Pharmacophore-based screening MCQs With Answer

Pharmacophore-based screening MCQs With Answer offers B. Pharm students a concise, practical way to master pharmacophore modeling and virtual screening principles. It covers 3D pharmacophore concepts and essential features — hydrogen-bond donors and acceptors, aromatic rings, hydrophobic centers, positive/negative ionizable groups, and spatial constraints. Topics include pharmacophore hypothesis generation, feature mapping, ligand alignment, screening compound libraries, enrichment metrics, validation techniques, and scoring functions. Emphasis is placed on interpreting pharmacophore models in structure–activity relationships, hit identification, and lead optimization. These focused MCQs reinforce concepts used in ligand-based drug design, pharmacophore fingerprints, and computational screening workflows. Now let’s test your knowledge with 30 MCQs on this topic.

Q1. What is a pharmacophore?

• A three-dimensional arrangement of features necessary for biological activity
• The complete chemical structure of a drug molecule
• An experimental assay used to measure potency
• A database of marketed drugs

Correct Answer: A three-dimensional arrangement of features necessary for biological activity

Q2. Which feature is commonly included in a pharmacophore model?

• Hydrogen-bond donor
• Mass spectrometry peak
• Melting point
• pH of formulation

Correct Answer: Hydrogen-bond donor

Q3. Pharmacophore-based screening is primarily categorized under which drug-design approach?

• Ligand-based drug design
• Receptor-based docking only
• Experimental high-throughput screening
• Formulation development

Correct Answer: Ligand-based drug design

Q4. What is the role of exclusion volumes in pharmacophore models?

• To represent sterically forbidden regions of the receptor
• To increase the number of hits in screening
• To define the solvent properties
• To indicate metabolic hotspots

Correct Answer: To represent sterically forbidden regions of the receptor

Q5. Which metric assesses how well actives are prioritized over decoys in screening?

• Enrichment factor (EF)
• Partition coefficient (log P)
• pKa value
• Melting temperature (Tm)

Correct Answer: Enrichment factor (EF)

Q6. In pharmacophore hypothesis generation, what does alignment refer to?

• Superimposing ligands to match common pharmacophoric features
• Measuring solubility across pH
• Comparing synthetic routes
• Arranging compounds by molecular weight

Correct Answer: Superimposing ligands to match common pharmacophoric features

Q7. Which software task is essential before virtual screening with a pharmacophore?

• Generating conformer ensembles of library molecules
• Performing wet-lab binding assays
• Running dissolution studies
• Measuring in vivo toxicity

Correct Answer: Generating conformer ensembles of library molecules

Q8. A 3D pharmacophore feature labeled “AR” typically stands for:

• Aromatic ring
• Acidic residue
• Aliphatic region
• Active rotamer

Correct Answer: Aromatic ring

Q9. Which of the following improves pharmacophore model specificity?

• Adding more discriminating features and exclusion volumes
• Increasing library size without constraints
• Removing hydrogen-bond features
• Allowing unlimited feature tolerances

Correct Answer: Adding more discriminating features and exclusion volumes

Q10. What is a pharmacophore fingerprint used for?

• Rapid similarity searching and virtual screening
• Measuring pH-dependent solubility
• Predicting in vivo clearance directly
• Estimating synthetic yield

Correct Answer: Rapid similarity searching and virtual screening

Q11. Which validation method evaluates model performance using known actives and decoys?

• Receiver operating characteristic (ROC) curve analysis
• Determination of partition coefficient (log P)
• NMR structure elucidation
• Accelerated stability testing

Correct Answer: Receiver operating characteristic (ROC) curve analysis

Q12. In pharmacophore mapping, feature tolerance refers to:

• The allowed distance variation around a feature
• The acceptable lab temperature during experiments
• The permitted ionic strength of buffers
• The range of pKa values of ligands

Correct Answer: The allowed distance variation around a feature

Q13. Which pharmacophore feature best describes a positively charged center?

• Positive ionizable (PI)
• Hydrophobic center (HY)
• Aromatic ring (AR)
• Negative ionizable (NI)

Correct Answer: Positive ionizable (PI)

Q14. When generating a pharmacophore from multiple ligands, a conserved feature indicates:

• A common interaction essential for activity
• A property irrelevant to binding
• Only a synthetic artifact
• A solubility-related moiety

Correct Answer: A common interaction essential for activity

Q15. Which factor can produce false positives in pharmacophore screening?

