History and development of QSAR MCQs With Answer

History and development of QSAR MCQs With Answer

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Quantitative Structure–Activity Relationship (QSAR) traces its history from classical Hansch and Free–Wilson approaches to modern 3D‑QSAR, chemoinformatics and machine‑learning models used in drug discovery and toxicity prediction. The development of QSAR introduced descriptor calculation, descriptor types (constitutional, topological, electronic, geometrical), statistical modeling (regression, PLS, ANN), descriptor selection, model validation (cross‑validation, external test sets, Y‑randomization) and applicability domain concepts. Understanding these milestones, OECD validation principles, model interpretability and practical ADMET applications is essential for B.Pharm students to apply QSAR in lead optimization and safety assessment. Now let’s test your knowledge with 30 MCQs on this topic.

Q1. What does QSAR stand for?

  • Quantitative Structure–Activity Relationship
  • Qualitative Structure–Activity Ranking
  • Quantum Structural Analytical Reaction
  • Quantified Structure–Affinity Rule

Correct Answer: Quantitative Structure–Activity Relationship

Q2. Which scientist is most closely associated with the early linear QSAR approach based on substituent constants?

  • Corwin Hansch
  • Martin Karplus
  • Gertrude Elion
  • Linus Pauling

Correct Answer: Corwin Hansch

Q3. The Free–Wilson method primarily relates biological activity to which of the following?

  • Presence or absence of specific substituents at defined positions
  • 3D steric and electrostatic fields
  • Quantum mechanical orbital energies
  • Macroscopic pharmacokinetic parameters

Correct Answer: Presence or absence of specific substituents at defined positions

Q4. Which one is a classic 3D‑QSAR technique?

  • CoMFA (Comparative Molecular Field Analysis)
  • Hansch regression
  • Free–Wilson analysis
  • Hammett correlation

Correct Answer: CoMFA (Comparative Molecular Field Analysis)

Q5. Which of the following is NOT a common descriptor type used in QSAR?

  • Topological descriptors
  • Geometrical descriptors
  • Electronic descriptors
  • Clinical trial endpoint descriptors

Correct Answer: Clinical trial endpoint descriptors

Q6. Which descriptor is commonly used to represent lipophilicity in QSAR models?

  • logP (octanol–water partition coefficient)
  • pKa value
  • Number of rotatable bonds
  • Molar refractivity only

Correct Answer: logP (octanol–water partition coefficient)

Q7. In QSAR model assessment, what does Q² usually indicate?

  • Cross‑validated predictive ability (internal predictivity)
  • Model complexity measured by number of descriptors
  • Maximum likelihood estimate of parameters
  • Experimental error in activity measurements

Correct Answer: Cross‑validated predictive ability (internal predictivity)

Q8. How many principles did OECD propose for documenting and validating QSAR models for regulatory use?

  • Five
  • Three
  • Seven
  • Ten

Correct Answer: Five

Q9. What is the applicability domain of a QSAR model?

  • The chemical space and conditions where the model’s predictions are considered reliable
  • The programming environment used to build the model
  • The list of journals that accepted the model publication
  • The maximum number of descriptors allowed

Correct Answer: The chemical space and conditions where the model’s predictions are considered reliable

Q10. What is the primary purpose of Y‑randomization (Y‑scrambling) in QSAR?

  • To test whether a model’s performance is due to chance correlation
  • To increase the number of descriptors available
  • To normalize descriptor scales
  • To create 3D conformers for alignment

Correct Answer: To test whether a model’s performance is due to chance correlation

Q11. Which pattern typically indicates overfitting in QSAR modeling?

  • Very high training R² but poor performance on external test set
  • Low training R² and equally low test R²
  • Identical performance across training and validation sets
  • High predictive power with minimal descriptors

Correct Answer: Very high training R² but poor performance on external test set

Q12. Which of the following is a commonly used descriptor selection method in QSAR?

  • Genetic algorithms for variable selection
  • Random deletion without criteria
  • Manual selection by alphabetical order
  • Discarding descriptors based on file size

Correct Answer: Genetic algorithms for variable selection

Q13. CoMFA models primarily analyze which types of molecular fields?

  • Steric and electrostatic fields around aligned molecules
  • Only hydrogen bond counts
  • pKa distributions of solvents
  • Clinical dosing regimens

Correct Answer: Steric and electrostatic fields around aligned molecules

Q14. What defines external validation in QSAR?

  • Evaluating model predictions on an independent test set not used during model training
  • Cross‑validating by leaving out one descriptor at a time
  • Assessing model performance using the same training data
  • Using simulated data generated from the model itself

Correct Answer: Evaluating model predictions on an independent test set not used during model training

Q15. Which metric directly quantifies average prediction error in a QSAR model?

