Hansch analysis, Free–Wilson and 2D QSAR methods MCQs With Answer

Hansch analysis, Free–Wilson and 2D QSAR methods MCQs With Answer

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Introduction

Hansch analysis, Free–Wilson and 2D QSAR methods MCQs With Answer

This quiz set is designed for M.Pharm students studying MPC 203T (Computer Aided Drug Design). It focuses on Hansch analysis, the Free–Wilson approach and modern 2D QSAR — key quantitative strategies for relating chemical structure to biological activity. Questions emphasize conceptual understanding of substituent constants, the Hansch equation and parabolic lipophilicity relationships, binary indicator variables in Free–Wilson, and commonly used 2D descriptors, model building, validation metrics and pitfalls such as multicollinearity and overfitting. Use these MCQs to strengthen problem-solving skills and prepare for exams or practical QSAR modeling tasks by applying theory to realistic drug-design scenarios.

Q1. Which statement best describes the primary goal of Hansch analysis?

  • To use binary indicator variables for each substituent position to predict activity
  • To correlate biological activity with molecular descriptors like hydrophobicity, electronic and steric parameters using linear regression
  • To compute 3D conformations and docking scores for target proteins
  • To generate chemical structures using combinatorial chemistry

Correct Answer: To correlate biological activity with molecular descriptors like hydrophobicity, electronic and steric parameters using linear regression

Q2. In the Hansch equation, the descriptor “pi” commonly refers to which property?

  • Electronic withdrawing power of a substituent
  • Hydrophobic substituent constant representing lipophilicity change
  • Steric bulk measured by molar refractivity
  • Topological index based on connectivity

Correct Answer: Hydrophobic substituent constant representing lipophilicity change

Q3. A Hansch model that includes a quadratic term for logP (e.g., a·logP + b·(logP)^2) and yields a negative coefficient for (logP)^2 indicates what about lipophilicity?

  • Activity increases indefinitely with increasing lipophilicity
  • There is an optimal logP value at which activity is maximal
  • Lipophilicity has no influence on activity
  • Activity decreases monotonically with increasing lipophilicity

Correct Answer: There is an optimal logP value at which activity is maximal

Q4. Free–Wilson analysis primarily differs from Hansch analysis because it:

  • Uses continuous physicochemical descriptors like sigma and pi instead of binary variables
  • Does not require experimental activity data
  • Represents substituent contributions using binary indicator variables for substituent identity and position
  • Is limited to 3D descriptors only

Correct Answer: Represents substituent contributions using binary indicator variables for substituent identity and position

Q5. In Free–Wilson analysis, a regression coefficient associated with a substituent indicator variable represents:

  • The exact pKa of the substituent
  • The average contribution of that substituent at that position to biological activity relative to a reference
  • The 3D conformational energy penalty introduced by the substituent
  • The lipophilicity of the whole molecule

Correct Answer: The average contribution of that substituent at that position to biological activity relative to a reference

Q6. Which 2D descriptor is a topological index representing molecular branching by using vertex degrees and paths?

  • LogP
  • Wiener index
  • Partial atomic charge
  • Molar refractivity

Correct Answer: Wiener index

Q7. In 2D QSAR, the molecular connectivity chi (χ) indices primarily capture information about:

  • Hydrogen-bond donors and acceptors only
  • Elemental isotopic composition
  • Atom connectivity and branching patterns
  • Three-dimensional molecular conformations

Correct Answer: Atom connectivity and branching patterns

Q8. Which validation metric is most appropriate to assess internal predictivity obtained via leave-one-out cross-validation?

  • q² (cross-validated r²)
  • LogP
  • VIF (variance inflation factor)
  • Partition coefficient

Correct Answer: q² (cross-validated r²)

Q9. Multicollinearity among descriptors in a regression QSAR model is commonly detected by which statistic?

