Lära QSAR Basics: Feature Selection, Regression & Classification | Similarity, Clustering and Drug Discovery

QSAR, or Quantitative Structure-Activity Relationship, is a foundational concept in chemoinformatics that links the structural features of molecules to their biological or chemical activities. In drug discovery, QSAR models help you predict how new molecules might behave—such as their potency, toxicity, or other properties—based on patterns learned from known compounds. This approach saves time and resources by focusing laboratory testing on the most promising candidates.

Definition

QSAR (Quantitative Structure-Activity Relationship) is a method for predicting molecular properties based on their chemical structure.


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# Extracting multiple molecular descriptors using RDKit
from rdkit import Chem
from rdkit.Chem import Descriptors

# Example: list of SMILES for small molecules
smiles_list = ["CCO", "CCN", "c1ccccc1", "CC(=O)O"]

# Prepare a list to hold descriptor values
descriptor_names = ["MolWt", "NumHDonors", "NumHAcceptors", "MolLogP"]
results = []

for smi in smiles_list:
    mol = Chem.MolFromSmiles(smi)
    if mol:
        desc_values = [
            Descriptors.MolWt(mol),
            Descriptors.NumHDonors(mol),
            Descriptors.NumHAcceptors(mol),
            Descriptors.MolLogP(mol)
        ]
        results.append([smi] + desc_values)

# Print the descriptors table
print(f"{'SMILES':<12}{'MolWt':>8}{'HDonors':>10}{'HAcceptors':>12}{'LogP':>8}")
for row in results:
    print(f"{row[0]:<12}{row[1]:8.2f}{row[2]:10}{row[3]:12}{row[4]:8.2f}")

When building QSAR models, you often calculate many descriptors for each molecule. However, not all descriptors are equally useful. Feature selection is the process of identifying which descriptors (features) actually help your model make accurate predictions. Selecting relevant features helps you avoid overfitting, reduces computational cost, and can reveal which molecular properties are most important for the activity you are modeling.


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# Fitting a simple regression model using scikit-learn with molecular descriptors
from rdkit import Chem
from rdkit.Chem import Descriptors
from sklearn.linear_model import LinearRegression
import numpy as np

# Example: SMILES and their (fake) experimental property values
smiles_list = ["CCO", "CCN", "c1ccccc1", "CC(=O)O"]
y = [0.5, 0.8, 1.2, 0.4]  # Example property values

# Calculate descriptors: MolWt and MolLogP
X = []
for smi in smiles_list:
    mol = Chem.MolFromSmiles(smi)
    if mol:
        X.append([
            Descriptors.MolWt(mol),
            Descriptors.MolLogP(mol)
        ])
X = np.array(X)

# Fit a regression model
model = LinearRegression()
model.fit(X, y)

# Predict property for a new molecule
test_smi = "CCCO"
test_mol = Chem.MolFromSmiles(test_smi)
test_desc = np.array([[Descriptors.MolWt(test_mol), Descriptors.MolLogP(test_mol)]])
prediction = model.predict(test_desc)
print(f"Predicted property for {test_smi}: {prediction[0]:.2f}")

In QSAR, the type of prediction you want to make determines whether you use regression or classification. Regression models predict continuous values, such as solubility or binding affinity. Classification models, on the other hand, predict discrete categories, such as "active" vs "inactive" compounds. Choosing between regression and classification depends on the nature of your target property and the available experimental data.

Study More

In this chapter, we briefly touched on several Python libraries that are important for effective analysis. To deepen your knowledge and gain more hands-on practice, we recommend taking the following courses:

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Avsnitt 2. Kapitel 5

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