Quantitative Structure Activity Relationship

1. Importing necessary libraries: The code imports several libraries such as NumPy, ChemBL API, Pandas, RDKit, scikit-learn, and matplotlib. These libraries provide functionality for data manipulation, molecular analysis, machine learning, and data visualization.

2. Defining functions for data retrieval and model building:

   • target_retrieval(search): Retrieves information about a target from ChemBL API based on a search term.
   • target_info_retrieval(search): Retrieves activity data for a specific target from ChemBL API.
   • build_qsar_model(data): Cleans the activity data, calculates pIC50 values, generates molecular fingerprints, and constructs a QSAR (Quantitative Structure-Activity Relationship) model using the Random Forest algorithm.
   • evaluate_molecule(mol_smiles, model): Predicts the pIC50 value for a given molecule using the trained QSAR model.

3. Defining additional utility functions:

   • plot_predicted_vs_real(y_test, y_pred): Generates a scatter plot comparing the predicted pIC50 values to the real pIC50 values.
   • identify_pharmacophores(feature_importance, fingerprint_columns, threshold): Identifies important pharmacophores (fingerprint features) based on their feature importance values.
   • draw_molecule(smiles): Draws a molecule using its SMILES string representation.

4. Example usage:

   • Retrieves target information and activity data for a given search term.
   • Builds a QSAR model using the activity data.
   • Evaluates a molecule using the trained model and prints the predicted pIC50 value.
   • Prints target information and activity data.
   • Generates a plot comparing the predicted and real pIC50 values.

This code provides a basic framework for retrieving target information, building QSAR models, and evaluating molecules using molecular fingerprints. It also includes utility functions for data visualization and identifying important pharmacophores.