Pharmacophore modeling – concept and tools MCQs With Answer

Pharmacophore modeling – concept and tools MCQs With Answer

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Introduction: Pharmacophore modeling is a core concept in modern computer-aided drug design that captures the essential 3D arrangement of chemical features required for biological activity. B. Pharm students should understand ligand‑based and structure‑based pharmacophore approaches, common features (hydrogen‑bond donors/acceptors, hydrophobes, aromatic rings, ionizable centers), conformer generation, alignment, and virtual screening workflows. Popular tools include LigandScout, Phase, MOE, Discovery Studio, Pharmit, ZINCPharmer and PharmaGist, as well as open‑source libraries like RDKit and OpenPharmacophore. Mastery of model validation (ROC, enrichment, decoys) and pitfalls such as overfitting is vital for hit identification and lead optimization. Now let’s test your knowledge with 30 MCQs on this topic.

Q1. What is a pharmacophore?

  • A 2D chemical structure of the most active ligand
  • The 3D arrangement of steric and electronic features necessary for biological activity
  • An algorithm for docking ligands into proteins
  • A protein active site sequence motif

Correct Answer: The 3D arrangement of steric and electronic features necessary for biological activity

Q2. Which feature is NOT typically included in a pharmacophore model?

  • Hydrogen-bond donor
  • Hydrophobic region
  • Metabolic half-life
  • Aromatic ring

Correct Answer: Metabolic half-life

Q3. Ligand-based pharmacophore modeling primarily requires:

  • Only the 3D structure of the protein target
  • Multiple active ligands and their alignments
  • High-throughput assay data without chemical structures
  • Crystal structure of the ligand–protein complex only

Correct Answer: Multiple active ligands and their alignments

Q4. Structure-based pharmacophore modeling is most directly derived from:

  • Pharmacokinetic predictions
  • Protein–ligand complex 3D structures (e.g., X-ray or cryo-EM)
  • Dose–response curves alone
  • 2D similarity clustering of ligands

Correct Answer: Protein–ligand complex 3D structures (e.g., X-ray or cryo-EM)

Q5. Which of the following is a common hydrogen-bond feature classification in pharmacophore models?

  • Donor and acceptor
  • Neutral and radical
  • Aliphatic and aromatic
  • Hydrophilic and hydrophobic only

Correct Answer: Donor and acceptor

Q6. Why is conformer generation important in pharmacophore modeling?

  • It identifies the most polar tautomer only
  • It explores possible 3D shapes ligands can adopt to match the pharmacophore
  • It calculates binding free energy exactly
  • It eliminates the need for validation

Correct Answer: It explores possible 3D shapes ligands can adopt to match the pharmacophore

Q7. Which validation metric measures the true positive rate versus false positive rate for a pharmacophore virtual screen?

  • Enrichment factor
  • ROC (receiver operating characteristic) curve / AUC
  • Root-mean-square deviation (RMSD)
  • LogP

Correct Answer: ROC (receiver operating characteristic) curve / AUC

Q8. Enrichment factor (EF) in virtual screening indicates:

  • How much better the model is at retrieving actives compared to random selection
  • The number of rotatable bonds in a ligand
  • The melting temperature of the target protein
  • The pharmacokinetic half-life of hits

Correct Answer: How much better the model is at retrieving actives compared to random selection

Q9. Which software is a widely used commercial pharmacophore tool from Schrödinger?

  • LigandScout
  • Phase
  • Pharmit
  • PharmaGist

Correct Answer: Phase

Q10. LigandScout primarily helps users to:

  • Predict ADME/Tox properties only
  • Automatically derive 3D pharmacophores from ligand–protein complexes
  • Perform molecular dynamics simulations exclusively
  • Synthesize compounds virtually

Correct Answer: Automatically derive 3D pharmacophores from ligand–protein complexes

Q11. Which of these web tools allows interactive pharmacophore-based virtual screening against ZINC libraries?

  • ZINCPharmer
  • MOE
  • Discovery Studio
  • Phase

Correct Answer: ZINCPharmer

Q12. Overfitting a pharmacophore model typically results in:

  • Improved generalization to diverse actives
  • Very high performance on training ligands but poor screening performance on new compounds
  • Faster virtual screening times always
  • More accurate ADMET predictions

Correct Answer: Very high performance on training ligands but poor screening performance on new compounds

Q13. Which pharmacophore feature represents positively charged or basic centers?

  • Hydrophobic feature
  • Positive ionizable (cationic) feature
  • Aromatic ring feature
  • Hydrogen-bond acceptor feature

Correct Answer: Positive ionizable (cationic) feature

Q14. Pharmit is best described as:

  • A proprietary docking engine for GPU clusters
  • An open web-based platform for pharmacophore and shape-based screening
  • Only a 2D chemical drawing tool
  • A wet‑lab protocol repository

Correct Answer: An open web-based platform for pharmacophore and shape-based screening

Q15. Which step is NOT part of typical pharmacophore model building?

