Virtual screening: drug-likeness and pharmacophore screening MCQs With Answer

Introduction: Virtual screening is a cornerstone of modern drug discovery, enabling M.Pharm students to prioritize compounds efficiently before experimental testing. This collection of MCQs focuses on drug-likeness rules and pharmacophore-based screening — two complementary approaches used to filter compound libraries and identify promising hits. Questions cover physicochemical property thresholds (Lipinski, Veber, Ghose, Egan, Muegge), PAINS and decoy strategies, conformer generation, and pharmacophore model construction, validation, and application. The set emphasizes conceptual understanding and practical considerations for screening workflows, scoring metrics, and common pitfalls, preparing students for both theoretical exams and real-world computational projects in lead identification and optimization.

Q1. Which of the following is the correct set of criteria defined by Lipinski’s Rule of Five for oral drug-likeness?

• Molecular weight ≤500, LogP ≤5, H-bond donors ≤5, H-bond acceptors ≤10
• Molecular weight ≤400, LogP ≤4, H-bond donors ≤3, H-bond acceptors ≤8
• Molecular weight ≤600, LogP ≤6, H-bond donors ≤10, H-bond acceptors ≤12
• Molecular weight ≤350, LogP ≤3, H-bond donors ≤2, H-bond acceptors ≤6

Correct Answer: Molecular weight ≤500, LogP ≤5, H-bond donors ≤5, H-bond acceptors ≤10

Q2. Which two descriptors form the core of Veber’s rule for predicting oral bioavailability?

• Number of rotatable bonds and topological polar surface area (TPSA)
• LogP and molecular refractivity
• Number of aromatic rings and pKa
• Molar volume and solubility

Correct Answer: Number of rotatable bonds and topological polar surface area (TPSA)

Q3. The Ghose filter for drug-likeness specifies which typical molecular weight range?

• 160–480 Da
• 50–200 Da
• 400–700 Da
• 500–1000 Da

Correct Answer: 160–480 Da

Q4. What is the main purpose of applying PAINS (Pan Assay INterference compoundS) filters during virtual screening?

• To remove compounds likely to produce false-positive biological assay results by non-specific mechanisms
• To prioritize compounds with high membrane permeability
• To identify compounds with optimal pKa values for oral absorption
• To select molecules with the highest synthetic accessibility scores

Correct Answer: To remove compounds likely to produce false-positive biological assay results by non-specific mechanisms

Q5. In pharmacophore modeling, which of the following is NOT a common pharmacophoric feature?

• Hydrogen bond donor
• Aromatic ring
• Hydrophobic centroid
• Optical rotation

Correct Answer: Optical rotation

Q6. Which statement best distinguishes ligand-based from structure-based pharmacophore modeling?

• Ligand-based models derive consensus features from aligned active molecules; structure-based models derive interaction features from the receptor binding site.
• Ligand-based models always require X-ray structures; structure-based models require only 2D structures.
• Ligand-based models map solvent interactions; structure-based models ignore solvent effects completely.
• Ligand-based models are used only for peptide ligands; structure-based models are used only for small molecules.

Correct Answer: Ligand-based models derive consensus features from aligned active molecules; structure-based models derive interaction features from the receptor binding site.

Q7. What is the typical role of “excluded volumes” in a receptor-based pharmacophore?

• To represent sterically forbidden regions where ligand atoms cannot be placed
• To indicate regions of high solubility
• To increase the number of matched pharmacophore hits artificially
• To measure compound lipophilicity within the pocket

Correct Answer: To represent sterically forbidden regions where ligand atoms cannot be placed

Q8. Why is conformer generation a critical step before pharmacophore screening?

• Because realistic 3D conformations are needed to map pharmacophore features and match spatial arrangements
• Because 2D structures have higher docking scores than 3D conformers
• Because conformer generation eliminates all rotatable bonds from molecules
• Because conformer generation directly predicts ADMET properties

Correct Answer: Because realistic 3D conformations are needed to map pharmacophore features and match spatial arrangements

Q9. Which benchmarking metric measures the ability of a screening method to prioritize active compounds early in a ranked list?

