Pharmacophore mapping and virtual screening workflows MCQs With Answer

Pharmacophore mapping and virtual screening workflows MCQs With Answer is a focused study aid designed for M.Pharm students taking MPC 203T Computer Aided Drug Design. This set of questions covers core principles of pharmacophore theory, both ligand- and receptor-based model generation, conformer sampling, feature definitions, validation metrics (AUC, enrichment), and practical virtual screening pipelines including decoy selection, scoring strategies, and ADMET prioritization. Questions emphasize deeper workflow decisions—tolerance settings, excluded volumes, consensus scoring, and integrating pharmacophores with docking—to strengthen conceptual understanding and decision-making for real screening campaigns. Each MCQ includes a clear correct answer to support study and exam preparation.

Q1. Which definition best describes a pharmacophore in the context of rational drug design?

• An abstract description of steric and electronic features necessary for optimal supramolecular interactions with a biological target
• A specific 2D chemical structure that guarantees biological activity
• A single lowest-energy conformation of a ligand bound to a protein
• A scoring function used in molecular docking

Correct Answer: An abstract description of steric and electronic features necessary for optimal supramolecular interactions with a biological target

Q2. What is the principal difference between ligand-based and structure-based pharmacophore modelling?

• Ligand-based derives features from known active ligands without requiring receptor coordinates, whereas structure-based derives features from the target binding site or receptor-ligand complex
• Structure-based cannot model hydrogen bond donors or acceptors
• Ligand-based always requires a co-crystallized ligand
• Structure-based uses only 2D information while ligand-based uses 3D information

Correct Answer: Ligand-based derives features from known active ligands without requiring receptor coordinates, whereas structure-based derives features from the target binding site or receptor-ligand complex

Q3. Which of the following is NOT a standard pharmacophore feature type used in most modelling tools?

• Hydrogen bond donor
• Aromatic ring
• Hydrophobic centroid
• Peptide bond

Correct Answer: Peptide bond

Q4. During pharmacophore hypothesis generation from multiple ligands, which step is critical to ensure features are comparable across molecules?

• Conformational sampling and 3D alignment of ligands
• Increasing the molecular weight of each ligand
• Converting all ligands to 2D SMILES strings
• Applying a single point energy minimization and discarding flexibility

Correct Answer: Conformational sampling and 3D alignment of ligands

Q5. Which validation metric is specifically useful to evaluate early recognition of actives in a virtual screening ranked list?

• Enrichment Factor (EF)
• Root Mean Square Deviation (RMSD)
• pKa
• Ligand Efficiency (LE)

Correct Answer: Enrichment Factor (EF)

Q6. How is ROC AUC interpreted when assessing a virtual screening model?

• An AUC of 0.5 indicates random performance while an AUC close to 1 indicates near-perfect discrimination between actives and inactives
• An AUC of 0.5 indicates perfect prediction and 1 indicates random
• AUC is unrelated to discrimination and measures docking score variance
• An AUC below 0.7 always implies a useful model for prospective screening

Correct Answer: An AUC of 0.5 indicates random performance while an AUC close to 1 indicates near-perfect discrimination between actives and inactives

Q7. What is the typical effect of increasing the tolerance radius around pharmacophore features during screening?

• Increases the number of matched molecules but may reduce specificity
• Decreases the number of matches and increases specificity
• Has no impact on hit rates
• Always eliminates false positives completely

Correct Answer: Increases the number of matched molecules but may reduce specificity

Q8. Which approach best describes receptor-based pharmacophore generation from a crystal structure?

• Identify interaction hotspots in the binding site (e.g., hydrogen-bonding acceptors/donors, hydrophobic pockets, metal coordination) often using a bound ligand or probe mapping
• Ignore the protein entirely and average ligand features in 2D
• Use only sequence alignment to predict features without structural input
• Derive features from calculated logP values of ligands

Correct Answer: Identify interaction hotspots in the binding site (e.g., hydrogen-bonding acceptors/donors, hydrophobic pockets, metal coordination) often using a bound ligand or probe mapping

Q9. Which sequence best represents a common pharmacophore-based virtual screening workflow?

• Database preparation → conformer generation → pharmacophore screening → ranking and post-filters
• Scoring → docking → database preparation → conformer generation
• Experimental testing → pharmacophore building → database preparation
• ADMET filtering → conformer generation → data mining → pharmacophore creation

Correct Answer: Database preparation → conformer generation → pharmacophore screening → ranking and post-filters

Q10. What is the purpose of using a decoy set when validating a pharmacophore or virtual screening protocol?

