Introduction to Python for drug development and discovery

This guide will introduce you to the fundamental concepts of using Python in drug development and discovery. You will learn how to set up your environment in Deepnote, manipulate biological data, perform molecular docking, and use machine learning models to predict drug-target interactions.

Data manipulation and analysis

Loading and exploring data

We will use Pandas to load and explore a sample dataset of drug molecules.

import pandas as pd

# Load the dataset
data = pd.read_csv('<https://example.com/drug_data.csv>')

# Display the first few rows of the dataset
data.head()

Data cleaning

Ensure that the data is clean and ready for analysis.

# Checking for missing values
print(data.isnull().sum())

# Dropping rows with missing values
data.dropna(inplace=True)

# Checking the data types
print(data.dtypes)

Molecular descriptors calculation

RDKit for molecular descriptors

RDKit is a powerful library for cheminformatics that allows you to compute molecular descriptors.

from rdkit import Chem
from rdkit.Chem import Descriptors

# Function to calculate molecular descriptors
def calculate_descriptors(smiles):
    mol = Chem.MolFromSmiles(smiles)
    descriptors = {
        'MolecularWeight': Descriptors.MolWt(mol),
        'LogP': Descriptors.MolLogP(mol),
        'NumHDonors': Descriptors.NumHDonors(mol),
        'NumHAcceptors': Descriptors.NumHAcceptors(mol),
    }
    return descriptors

# Apply the function to the dataset
data['descriptors'] = data['SMILES'].apply(calculate_descriptors)

# Convert the descriptors to a DataFrame
descriptors_df = pd.json_normalize(data['descriptors'])
data = data.join(descriptors_df)
data.drop(columns=['descriptors'], inplace=True)

data.head()

Molecular docking

Preparing for docking

We will use Open Babel to convert molecular formats and prepare files for docking simulations.

import openbabel

# Convert SMILES to PDB format using Open Babel
def smiles_to_pdb(smiles, output_filename):
    obConversion = openbabel.OBConversion()
    obConversion.SetInAndOutFormats("smi", "pdb")
    mol = openbabel.OBMol()
    obConversion.ReadString(mol, smiles)
    obConversion.WriteFile(mol, output_filename)

# Example usage
smiles_to_pdb('CCO', 'ethanol.pdb')

Performing docking (Placeholder)

Here, we provide a placeholder for the docking simulation process. In practice, you would use tools like AutoDock Vina for this step.

# This is a placeholder for actual docking code
def perform_docking(receptor_file, ligand_file):
    # Code to perform docking
    pass

# Example usage
perform_docking('receptor.pdb', 'ethanol.pdb')

Machine learning for drug discovery

Data preparation

Prepare the dataset for machine learning.

from sklearn.model_selection import train_test_split

# Selecting features and target variable
X = data[['MolecularWeight', 'LogP', 'NumHDonors', 'NumHAcceptors']]
y = data['Activity']

# Splitting the dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Building a machine learning model

We will use scikit-learn to build a simple Random Forest model to predict drug activity.

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix

# Initialize the model
model = RandomForestClassifier(n_estimators=100, random_state=42)

# Train the model
model.fit(X_train, y_train)

# Predict on the test set
y_pred = model.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)

print(f'Accuracy: {accuracy}')
print(f'Confusion Matrix:\\\\n{conf_matrix}')

Visualization

Visualize the results using Matplotlib and Seaborn.

import matplotlib.pyplot as plt
import seaborn as sns

# Confusion matrix heatmap
plt.figure(figsize=(10, 7))
sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.title('Confusion Matrix')
plt.show()

Conclusion

In this guide, you have learned how to set up a Python environment in Deepnote, manipulate biological data, calculate molecular descriptors, prepare for molecular docking, and build a machine-learning model for drug discovery. With these skills, you can start exploring more advanced techniques and tools in drug development.