QSAR – concept and significance MCQs With Answer

Quantitative Structure-Activity Relationship (QSAR) is a computational approach that correlates chemical structure with biological activity using molecular descriptors and statistical models. For B. Pharm students, QSAR is essential for rational drug design, lead optimization, and ADME/Tox prediction. Key topics include descriptor types (topological, electronic, hydrophobic, geometrical), model-building methods (MLR, PLS, SVM, random forest), validation strategies (cross-validation, external validation, Y‑randomization), and applicability domain. Mastery of descriptor calculation, feature selection, alignment in 3D-QSAR, and interpretability helps predict activity and reduce experimental costs. Now let’s test your knowledge with 30 MCQs on this topic.

Q1. What does QSAR primarily relate to biological activity?

• Molecular descriptors derived from chemical structure
• Clinical trial outcomes
• Manufacturer batch numbers
• Physician prescribing habits

Correct Answer: Molecular descriptors derived from chemical structure

Q2. Which descriptor type captures hydrophobic character important for membrane permeability?

• Topological descriptors
• Electronic descriptors
• Hydrophobic (logP) descriptors
• Geometrical descriptors

Correct Answer: Hydrophobic (logP) descriptors

Q3. What is the purpose of splitting data into training and test sets in QSAR?

• To increase descriptor dimensionality
• To evaluate model generalizability on unseen data
• To remove outliers from the dataset
• To normalize descriptor scales

Correct Answer: To evaluate model generalizability on unseen data

Q4. Which statistical method is commonly used for simple, interpretable QSAR models?

• Partial least squares (PLS)
• Multiple linear regression (MLR)
• Convolutional neural networks (CNN)
• K-means clustering

Correct Answer: Multiple linear regression (MLR)

Q5. What does a high q2 (cross-validated R2) value indicate?

• Poor internal predictivity
• Good internal predictive ability of the model
• Overfitting to the training data
• Large applicability domain

Correct Answer: Good internal predictive ability of the model

Q6. Which technique is specific to 3D-QSAR and visualizes steric and electrostatic fields?

• HQSAR
• CoMFA (Comparative Molecular Field Analysis)
• MLR
• Topological analysis

Correct Answer: CoMFA (Comparative Molecular Field Analysis)

Q7. What is Y‑randomization used to test in QSAR modeling?

• Descriptor calculation accuracy
• Whether the model performance is due to chance correlations
• Computational speed of the algorithm
• Applicability domain size

Correct Answer: Whether the model performance is due to chance correlations

Q8. Which descriptor category includes molecular weight and atom counts?

• Electronic descriptors
• Geometrical descriptors
• Constitutional descriptors
• Topological descriptors

Correct Answer: Constitutional descriptors

Q9. What problem arises when descriptors are highly collinear?

• Improved model interpretability
• Unstable coefficient estimates and multicollinearity issues
• Increased external predictivity
• Reduced descriptor count automatically

Correct Answer: Unstable coefficient estimates and multicollinearity issues

Q10. Which feature selection method searches descriptor space using evolution-inspired operators?

• Stepwise regression
• Genetic algorithm (GA)
• Principal component analysis (PCA)
• Hierarchical clustering

Correct Answer: Genetic algorithm (GA)

Q11. In QSAR, the applicability domain defines:

• The software license terms
• The chemical space where model predictions are reliable
• The type of machine used for computation
• The number of descriptors used

Correct Answer: The chemical space where model predictions are reliable

Q12. Which validation assesses predictive power on completely unseen external molecules?

• Internal cross-validation (leave-one-out)
• External validation using a test set
• Descriptor scaling
• Descriptor pruning

Correct Answer: External validation using a test set

Q13. What is the main advantage of PLS over MLR in QSAR?

• PLS cannot handle collinearity
• PLS reduces dimensionality and handles multicollinearity well
• PLS always produces simpler models than MLR
• PLS does not require descriptor calculation

Correct Answer: PLS reduces dimensionality and handles multicollinearity well

Q14. Which of the following is a 2D-QSAR method that uses fragments as descriptors?

• CoMSIA
• HQSAR (Hologram QSAR)
• CoMFA
• 3D field mapping

Correct Answer: HQSAR (Hologram QSAR)

Q15. Which metric measures average magnitude of prediction errors (lower is better)?

