doi.org/10.5281/zenodo.8373019
   GitHub Repository

2. RDKit: Open-source cheminformatics software. https://rdkit.org

3. Moriwaki, H., Tian, YS., Kawashita, N. et al. (2018). Mordred: a molecular descriptor calculator. Journal of Cheminformatics, 10, 4. https://doi.org/10.1186/s13321-018-0258-y

4. PaDELPy: A Python wrapper for PaDEL-Descriptor software. GitHub Repository

5. Ahmed Alhilal. Chemical Descriptors Repository. GitHub Repository

Ahmed Alhilal

0.0.7 (05/01/2025)

• First Release