  • Root mean square deviation (RMSD)
  • Variance inflation factor (VIF)
  • Cross-validated q²
  • Wiener index

Correct Answer: Variance inflation factor (VIF)

Q10. In a Hansch-type regression, a positive sigma (σ) electronic constant for a substituent suggests what about its electronic effect?

  • It is an electron-donating substituent (activating)
  • It is an electron-withdrawing substituent (deactivating)
  • It increases molecular lipophilicity
  • It increases steric hindrance

Correct Answer: It is an electron-withdrawing substituent (deactivating)

Q11. Which of the following is a common limitation of Free–Wilson analysis?

  • It requires accurate 3D structures for each compound
  • It assumes substituent effects are additive and independent, which may fail when interactions or non-additivity occur
  • It cannot handle binary substituents
  • It only models lipophilicity and ignores electronic effects

Correct Answer: It assumes substituent effects are additive and independent, which may fail when interactions or non-additivity occur

Q12. A 2D QSAR model shows excellent r² on training set but very low q² and poor external predictivity. The most likely problem is:

  • Appropriate descriptor selection
  • Overfitting to the training data
  • High experimental reproducibility of activities
  • Perfect model generalizability

Correct Answer: Overfitting to the training data

Q13. Which descriptor type is NOT typically part of 2D QSAR descriptor sets?

  • Constitutional descriptors (e.g., atom counts)
  • Topological indices (e.g., connectivity and path counts)
  • 3D electrostatic potential mapped on molecular surface
  • Functional group counts and fragment-based descriptors

Correct Answer: 3D electrostatic potential mapped on molecular surface

Q14. Y-scrambling (response permutation) is used in QSAR modeling primarily to:

  • Increase the number of descriptors available
  • Test whether model predictive power arises from chance correlation
  • Compute logP values more accurately
  • Improve model extrapolation beyond chemical space

Correct Answer: Test whether model predictive power arises from chance correlation

Q15. Partial least squares (PLS) regression is often used in 2D QSAR because it:

  • Requires no descriptors at all
  • Handles many correlated descriptors by projecting them into orthogonal latent variables
  • Guarantees a physically interpretable single-descriptor model
  • Is equivalent to simple linear regression for uncorrelated data

Correct Answer: Handles many correlated descriptors by projecting them into orthogonal latent variables

Q16. In a Hansch study, if the coefficient of the sigma (σ) term is significantly negative, this indicates that:

  • Electron-withdrawing substituents increase the biological activity
  • Electron-donating substituents increase the biological activity
  • Lipophilicity is irrelevant to activity
  • Steric bulk is the dominant factor

Correct Answer: Electron-donating substituents increase the biological activity

Q17. When constructing a Free–Wilson matrix for a homologous series varying substituents at two positions, the matrix rows represent:

  • Different descriptor calculation methods
  • Individual compounds and columns represent indicator variables for substituent presence at specific positions
  • Only the logP values for each compound
  • 3D conformers of each compound

Correct Answer: Individual compounds and columns represent indicator variables for substituent presence at specific positions

Q18. Which statistical parameter assesses the predictive performance of an external test set in QSAR?

  • q² (leave-one-out cross-validated correlation)
  • r² for the external test set (r²pred or r²ext)
  • Chi index
  • Atom count

Correct Answer: r² for the external test set (r²pred or r²ext)

Q19. The applicability domain of a QSAR model refers to:

  • The geographic region where the model was developed
  • The chemical space (descriptor range and similarity) where model predictions are considered reliable
  • The number of descriptors used in the model
  • The runtime required to compute descriptors

Correct Answer: The chemical space (descriptor range and similarity) where model predictions are considered reliable

Q20. Which descriptor selection method is commonly used to reduce descriptor dimensionality while retaining predictive power?

  • Stepwise regression, genetic algorithms and principal component analysis
  • Increasing the number of descriptors indefinitely
  • Removing experimental activity values
  • Randomly shuffling descriptor columns

Correct Answer: Stepwise regression, genetic algorithms and principal component analysis

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