SMILES Feature Extraction

This Python script processes SMILES (Simplified Molecular Input Line Entry System) strings to extract molecular features using the Morgan descriptor. It is designed for cheminformatics applications, particularly in preparing molecular datasets for machine learning and data analysis tasks.

Module properties

• Molecular Feature Extraction: Extracts features from SMILES strings using the Morgan descriptor method.
• Normalization Options: Supports various normalization techniques, including cumulative distribution function, min-max scaling, and standard scaling.
• Missing Value Management: Offers options to handle missing values by either filling them with the column mean or leaving them as is.
• Output Flexibility: Allows saving the processed data in either CSV or Parquet formats.

Requirements

The script requires the following environment and libraries:

• Python 3.6 or higher
• Pandas
• NumPy
• scikit-learn
• descriptastorus

Installation

To get started with this script, clone the repository and install the required dependencies:

git clone https://github.com/HamidHadipour/RDkit_features.git
cd RDkit_features
pip install -r requirements.txt

Usage

Command-Line Arguments

The script can be configured via the following command-line arguments:

• --input (required): Path to the CSV input file containing the SMILES column.
• --output_type (required): Type of the output file (csv or parquet).
• --output_file (required): Name of the output file to save the processed data.
• --normalization: Normalization method for the data (False, CDF, minmax, standardscaler).
• --fill_nan: Option to fill missing values with the column's mean (True or False).

Example Commands

Processing a dataset with Min-Max normalization and not filling NaN values:

python extract_rdkit_features.py --input test_full.csv --output_type csv --output_file smiles_features --normalization minmax --fill_nan True

The test_full.csv file is an example you can try to run the code that includes 2954 SMILES that are labelled active/inactive.

Output

The script will output a file containing the processed features derived from the SMILES strings, in either CSV or Parquet format, as specified.

Acknowledgements

This implementation is inspired and partially based on earlier work https://github.com/bp-kelley/descriptastorus/blob/master/descriptastorus/descriptors/DescriptorGenerator.py

Contributing

Contributions are welcome. Please fork the repository, make your changes, and submit a pull request.