SAR versus QSAR comparison MCQs With Answer

SAR versus QSAR comparison MCQs With Answer

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Understanding SAR versus QSAR comparison MCQs With Answer is essential for B. Pharm students focusing on rational drug design and lead optimization. SAR (structure–activity relationship) describes qualitative links between chemical structure and biological effect, while QSAR (quantitative structure–activity relationship) uses molecular descriptors and statistical models to predict potency, ADMET, and selectivity. This comparison highlights key concepts—molecular descriptors, 3D‑QSAR methods (CoMFA/CoMSIA), Hansch and Free‑Wilson approaches, model validation (R2, Q2, RMSEP), applicability domain, and common pitfalls like overfitting and multicollinearity. These focused MCQs with answers reinforce core principles and practical considerations in descriptor selection, dataset curation, and model interpretation. Now let’s test your knowledge with 30 MCQs on this topic.

Q1. What fundamentally distinguishes SAR from QSAR?

  • SAR is qualitative relationships between structure and activity, QSAR is quantitative mathematical modeling
  • SAR uses complex statistical models, QSAR only uses visual inspection
  • SAR requires 3D alignment, QSAR never uses 3D information
  • SAR is only for pharmacokinetics and QSAR is only for pharmacodynamics

Correct Answer: SAR is qualitative relationships between structure and activity, QSAR is quantitative mathematical modeling

Q2. What is the primary goal of a QSAR study?

  • To predict biological activity quantitatively using molecular descriptors and statistical models
  • To determine the crystal structure of drug targets
  • To perform clinical trials faster
  • To synthesize novel compounds without computational analysis

Correct Answer: To predict biological activity quantitatively using molecular descriptors and statistical models

Q3. Which of the following is a commonly used molecular descriptor in QSAR?

  • logP (partition coefficient)
  • LC-MS retention time in unrelated solvent
  • Company stock price
  • Document word count

Correct Answer: logP (partition coefficient)

Q4. What does CoMFA stand for in 3D-QSAR methods?

  • Comparative Molecular Field Analysis
  • Compound Molecular Fragment Assembly
  • Computational Model for Activity Forecasting
  • Conformational Molecular Feature Assessment

Correct Answer: Comparative Molecular Field Analysis

Q5. How do Hansch and Free‑Wilson approaches differ?

  • Free‑Wilson uses substituent indicator variables; Hansch relates activity to physicochemical descriptors like logP
  • Hansch uses indicator variables; Free‑Wilson uses logP exclusively
  • Both are identical methods with different names
  • Free‑Wilson requires 3D structures while Hansch does not

Correct Answer: Free‑Wilson uses substituent indicator variables; Hansch relates activity to physicochemical descriptors like logP

Q6. What does Q2 (cross‑validated R2) indicate in QSAR model evaluation?

  • Q2 measures internal predictive ability estimated by cross‑validation
  • Q2 is the number of descriptors used in the model
  • Q2 indicates computational time required for modeling
  • Q2 is the external test set error

Correct Answer: Q2 measures internal predictive ability estimated by cross‑validation

Q7. Which practice most commonly leads to overfitting in QSAR models?

  • Using more descriptors than justified by dataset size (overparameterization)
  • Removing redundant descriptors with feature selection
  • Performing proper external validation
  • Standardizing descriptor units

Correct Answer: Using more descriptors than justified by dataset size (overparameterization)

Q8. What is the applicability domain of a QSAR model?

  • The chemical space or range of descriptor values where a QSAR model makes reliable predictions
  • The set of software licenses needed to run the model
  • The total number of features calculated for all compounds
  • The container where the dataset is stored

Correct Answer: The chemical space or range of descriptor values where a QSAR model makes reliable predictions

Q9. Why is descriptor scaling (normalization/standardization) important in QSAR?

  • To normalize descriptor ranges so variables contribute comparably and models converge properly
  • To increase the absolute values of all descriptors for easier display
  • To remove biological relevance from descriptors
  • To randomly shuffle descriptor values

Correct Answer: To normalize descriptor ranges so variables contribute comparably and models converge properly

Q10. When is partial least squares (PLS) particularly useful in QSAR?

  • When multicollinearity among descriptors exists and dimensionality reduction is needed
  • When there is only one descriptor available
  • Only for classification problems with binary endpoints
  • When descriptors are independent and few in number

Correct Answer: When multicollinearity among descriptors exists and dimensionality reduction is needed

Q11. What defines external validation in QSAR?

  • Testing the model on a separate external test set not used during model training
  • Using the same data for training and reporting R2
  • Validating the model by visual inspection alone
  • Running the model on shuffled labels

Correct Answer: Testing the model on a separate external test set not used during model training

Q12. How is pIC50 related to IC50 and what does a larger pIC50 indicate?

  • pIC50 is -log10(IC50); larger pIC50 indicates higher potency
  • pIC50 equals IC50; larger pIC50 indicates lower potency
  • pIC50 is IC50 multiplied by 10; larger pIC50 indicates toxicity
  • pIC50 is unrelated to IC50 and measures lipophilicity

Correct Answer: pIC50 is -log10(IC50); larger pIC50 indicates higher potency

Q13. Which is NOT a prerequisite for reliable 3D‑QSAR?

  • Completely unrelated target proteins among dataset compounds
  • Consistent bioassay conditions across compounds
  • Accurate molecular alignment and common binding mode
  • Representative and chemically relevant conformations

Correct Answer: Completely unrelated target proteins among dataset compounds

Q14. Which statistical method is appropriate for classification QSAR problems?