• Over-permissive feature tolerances
• Using experimentally validated actives
• Including exclusion volumes
• Using diverse decoy sets

Correct Answer: Over-permissive feature tolerances

Q16. What is a common source of pharmacophore hypotheses besides ligand alignment?

• Receptor-based interaction mapping from crystal structures
• Measuring melting points of compounds
• Random library selection
• Synthetic route optimization

Correct Answer: Receptor-based interaction mapping from crystal structures

Q17. Which statement about conformer generation is true?

• Accurate conformer ensembles are crucial for matching 3D pharmacophoric geometry
• Only the lowest-energy conformer is ever used
• Conformer generation is irrelevant for rigid molecules
• High-energy conformers should be preferred for screening

Correct Answer: Accurate conformer ensembles are crucial for matching 3D pharmacophoric geometry

Q18. The term “hit-to-lead” in the context of pharmacophore screening means:

• Optimizing initial screening hits into more potent, selective leads
• Measuring dissolution rates of hits
• Registering a compound as a drug
• Scaling up synthesis without biological evaluation

Correct Answer: Optimizing initial screening hits into more potent, selective leads

Q19. Which is a common disadvantage of ligand-based pharmacophore screening?

• Requires known active ligands and may miss novel chemotypes
• Always predicts ADME properties accurately
• Does not need any computational resources
• Provides exact binding poses in the receptor

Correct Answer: Requires known active ligands and may miss novel chemotypes

Q20. In enrichment calculations, an EF1% value measures:

• The fold-enrichment of actives within the top 1% of the screened library
• The solubility at pH 1%
• The fraction of decoys in the whole database
• The pKa shift upon binding

Correct Answer: The fold-enrichment of actives within the top 1% of the screened library

Q21. Which feature pairing is most relevant for hydrogen bonding in a pharmacophore?

• Hydrogen-bond donor with hydrogen-bond acceptor
• Hydrophobic with aromatic
• Positive ionizable with hydrophobic
• Exclusion volume with log P

Correct Answer: Hydrogen-bond donor with hydrogen-bond acceptor

Q22. What does a pharmacophore scoring function typically evaluate?

• How well a ligand matches required features and geometry
• The exact metabolic pathway of the ligand
• The cost of synthesis
• The melting point of the ligand

Correct Answer: How well a ligand matches required features and geometry

Q23. Which is a best practice when building a pharmacophore model?

• Use diverse active ligands and validate with external test sets
• Include only a single ligand to avoid complexity
• Ignore experimental SAR data
• Never include exclusion volumes

Correct Answer: Use diverse active ligands and validate with external test sets

Q24. Pharmacophore-based virtual screening is most complementary to which technique?

• Molecular docking for pose and receptor context
• Wet-lab tablet compression testing
• Clinical trial patient recruitment
• Solubility at different pH

Correct Answer: Molecular docking for pose and receptor context

Q25. Why are decoy sets used in validation of pharmacophore models?

• To test model ability to discriminate actives from similar-size inactives
• To determine compound melting points
• To replace actives in SAR studies
• To measure synthetic feasibility

Correct Answer: To test model ability to discriminate actives from similar-size inactives

Q26. A pharmacophore hypothesis with many optional features is likely to:

• Increase hit rate but reduce specificity
• Always find only true actives
• Reduce library diversity
• Improve experimental solubility

Correct Answer: Increase hit rate but reduce specificity

Q27. Which descriptor is least relevant when interpreting pharmacophore matches?

• Melting point
• Feature match count
• Root-mean-square deviation (RMSD) of matched features
• Pharmacophore fit score

Correct Answer: Melting point

Q28. How does including receptor information improve pharmacophore models?

• By providing precise interaction geometry and exclusion regions
• By increasing molecular weight of hits
• By guaranteeing clinical success
• By speeding up synthesis

Correct Answer: By providing precise interaction geometry and exclusion regions

Q29. Which practice helps reduce false negatives in pharmacophore screening?

• Allowing reasonable feature tolerances and diverse conformers
• Using only a single rigid conformer per ligand
• Removing all hydrophobic features
• Screening at extremely strict tolerances only

Correct Answer: Allowing reasonable feature tolerances and diverse conformers

Q30. Which outcome indicates a good pharmacophore model during retrospective validation?

• High AUC in ROC analysis and significant enrichment of actives
• Low number of total hits regardless of actives
• High computational time with poor discrimination
• Random distribution of actives throughout ranked list

Correct Answer: High AUC in ROC analysis and significant enrichment of actives

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