  • RMSE (Root Mean Square Error)
  • R² (coefficient of determination)
  • Descriptor count
  • Number of cross‑validation folds

Correct Answer: RMSE (Root Mean Square Error)

Q16. Multicollinearity among descriptors in a QSAR model mainly causes which problem?

  • Instability and inflated variance of regression coefficients
  • Improved interpretability of individual descriptors
  • Decreased computational cost
  • Guaranteed higher external predictivity

Correct Answer: Instability and inflated variance of regression coefficients

Q17. Why is descriptor scaling (e.g., standardization) important before model building?

  • To bring descriptors to comparable scales so algorithms weight them fairly
  • To increase the raw values of descriptors for easier reading
  • To remove the need for cross‑validation
  • To automatically remove outliers

Correct Answer: To bring descriptors to comparable scales so algorithms weight them fairly

Q18. Which software/tool is freely available for calculating many molecular descriptors?

  • PaDEL‑Descriptor
  • SYBYL (proprietary only)
  • Commercial-only Dragon version
  • MOE licensed version

Correct Answer: PaDEL‑Descriptor

Q19. Which method is most suitable for capturing complex non‑linear relationships in QSAR?

  • Artificial neural networks (ANN)
  • Ordinary least squares linear regression only
  • Simple mean imputation
  • Counting heavy atoms alone

Correct Answer: Artificial neural networks (ANN)

Q20. Compared with CoMFA, CoMSIA adds which feature to 3D‑QSAR analysis?

  • Additional similarity fields such as hydrophobic and hydrogen‑bond donor/acceptor fields
  • Elimination of the need for molecular alignment entirely
  • Direct prediction of clinical efficacy without training data
  • Automatic descriptor selection by file name

Correct Answer: Additional similarity fields such as hydrophobic and hydrogen‑bond donor/acceptor fields

Q21. What is the main use of Principal Component Analysis (PCA) in QSAR?

  • Dimensionality reduction and decorrelation of descriptors
  • Direct calculation of logP values
  • Automatic external validation
  • Generating 3D conformers for CoMFA

Correct Answer: Dimensionality reduction and decorrelation of descriptors

Q22. Why is mechanistic interpretation of a QSAR model valuable for regulatory acceptance?

  • It helps relate model descriptors to biological mode of action, increasing trust and transparency
  • It reduces the need for experimental data entirely
  • Regulators prefer opaque black‑box models always
  • Mechanistic interpretation shortens computational time

Correct Answer: It helps relate model descriptors to biological mode of action, increasing trust and transparency

Q23. Leave‑one‑out (LOO) cross‑validation means:

  • Each compound is left out once as a validation case while the model is trained on the remaining data
  • All descriptors are left out sequentially
  • The entire dataset is used for training and testing simultaneously
  • Only one descriptor is used to build the model

Correct Answer: Each compound is left out once as a validation case while the model is trained on the remaining data

Q24. Which parameter is NOT part of Lipinski’s Rule of Five?

  • Topological polar surface area (TPSA)
  • Molecular weight ≤ 500 Da
  • LogP ≤ 5
  • Hydrogen bond donors ≤ 5

Correct Answer: Topological polar surface area (TPSA)

Q25. What is the difference between QSAR and QSPR?

  • QSAR models biological activity; QSPR models physicochemical or material properties
  • QSAR uses only quantum descriptors; QSPR uses only topology
  • There is no difference; they are identical terms
  • QSPR is only used for proteins while QSAR is for small molecules

Correct Answer: QSAR models biological activity; QSPR models physicochemical or material properties

Q26. During dataset curation for QSAR, which action is essential?

  • Removing salts, duplicates and standardizing tautomers
  • Randomizing activity values without record
  • Keeping all stereoisomers unlabeled and unstandardized
  • Mixing experimental and predicted activities without flagging

Correct Answer: Removing salts, duplicates and standardizing tautomers

Q27. Which approach helps mitigate descriptor collinearity before regression modeling?

  • Principal Component Analysis (PCA)
  • Increasing the number of descriptors arbitrarily
  • Sorting descriptors alphabetically
  • Reducing the dataset size by half randomly

Correct Answer: Principal Component Analysis (PCA)

Q28. Which metric specifically estimates external predictive ability of a QSAR model?

  • R²pred (external predictive R²)
  • Number of descriptors used
  • Mean molecular weight of the dataset
  • Training-set R² only

Correct Answer: R²pred (external predictive R²)

Q29. For small datasets, which practice helps reduce the risk of chance correlations?

  • Performing Y‑randomization and using external validation if possible
  • Using as many descriptors as possible without selection
  • Reporting only the highest training R²
  • Skipping cross‑validation entirely

Correct Answer: Performing Y‑randomization and using external validation if possible

Q30. Which of the following is explicitly one of the OECD principles for QSAR model documentation?

  • Defined domain of applicability
  • Mandatory use of neural networks
  • Exact number of descriptors must be five
  • Use of proprietary software only

Correct Answer: Defined domain of applicability

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