Introduction: Hansch analysis is a cornerstone of QSAR (Quantitative Structure–Activity Relationship) studies used by B.Pharm students to relate chemical structure to biological activity. This method uses descriptors—hydrophobicity (π or LogP), electronic (Hammett σ), and steric (Taft Es)—within the Hansch equation to build multiple linear regression models that predict potency (e.g., pIC50). Key concepts include descriptor selection, regression coefficients interpretation, r² and q² validation, overfitting, and applicability domain. Understanding Hansch analysis helps in rational drug design, lead optimization, and mechanistic insight. Now let’s test your knowledge with 30 MCQs on this topic.
Q1. Which descriptor in Hansch analysis primarily represents hydrophobicity?
- Hammett sigma (σ)
- Taft steric constant (Es)
- Partition coefficient or pi (π) / LogP
- Topological polar surface area (TPSA)
Correct Answer: Partition coefficient or pi (π) / LogP
Q2. The Hansch equation commonly used in QSAR is best described as which type of model?
- Nonlinear neural network model
- Multiple linear regression model
- Single-variable logistic regression
- Clustering algorithm
Correct Answer: Multiple linear regression model
Q3. In Hansch analysis, what is the typical dependent variable when comparing potencies?
- LogP
- pIC50 or pKi (negative logarithm of activity)
- Hammett sigma (σ)
- Molecular weight
Correct Answer: pIC50 or pKi (negative logarithm of activity)
Q4. Which substituent constant represents electronic effects in Hansch analysis?
- Taft Es
- Hammett sigma (σ)
- π constant (pi)
- Linkage index
Correct Answer: Hammett sigma (σ)
Q5. Why is a quadratic (π and π²) term often included for hydrophobicity in Hansch equations?
- To model experimental error
- To capture an optimum (parabolic) relationship between lipophilicity and activity
- Because π values are noisy and need smoothing
- To represent electronic effects
Correct Answer: To capture an optimum (parabolic) relationship between lipophilicity and activity
Q6. What does a positive coefficient for π in a Hansch model usually indicate?
- Increased hydrophobicity reduces activity
- Increased hydrophobicity increases activity
- Poor model fit
- Descriptor is irrelevant
Correct Answer: Increased hydrophobicity increases activity
Q7. Which statistical parameter primarily measures the proportion of variance in activity explained by the model?
- q² (cross-validated R²)
- RMSE (root mean square error)
- r² (coefficient of determination)
- VIF (variance inflation factor)
Correct Answer: r² (coefficient of determination)
Q8. What does q² (leave-one-out cross-validation) assess in QSAR/Hansch analysis?
- Internal predictive ability of the model
- Descriptor collinearity
- Experimental reproducibility
- External validation on an independent test set
Correct Answer: Internal predictive ability of the model
Q9. Which problem arises when descriptors are highly correlated in a Hansch regression?
- Overfitting is impossible
- Multicollinearity, inflating coefficient variance
- Model becomes linear
- Improved external predictivity automatically
Correct Answer: Multicollinearity, inflating coefficient variance
Q10. Which test is commonly used to detect multicollinearity among descriptors?
- Y-randomization
- Variance inflation factor (VIF)
- Leave-one-out cross-validation (LOO)
- Principal component regression (PCR)
Correct Answer: Variance inflation factor (VIF)
Q11. What is Y-randomization used to check in QSAR modeling?
- Descriptor scaling requirements
- Whether model results could arise by chance
- Calculation of LogP values
- Applicability domain boundaries
Correct Answer: Whether model results could arise by chance
Q12. In Hansch analysis, which descriptor accounts for steric effects?
- Hammett sigma (σ)
- Taft steric constant (Es)
- Partition coefficient (π)
- Hydrogen bond donor count
Correct Answer: Taft steric constant (Es)
Q13. Which descriptor would be most relevant for modeling hydrogen-bonding contributions in QSAR?
- π constant for lipophilicity
- Hammett sigma (σ)
- Number of hydrogen-bond donors/acceptors or HBD/HBA counts
- Taft Es
Correct Answer: Number of hydrogen-bond donors/acceptors or HBD/HBA counts
Q14. Why transform activity to pIC50 or pKi before Hansch regression?
- To linearize the relation and stabilize variance
- To increase descriptor correlation automatically
- To remove stereochemistry effects
- To make model nonlinear
Correct Answer: To linearize the relation and stabilize variance
Q15. What does an r² of 0.85 indicate about a Hansch QSAR model?
- 85% of descriptor variance is explained
- Model explains 85% of variance in biological activity
- Model is definitely free from overfitting
- External predictivity is guaranteed
Correct Answer: Model explains 85% of variance in biological activity
Q16. Which practice improves robustness and prevents overfitting in Hansch modeling?