• Enrichment factor (EF)
• LogP
• Molar refractivity
• pKa

Correct Answer: Enrichment factor (EF)

Q10. DUD-E and DEKOIS are examples of what resource commonly used in virtual screening validation?

• Decoy sets and benchmark datasets
• Commercial docking engines
• Experimental solubility assays
• Fragment libraries for SAR

Correct Answer: Decoy sets and benchmark datasets

Q11. Which descriptor is commonly limited in Egan’s eggplot filter for predicting oral absorption?

• Topological polar surface area (TPSA) and logP (with TPSA ≤ ~131.6 and logP ≤ ~5.88)
• Number of chiral centers only
• Number of rotatable bonds > 30
• Number of aromatic heterocycles only

Correct Answer: Topological polar surface area (TPSA) and logP (with TPSA ≤ ~131.6 and logP ≤ ~5.88)

Q12. Which of the following best describes ligand efficiency (LE)?

• Binding energy (or potency) normalized per heavy atom of the ligand
• Absolute logP of the molecule
• Number of hydrogen bonds formed divided by molecular weight
• Solubility per rotatable bond

Correct Answer: Binding energy (or potency) normalized per heavy atom of the ligand

Q13. In pharmacophore feature matching, what is the purpose of a tolerance radius?

• To allow spatial flexibility so feature centers can deviate within an acceptable distance
• To define the pKa range accepted for a feature
• To restrict matching only to planar aromatic features
• To calculate logP for matched ligands

Correct Answer: To allow spatial flexibility so feature centers can deviate within an acceptable distance

Q14. Which software is widely used specifically for automated pharmacophore model generation and virtual screening?

• LigandScout
• Gaussian09
• NMRPipe
• Excel

Correct Answer: LigandScout

Q15. Which validation metric provides an overall measure of discrimination between actives and inactives across the whole ranked list?

• Area under the ROC curve (AUC)
• Topological polar surface area (TPSA)
• Number of rotatable bonds
• Molar refractivity

Correct Answer: Area under the ROC curve (AUC)

Q16. When constructing a pharmacophore from multiple active ligands, which approach helps identify common interaction patterns?

• Alignment of active ligands followed by consensus feature extraction
• Random selection of atom coordinates from each ligand
• Excluding all hydrogen bond features to focus on hydrophobicity only
• Using solely 2D fingerprints without 3D data

Correct Answer: Alignment of active ligands followed by consensus feature extraction

Q17. Which of the following is a common pitfall when using strict drug-likeness filters early in virtual screening?

• Potentially excluding novel chemotypes or larger biologically relevant molecules that could be optimized
• Automatically improving the accuracy of docking scores
• Guaranteeing perfect ADMET properties for all retained hits
• Eliminating the need for experimental testing

Correct Answer: Potentially excluding novel chemotypes or larger biologically relevant molecules that could be optimized

Q18. What is the main advantage of combining shape-based screening with pharmacophore filters?

• It improves both geometric complementarity and chemical feature matching, increasing hit quality
• It removes the need for any conformer sampling
• It ensures 100% specificity in biological assays
• It decreases computational cost to zero

Correct Answer: It improves both geometric complementarity and chemical feature matching, increasing hit quality

Q19. Which practice is recommended to reduce false positives in virtual screening hit lists?

• Apply PAINS and Brenk filters, use decoy benchmarking, and perform orthogonal rescoring or docking
• Only retain compounds with molecular weight >1000 Da
• Always prioritize the highest logP compounds
• Exclude all molecules containing heteroatoms

Correct Answer: Apply PAINS and Brenk filters, use decoy benchmarking, and perform orthogonal rescoring or docking

Q20. For pharmacophore-based virtual screening, which step most improves the likelihood that hits will be experimentally active?

• Combining validated pharmacophore hypotheses with property filters (e.g., drug-likeness), diverse conformer ensembles and rescoring strategies
• Using only 2D similarity to a single active compound without 3D checks
• Filtering solely by high molecular weight and lipophilicity
• Rejecting any compound without an X-ray co-crystal structure

Correct Answer: Combining validated pharmacophore hypotheses with property filters (e.g., drug-likeness), diverse conformer ensembles and rescoring strategies

Download