  • Logistic regression
  • Multiple linear regression (MLR) for continuous outcomes only
  • CoMFA (a 3D method for continuous potency)
  • Hansch analysis (originally regression for continuous activity)

Correct Answer: Logistic regression

Q15. Which diagnostic helps detect multicollinearity among descriptors?

  • Variance inflation factor (VIF)
  • pIC50 transformation
  • Root mean square deviation (RMSD) of geometry
  • Williams plot only

Correct Answer: Variance inflation factor (VIF)

Q16. What do topological descriptors describe?

  • Topological descriptors describe molecular connectivity irrespective of 3D geometry
  • Topological descriptors measure only 3D steric fields
  • Topological descriptors quantify solvent properties
  • Topological descriptors are the experimental bioassay readouts

Correct Answer: Topological descriptors describe molecular connectivity irrespective of 3D geometry

Q17. What is a pharmacophore in the context of SAR/QSAR?

  • Spatial arrangement of features necessary for biological activity
  • A single numerical descriptor used in Hansch equations
  • The name of a QSAR software package
  • The solvent used in bioassays

Correct Answer: Spatial arrangement of features necessary for biological activity

Q18. Why must biological activity data be consistent when building QSAR models?

  • Using consistent assay conditions and standard units ensures reliable, comparable activity values
  • Consistency makes descriptors unnecessary
  • Inconsistent data improves model generalization
  • Biological assays are irrelevant for QSAR

Correct Answer: Using consistent assay conditions and standard units ensures reliable, comparable activity values

Q19. Which software is commonly used for QSAR modeling and validation?

  • QSARINS
  • Adobe Illustrator
  • Windows Media Player
  • Microsoft Word

Correct Answer: QSARINS

Q20. What is leave‑one‑out cross‑validation (LOO‑CV)?

  • Each compound is left out once and the model is trained on remaining compounds to predict the left‑out compound
  • All compounds are left out and model is not trained
  • Only half the dataset is used repeatedly for training without systematic exclusion
  • Cross‑validation using an external test set only

Correct Answer: Each compound is left out once and the model is trained on remaining compounds to predict the left‑out compound

Q21. Which technique reduces descriptor dimensionality while retaining variance?

  • Principal component analysis (PCA)
  • Hansch analysis
  • Leverage calculation
  • pIC50 conversion

Correct Answer: Principal component analysis (PCA)

Q22. How does pKa of a molecule affect QSAR-relevant properties?

  • pKa influences ionization state, affecting membrane permeability and binding interactions
  • pKa only affects color and is irrelevant to QSAR
  • pKa is synonymous with logP and measures lipophilicity
  • pKa determines molecular weight

Correct Answer: pKa influences ionization state, affecting membrane permeability and binding interactions

Q23. Which metric primarily reflects goodness‑of‑fit for training data?

  • Coefficient of determination (R2)
  • Q2 (cross‑validated R2)
  • Leverage value
  • Descriptor mean

Correct Answer: Coefficient of determination (R2)

Q24. Which metric assesses external prediction error?

  • Root mean square error of prediction (RMSEP)
  • Internal R2 from training set only
  • Number of descriptors
  • Alignment RMSD alone

Correct Answer: Root mean square error of prediction (RMSEP)

Q25. How do ensemble methods like random forest benefit QSAR modeling?

  • Random forest reduces overfitting and handles nonlinear relationships via ensemble learning
  • Random forest always gives linear models only
  • Ensemble methods require no descriptors
  • Random forest is only for image analysis and not applicable to QSAR

Correct Answer: Random forest reduces overfitting and handles nonlinear relationships via ensemble learning

Q26. How should molecules be aligned for reliable 3D‑QSAR?

  • Superposition based on a common pharmacophore or binding conformation
  • Random orientation without consideration of binding mode
  • Alignment by molecular weight only
  • No alignment is necessary for 3D‑QSAR

Correct Answer: Superposition based on a common pharmacophore or binding conformation

Q27. Which approach helps assess applicability domain using leverage?

  • Leverage (hat) values plotted against standardized residuals (Williams plot)
  • Using pIC50 alone to define domain
  • A histogram of molecular weights only
  • Cross‑validation without any diagnostic plots

Correct Answer: Leverage (hat) values plotted against standardized residuals (Williams plot)

Q28. What is a key limitation of SAR compared to QSAR?

  • SAR is qualitative and cannot predict numerical potency values reliably
  • SAR always requires sophisticated statistics
  • SAR provides exact IC50 predictions
  • SAR replaces the need for any experimental assays

Correct Answer: SAR is qualitative and cannot predict numerical potency values reliably

Q29. When is it preferable to use SAR instead of QSAR?

  • When dataset is small or heterogeneous making quantitative modeling unreliable
  • When thousands of reliable data points are available for modeling
  • When numerical prediction accuracy is the primary goal
  • When automatic descriptor selection tools are available

Correct Answer: When dataset is small or heterogeneous making quantitative modeling unreliable

Q30. Which is best practice in dataset curation for QSAR modeling?

  • Remove duplicates, standardize tautomers/ionization states, and verify assay consistency
  • Combine results from unrelated targets without checking assay conditions
  • Keep raw mixed units and inconsistent activity measures
  • Remove all polar compounds regardless of relevance

Correct Answer: Remove duplicates, standardize tautomers/ionization states, and verify assay consistency

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