- Using as many descriptors as possible
- Using cross-validation and external test sets
- Ignoring statistical significance
- Maximizing the number of terms regardless of sample size
Correct Answer: Using cross-validation and external test sets
Q17. What is the applicability domain in QSAR/Hansch models?
- The range of descriptor values where model predictions are reliable
- A list of all descriptors available
- Only the training set molecules
- Statistical significance threshold
Correct Answer: The range of descriptor values where model predictions are reliable
Q18. Which method can be used to select an optimal subset of descriptors for a Hansch model?
- Stepwise regression or genetic algorithms
- Random guessing
- Computing only molecular weight
- Ignoring descriptor correlation
Correct Answer: Stepwise regression or genetic algorithms
Q19. What does a negative coefficient for Hammett σ typically suggest about electronic effects?
- Electron-withdrawing substituents increase activity
- Electron-donating substituents increase activity
- Electronic effects are irrelevant
- Model has no hydrophobic term
Correct Answer: Electron-donating substituents increase activity
Q20. Which validation metric assesses external predictive power on an independent set?
- Leave-one-out q²
- r²pred or R²ext (predictive R² for test set)
- Number of descriptors
- Taft Es value
Correct Answer: r²pred or R²ext (predictive R² for test set)
Q21. What is the main limitation of univariate Hansch correlations compared to multivariate models?
- They are always more predictive
- They ignore interactions among hydrophobic, electronic, and steric factors
- They require complex software
- They automatically include quadratic terms
Correct Answer: They ignore interactions among hydrophobic, electronic, and steric factors
Q22. Which descriptor type is derived from calculated molecular properties rather than substituent constants?
- Hammett sigma (σ)
- Quantum chemical descriptors like HOMO/LUMO energies
- Taft Es
- Classical π constants
Correct Answer: Quantum chemical descriptors like HOMO/LUMO energies
Q23. In Hansch studies, what is the purpose of standardizing or scaling descriptors?
- To change the chemical meaning of descriptors
- To ensure comparability and numerical stability in regression
- To remove activity dependence
- To eliminate the need for validation
Correct Answer: To ensure comparability and numerical stability in regression
Q24. Which descriptor would best capture molecular size or volume effects?
- Topological polar surface area (TPSA)
- Molecular volume or molecular refractivity (MR)
- Hammett sigma (σ)
- π constant
Correct Answer: Molecular volume or molecular refractivity (MR)
Q25. What does a high variance inflation factor (VIF > 10) indicate?
- Model has perfect fit
- Severe multicollinearity among descriptors
- Excellent external predictivity
- Descriptors are independent
Correct Answer: Severe multicollinearity among descriptors
Q26. Which technique reduces descriptor dimensionality before regression?
- Principal component analysis (PCA)
- Hammett plotting
- Y-randomization
- Taft correction
Correct Answer: Principal component analysis (PCA)
Q27. What is an important mechanistic advantage of Hansch analysis compared to black-box methods?
- It always yields higher predictive accuracy
- It provides interpretable coefficients linked to hydrophobic, electronic, and steric contributions
- It requires no statistical validation
- It eliminates the need for chemical intuition
Correct Answer: It provides interpretable coefficients linked to hydrophobic, electronic, and steric contributions
Q28. During model building, which rule of thumb relates sample size to number of descriptors to avoid overfitting?
- Use more descriptors than molecules
- At least 5–10 molecules per descriptor
- Number of descriptors must equal number of molecules
- Descriptor count is irrelevant
Correct Answer: At least 5–10 molecules per descriptor
Q29. What does external validation with an independent test set primarily demonstrate?
- Model’s ability to describe training set only
- Model’s predictive power on unseen compounds
- That descriptors are correlated
- That r² on training set is maximized
Correct Answer: Model’s predictive power on unseen compounds
Q30. Which practice helps provide chemical interpretability alongside statistical validity in Hansch QSAR?
- Including only random descriptors
- Combining descriptor selection with mechanistic reasoning and validation
- Maximizing R² regardless of descriptor meaning
- Ignoring outliers without investigation
Correct Answer: Combining descriptor selection with mechanistic reasoning and validation
I am a Registered Pharmacist under the Pharmacy Act, 1948, and the founder of PharmacyFreak.com. I hold a Bachelor of Pharmacy degree from Rungta College of Pharmaceutical Science and Research. With a strong academic foundation and practical knowledge, I am committed to providing accurate, easy-to-understand content to support pharmacy students and professionals. My aim is to make complex pharmaceutical concepts accessible and useful for real-world application.
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