  • Selection/alignment of active ligands
  • Identification of common features and spatial constraints
  • Experimental measurement of binding kinetics for each database molecule
  • Validation using decoys and enrichment analysis

Correct Answer: Experimental measurement of binding kinetics for each database molecule

Q16. What is a “decoy” set used for in pharmacophore validation?

  • Known inactive compounds used to test selectivity of screening
  • Highly active compounds used to bias the model
  • A set of protein sequences for homology modeling
  • Chemical reactions for synthesizing hits

Correct Answer: Known inactive compounds used to test selectivity of screening

Q17. Which open-source cheminformatics toolkit can be used to generate pharmacophore features programmatically?

  • RDKit
  • MS Excel
  • Adobe Photoshop
  • GraphPad Prism

Correct Answer: RDKit

Q18. In structure-based pharmacophore extraction, metal coordination sites are represented as:

  • Hydrophobic features only
  • Specific metal-binding features or constraints
  • Always ignored because metals are rare
  • As aromatic features

Correct Answer: Specific metal-binding features or constraints

Q19. Which is a common cause of false positives in pharmacophore virtual screening?

  • Using diverse training actives
  • Overly permissive feature tolerances and ignoring steric clashes
  • Including protein structure information
  • Performing conformer sampling

Correct Answer: Overly permissive feature tolerances and ignoring steric clashes

Q20. Which of the following best improves pharmacophore model specificity?

  • Removing steric exclusion spheres where the protein blocks ligand atoms
  • Adding excluded volumes derived from the protein to prevent steric clashes
  • Allowing unlimited rotatable bonds during screening
  • Using only 2D fingerprints for matching

Correct Answer: Adding excluded volumes derived from the protein to prevent steric clashes

Q21. How does a 3D-pharmacophore differ from a 2D pharmacophore?

  • 3D includes spatial distances and angles between features; 2D captures only feature types without geometry
  • 3D is always less accurate than 2D
  • 2D requires protein structure, 3D does not
  • They are identical concepts

Correct Answer: 3D includes spatial distances and angles between features; 2D captures only feature types without geometry

Q22. Which descriptor is often used to compare how well a ligand matches a pharmacophore?

  • Match score or fitness score
  • pKa value
  • Experimental IC50 only
  • UV absorbance

Correct Answer: Match score or fitness score

Q23. PharmaGist is primarily used for:

  • Mining common pharmacophoric patterns from a set of ligands via a web server
  • High-throughput synthesis planning
  • Protein homology modeling
  • In vivo toxicity testing

Correct Answer: Mining common pharmacophoric patterns from a set of ligands via a web server

Q24. What role do excluded volumes play in a pharmacophore model?

  • They represent allowed hydrophilic regions
  • They denote regions where ligand atoms should not be placed due to protein steric hindrance
  • They are equivalent to hydrogen-bond donors
  • They increase the number of hits by relaxing constraints

Correct Answer: They denote regions where ligand atoms should not be placed due to protein steric hindrance

Q25. Which approach is preferred when no target structure is available?

  • Structure-based pharmacophore from crystal structure
  • Ligand‑based pharmacophore modeling using known actives
  • Molecular dynamics of the apo protein exclusively
  • Only wet‑lab high-throughput screening without in silico work

Correct Answer: Ligand‑based pharmacophore modeling using known actives

Q26. Which factor is most critical when selecting ligands to build a ligand‑based pharmacophore?

  • They must all be identical in size and scaffold
  • They should be structurally diverse but share common activity and binding mode
  • They should all be inactive compounds
  • They should lack any hydrogen-bonding groups

Correct Answer: They should be structurally diverse but share common activity and binding mode

Q27. Which commercial package integrates pharmacophore modeling with other drug design modules and is frequently used in industry?

  • Discovery Studio
  • PaintShop Pro
  • Microsoft Word
  • AutoDock Vina only

Correct Answer: Discovery Studio

Q28. What is the main advantage of combining pharmacophore screening with shape-based filters?

  • It slows down screening without improving results
  • It increases both chemical feature and steric complementarity, improving hit quality
  • It removes the need for conformer generation
  • It converts 3D models to 1D fingerprints

Correct Answer: It increases both chemical feature and steric complementarity, improving hit quality

Q29. Which of the following best describes consensus pharmacophore modeling?

  • Combining multiple pharmacophore models or features to capture shared interactions across diverse ligands
  • Using only a single ligand to derive the model
  • Ignoring experimental activities when building models
  • Using pharmacophores to predict protein tertiary structure

Correct Answer: Combining multiple pharmacophore models or features to capture shared interactions across diverse ligands

Q30. When reporting a pharmacophore-based virtual screening study, which information is essential for reproducibility?

  • Only the number of hits found
  • Detailed description of features, tolerances, conformer settings, database used, and validation metrics
  • The color scheme used for visualizations
  • Only the names of software used without parameters

Correct Answer: Detailed description of features, tolerances, conformer settings, database used, and validation metrics

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