• r2
• q2
• RMSE (Root Mean Square Error)
• Descriptor variance

Correct Answer: RMSE (Root Mean Square Error)

Q16. Which preprocessing step helps make descriptors comparable by scale?

• Y‑randomization
• Descriptor normalization or standardization
• External validation
• Grid spacing selection

Correct Answer: Descriptor normalization or standardization

Q17. In 3D-QSAR, why is molecular alignment important?

• Alignment defines comparative positions of molecules for field calculations
• Alignment increases computation time without benefit
• Alignment eliminates need for descriptors
• Alignment is only used for 2D-QSAR

Correct Answer: Alignment defines comparative positions of molecules for field calculations

Q18. Which machine-learning method is non-linear and useful for complex QSAR patterns?

• Multiple linear regression (MLR)
• Support vector machine (SVM)
• Stepwise regression
• Simple averaging

Correct Answer: Support vector machine (SVM)

Q19. What does an r2 value close to 1 indicate for a QSAR model on training data?

• Perfect external predictivity always
• Good fit to the training data
• Model has no descriptors
• Applicability domain is infinite

Correct Answer: Good fit to the training data

Q20. Which descriptor type captures electronic distribution like partial charges?

• Hydrophobic descriptors
• Electronic descriptors
• Constitutional descriptors
• Topological indices

Correct Answer: Electronic descriptors

Q21. What is a common sign of model overfitting?

• High training r2 but low external predictive performance
• Low training r2 and high test performance
• Balanced training and test performance
• Small number of descriptors

Correct Answer: High training r2 but low external predictive performance

Q22. CoMSIA differs from CoMFA by:

• Using hologram fragments
• Comparing only 2D descriptors
• Using Gaussian-type functions for similarity fields including hydrophobic and H-bond descriptors
• Being identical in all procedures

Correct Answer: Using Gaussian-type functions for similarity fields including hydrophobic and H-bond descriptors

Q23. Which approach helps interpret which molecular features increase activity?

• Random descriptor removal
• Contour maps from 3D-QSAR and coefficient interpretation in MLR/PLS
• Using larger training sets only
• Ignoring applicability domain

Correct Answer: Contour maps from 3D-QSAR and coefficient interpretation in MLR/PLS

Q24. Which performance measure evaluates how much variance is explained by the model?

• RMSE
• r2 (coefficient of determination)
• Descriptor count
• Grid spacing

Correct Answer: r2 (coefficient of determination)

Q25. What is the role of an applicability domain (AD) check before using a QSAR prediction?

• To assess whether the compound lies in model’s reliable chemical space
• To compute r2 automatically
• To remove descriptors that are invalid
• To convert 3D structures to 2D

Correct Answer: To assess whether the compound lies in model’s reliable chemical space

Q26. Which descriptor is a topological index representing molecular branching?

• LogP
• Wiener index
• Partial charge
• Polar surface area (PSA)

Correct Answer: Wiener index

Q27. Why is external validation preferred over only internal cross-validation?

• External validation is faster
• External validation better assesses true predictive ability on independent data
• Internal cross-validation always overestimates error
• External validation eliminates the need for descriptors

Correct Answer: External validation better assesses true predictive ability on independent data

Q28. Which QSAR workflow step directly follows descriptor calculation?

• Model deployment to production
• Feature selection and preprocessing
• Clinical trials
• Grid spacing optimization

Correct Answer: Feature selection and preprocessing

Q29. What is one benefit of consensus modeling in QSAR?

• It always produces the simplest model
• Combining predictions from multiple models can improve robustness and reduce error
• It eliminates need for validation
• It reduces descriptor diversity

Correct Answer: Combining predictions from multiple models can improve robustness and reduce error

Q30. Which QSAR practice enhances mechanistic interpretability of models?

• Using many correlated descriptors without reporting coefficients
• Prioritizing interpretable descriptors, visualizing contour maps, and reporting coefficients
• Never validating the model externally
• Avoiding reporting of applicability domain

Correct Answer: Prioritizing interpretable descriptors, visualizing contour maps, and reporting coefficients

G S Sachin

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.

Mail- Sachin@pharmacyfreak.com