Virtual screening of compound libraries MCQs With Answer

Introduction: Virtual screening of compound libraries is an essential computational technique in modern drug discovery, enabling M. Pharm students to prioritize molecules before experimental testing. This blog presents focused multiple-choice questions on virtual screening concepts such as ligand- and structure-based approaches, molecular fingerprints, docking workflows, scoring functions, library preparation (tautomers, protonation), enrichment metrics, rescoring methods (MM-GBSA), and practical issues like receptor flexibility and PAINS filters. The set targets both theoretical understanding and practical considerations, helping students consolidate knowledge needed for designing, validating, and interpreting in silico screening campaigns. These MCQs emphasize deeper concepts and decision-making criteria rather than superficial definitions.

Q1. What is the primary goal of virtual screening in early drug discovery?

• To identify compounds with the highest synthetic yield
• To prioritize small molecules likely to bind a biological target
• To predict clinical trial outcomes
• To optimize large-scale manufacturing processes

Correct Answer: To prioritize small molecules likely to bind a biological target

Q2. Which two broad approaches classify most virtual screening workflows?

• High-throughput experimental screening and lead optimization
• Structure-based virtual screening and ligand-based virtual screening
• Fragment-based screening and ADMET profiling
• QSAR modeling and combinatorial chemistry

Correct Answer: Structure-based virtual screening and ligand-based virtual screening

Q3. Which molecular fingerprint is widely used for similarity searches in ligand-based virtual screening?

• Protein Data Bank (PDB) fingerprint
• Extended-Connectivity Fingerprint (ECFP)
• SHAKE fingerprint
• Force-field fingerprint

Correct Answer: Extended-Connectivity Fingerprint (ECFP)

Q4. Which similarity metric is most commonly used to compare binary molecular fingerprints?

• Euclidean distance
• Cosine similarity
• Tanimoto coefficient
• Pearson correlation

Correct Answer: Tanimoto coefficient

Q5. Which of the following is NOT a parameter in Lipinski’s Rule of Five?

• Molecular weight ≤ 500 Da
• LogP ≤ 5
• Topological polar surface area (TPSA) ≤ 140 Ų
• Hydrogen bond donors ≤ 5

Correct Answer: Topological polar surface area (TPSA) ≤ 140 Ų

Q6. What is the main purpose of applying PAINS filters to a screening library?

• To remove compounds with poor predicted solubility only
• To eliminate molecules likely to produce assay artifacts and false positives
• To prioritize highly lipophilic compounds
• To select compounds with ideal molecular weight for oral drugs

Correct Answer: To eliminate molecules likely to produce assay artifacts and false positives

Q7. Why are decoy molecules used when validating a virtual screening protocol?

• To serve as alternative drug candidates for clinical trials
• To benchmark enrichment and discriminate true actives from random compounds
• To reduce computational cost by replacing actives
• To increase library chemical diversity

Correct Answer: To benchmark enrichment and discriminate true actives from random compounds

Q8. Which of the following are common categories of docking scoring functions?

• Empirical, knowledge-based, and force-field based
• Chromatographic, spectrometric, and calorimetric
• Thermodynamic, kinetic, and stoichiometric
• Genomic, proteomic, and metabolomic

Correct Answer: Empirical, knowledge-based, and force-field based

Q9. Which software listed is NOT primarily designed for molecular docking?

• AutoDock Vina
• Glide
• GOLD
• GROMACS

Correct Answer: GROMACS

Q10. In grid-based docking, what is the purpose of generating a grid around the binding site?

• To model ligand solubility in aqueous solution
• To precompute receptor interaction potentials for rapid scoring
• To store experimental assay data
• To generate conformers of the ligand

Correct Answer: To precompute receptor interaction potentials for rapid scoring

Q11. How does ensemble docking help address receptor flexibility?

• By docking ligands only into the apo structure of a protein
• By averaging ligand conformations before docking
• By docking ligands against multiple receptor conformations derived from experiment or simulation
• By using only rigid-body docking with larger grids

Correct Answer: By docking ligands against multiple receptor conformations derived from experiment or simulation

Q12. What is the main advantage of consensus scoring in virtual screening?

• It reduces the need for ligand preparation
• It increases hit-rate reliability by combining multiple scoring functions
• It guarantees identification of true binders with 100% accuracy
• It replaces the need for experimental validation

Correct Answer: It increases hit-rate reliability by combining multiple scoring functions

Q13. Why must protonation states and tautomers be considered when preparing a compound library for docking?

• They do not affect docking and can be ignored
• They influence predicted binding poses, interaction patterns, and scoring
• They only affect compound solubility in organic solvents
• They determine the synthetic route of the compound

Correct Answer: They influence predicted binding poses, interaction patterns, and scoring

Q14. In virtual high-throughput screening (vHTS), which factor most commonly limits the practical success of hit identification?

• The number of rotatable bonds in ligands
• The accuracy of scoring functions to discriminate true binders
• The color of the graphical user interface
• The availability of commercial vendors

Correct Answer: The accuracy of scoring functions to discriminate true binders

Q15. What does the enrichment factor (EF) quantify in virtual screening validation?

• The absolute number of docking poses generated
• The fold increase in actives found among top-ranked compounds compared with random selection
• The computational time required per compound
• The molecular weight distribution of hits

Correct Answer: The fold increase in actives found among top-ranked compounds compared with random selection

Q16. Which area-under-curve (AUC) value for a ROC plot indicates discrimination better than random but not perfect?

• AUC = 0.5
• AUC = 0.2
• AUC = 0.95
• AUC = 0.75

Correct Answer: AUC = 0.75

Q17. Why is MM-GBSA (or MM-PBSA) rescoring applied after docking?

• To replace docking completely with quantum mechanics calculations
• To obtain a more physically detailed estimate of binding free energy for prioritized poses
• To speed up initial docking by approximating ligand flexibility
• To compute only ligand solubility parameters

Correct Answer: To obtain a more physically detailed estimate of binding free energy for prioritized poses

Q18. What does a pharmacophore model represent in ligand-based virtual screening?

• The 2D chemical synthesis route for a compound
• The 3D arrangement of key steric and electronic features necessary for target binding
• The full protein sequence of the target
• The chromatographic retention time of ligands

Correct Answer: The 3D arrangement of key steric and electronic features necessary for target binding

Q19. What is a primary advantage of fragment-based virtual screening compared with screening large drug-like molecules?

• Fragments always have higher affinity than full-size ligands
• Fragments sample chemical space more efficiently and can be grown or linked into higher-affinity leads
• Fragment screening eliminates the need for any experimental follow-up
• Fragments are not subject to ADMET issues

Correct Answer: Fragments sample chemical space more efficiently and can be grown or linked into higher-affinity leads

Q20. Which common cause often leads to false positive hits from virtual screening?

• Perfectly accurate scoring functions
• Inaccurate scoring functions and imperfect receptor models that mis-rank non-binders as top hits
• Use of multiple receptor conformations in ensemble docking
• Application of PAINS filters to remove problematic chemotypes

Correct Answer: Inaccurate scoring functions and imperfect receptor models that mis-rank non-binders as top hits

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