• To assess enrichment and specificity by comparing actives to property-matched inactive compounds
• To increase the number of true positive hits in prospective screening
• To train machine learning models only using actives
• To provide 2D structural motifs for pharmacophore generation

Correct Answer: To assess enrichment and specificity by comparing actives to property-matched inactive compounds

Q11. Which factor commonly leads to false positives in pharmacophore-based screening?

• Flexible molecules that can satisfy feature geometry but would sterically clash with the protein in the bound pose
• Using 3D conformer ensembles instead of single conformers
• Applying excluded volume constraints
• Including metal-binding features when the target has no metal

Correct Answer: Flexible molecules that can satisfy feature geometry but would sterically clash with the protein in the bound pose

Q12. What is the main advantage of combining pharmacophore filtering before molecular docking in a screening pipeline?

• Rapidly reduce library size to focus docking on likely binders, lowering computational cost and improving hit rates
• Completely replace the need for any docking or scoring
• Guarantee that remaining hits are non-toxic
• Reduce the need for conformer generation

Correct Answer: Rapidly reduce library size to focus docking on likely binders, lowering computational cost and improving hit rates

Q13. How does consensus scoring help in virtual screening campaigns?

• By combining results from multiple scoring functions or methods to reduce method-specific false positives and improve overall reliability
• By averaging molecular weights of top hits
• By increasing the number of docking poses per ligand without scoring
• By converting 3D pharmacophores into 2D fingerprints

Correct Answer: By combining results from multiple scoring functions or methods to reduce method-specific false positives and improve overall reliability

Q14. What is the purpose of defining excluded volumes in a receptor-based pharmacophore?

• To represent regions where ligand atoms cannot occupy due to protein steric hindrance and prevent unrealistic matches
• To indicate where additional hydrogen bonds must form
• To increase the tolerance of pharmacophore features
• To mark flexible residues for mutation

Correct Answer: To represent regions where ligand atoms cannot occupy due to protein steric hindrance and prevent unrealistic matches

Q15. What is the recommended typical number of pharmacophore features in a practical hypothesis used for virtual screening?

• 3 to 7 complementary features balancing specificity and hit-rate
• Exactly 12 features for best discrimination
• Only one feature to maximize diversity
• More than 20 features to ensure high specificity

Correct Answer: 3 to 7 complementary features balancing specificity and hit-rate

Q16. Which RMSD threshold is generally considered acceptable when comparing a predicted ligand pose to a known crystallographic pose?

• ≤2.0 Å
• ≥5.0 Å
• Between 3.5 and 4.5 Å
• Exactly 0 Å is required for validation

Correct Answer: ≤2.0 Å

Q17. How does fragment-based pharmacophore mapping help characterize binding sites?

• By using small probe fragments to map interaction hotspots and derive key pharmacophoric features for ligand growth or linking
• By excluding small molecular fragments from libraries to reduce noise
• By converting protein sequence to a single pharmacophore
• By increasing the conformational energy window to include unstable conformers

Correct Answer: By using small probe fragments to map interaction hotspots and derive key pharmacophoric features for ligand growth or linking

Q18. Which is a best practice for conformer generation prior to pharmacophore screening?

• Generate diverse low-energy conformers within an energy window (e.g., ≤10 kcal/mol) and limit the number per molecule to a practical maximum (e.g., ≤200)
• Generate only the single lowest-energy conformer for each molecule regardless of flexibility
• Ignore torsional sampling and use 2D coordinates for matching
• Only include conformers above 50 kcal/mol to capture extreme geometries

Correct Answer: Generate diverse low-energy conformers within an energy window (e.g., ≤10 kcal/mol) and limit the number per molecule to a practical maximum (e.g., ≤200)

Q19. At what stage in a virtual screening workflow is ADMET and physicochemical filtering most commonly applied?

• After ranking initial hits to prioritize compounds with desirable absorption, distribution, metabolism, excretion, and toxicity properties
• Only before any virtual screening steps, never after
• Immediately after decoy generation and before model validation
• ADMET filtering is never integrated into virtual screening workflows

Correct Answer: After ranking initial hits to prioritize compounds with desirable absorption, distribution, metabolism, excretion, and toxicity properties

Q20. What distinguishes prospective validation from retrospective validation of a pharmacophore model?

• Prospective validation tests predicted hits experimentally going forward, whereas retrospective validation evaluates model performance using known actives and decoys
• Prospective validation uses only in silico decoys, retrospective requires new synthesis
• Retrospective validation is always superior and replaces prospective testing
• There is no practical difference; both terms are interchangeable

Correct Answer: Prospective validation tests predicted hits experimentally going forward, whereas retrospective validation evaluates model performance using known actives and decoys

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