In silico lead discovery techniques and virtual screening MCQs With Answer

Introduction: In silico lead discovery and virtual screening are essential components of modern drug discovery, allowing M.Pharm students to prioritize chemical matter efficiently. This blog focuses on computational techniques — including ligand‑ and structure‑based virtual screening, docking, scoring functions, pharmacophore modeling, homology modeling, fragment‑based approaches, and machine‑learning‑driven methods — that accelerate hit identification and early lead optimization. Emphasis is placed on practical concepts such as library design, decoy selection, validation metrics (ROC AUC, enrichment), ADMET filters and rescoring (MM‑GBSA). Understanding these tools helps you design robust screening workflows, interpret results critically, and reduce time and cost in preclinical research.

Q1. What fundamentally distinguishes ligand‑based virtual screening from structure‑based virtual screening?

• Ligand‑based uses known active ligands to model activity while structure‑based uses the target’s 3D structure for docking.
• Ligand‑based always requires a high resolution crystal structure while structure‑based does not.
• Ligand‑based predicts protein folding, structure‑based predicts ligand ADMET.
• There is no difference; both use the same algorithms and inputs.

Correct Answer: Ligand‑based uses known active ligands to model activity while structure‑based uses the target’s 3D structure for docking.

Q2. Which of the following lists the major categories of scoring functions used in molecular docking?

• Force‑field, empirical and knowledge‑based scoring functions.
• Thermodynamic, kinetic and genomic scoring functions.
• Hydropathic, electrostatic and van der Waals scoring only.
• QSAR, PCA and clustering scoring functions.

Correct Answer: Force‑field, empirical and knowledge‑based scoring functions.

Q3. What does the enrichment factor (EF) measure in virtual screening performance?

• Measure of how many actives are retrieved in top‑ranked fraction compared to random selection.
• Absolute binding free energy in kcal/mol for the top hit.
• Average molecular weight of compounds in the top 1% of the library.
• Percentage of decoys incorrectly predicted as actives.

Correct Answer: Measure of how many actives are retrieved in top‑ranked fraction compared to random selection.

Q4. How is ROC AUC interpreted when evaluating a virtual screening experiment?

• Area under the ROC curve; 0.5 indicates random performance while 1.0 indicates perfect discrimination between actives and inactives.
• Area under the ROC curve; values above 2 indicate excellent model performance.
• ROC AUC measures ligand solubility prediction accuracy.
• ROC AUC is only meaningful for regression models, not classification.

Correct Answer: Area under the ROC curve; 0.5 indicates random performance while 1.0 indicates perfect discrimination between actives and inactives.

Q5. What is the primary goal of pharmacophore modeling in virtual screening?

• Identify a spatial arrangement of essential steric and electronic features required for biological activity and use it to screen libraries.
• Predict toxic metabolites by simulating metabolic enzymes.
• Compute exact binding free energies for ligand‑protein complexes.
• Generate 3D protein structures from scratch without templates.

Correct Answer: Identify a spatial arrangement of essential steric and electronic features required for biological activity and use it to screen libraries.

Q6. Why are decoy sets (e.g., DUD‑E) used in benchmarking virtual screening methods?

• Decoys are inactive compounds matched for physical properties to act as negative controls to assess virtual screening performance.
• Decoys are additional actives used to inflate enrichment statistics.
• Decoys are chemical fragments used only in fragment‑based screening.
• Decoys are used to predict ADMET properties of hits.

Correct Answer: Decoys are inactive compounds matched for physical properties to act as negative controls to assess virtual screening performance.

Q7. When is homology modeling typically applied in an in silico lead discovery workflow?

• When no experimental 3D structure of the target protein is available, using a related template structure to build a model.
• Only when a high‑resolution ligand structure needs to be determined by X‑ray crystallography.
• To generate conformers of small molecules for ligand‑based screening.
• To directly compute binding free energies without docking.

Correct Answer: When no experimental 3D structure of the target protein is available, using a related template structure to build a model.

Q8. What does induced‑fit docking account for that rigid docking does not?

• Protein side‑chain and backbone flexibility allowing ligand‑driven conformational changes in the binding site.
• Only the quantum tunneling effects during ligand binding.
• Explicit simulation of enzymatic turnover and covalent catalysis.
• Solubility and pKa shifts of the ligand in blood plasma.

Correct Answer: Protein side‑chain and backbone flexibility allowing ligand‑driven conformational changes in the binding site.

Q9. What is the main purpose of applying MM‑GBSA or MM‑PBSA rescoring after docking?

• Provide an improved estimation of binding free energy by combining molecular mechanics with implicit solvent models to refine ranked hits.
• Automatically generate pharmacophores from docked poses.
• Replace docking entirely to obtain faster screening of large libraries.
• Calculate the synthetic route and retrosynthetic analysis for hits.

Correct Answer: Provide an improved estimation of binding free energy by combining molecular mechanics with implicit solvent models to refine ranked hits.

Q10. Which statement best describes fragment‑based lead discovery (FBLD)?

• Screening small, low molecular weight fragments with high ligand efficiency and linking or growing them into larger leads.
• Using only natural product libraries for primary screening.
• Performing full‑length protein assays to find allosteric modulators exclusively.
• Randomly screening peptides above 10 kDa for binding activity.

Correct Answer: Screening small, low molecular weight fragments with high ligand efficiency and linking or growing them into larger leads.

Q11. What is the principal advantage of consensus scoring in virtual screening?

• Combining multiple scoring functions reduces false positives by leveraging complementary strengths of different scores.
• It guarantees the single best binding pose without further validation.
• It speeds up docking by running fewer calculations per ligand.
• It eliminates the need for any experimental validation of hits.

Correct Answer: Combining multiple scoring functions reduces false positives by leveraging complementary strengths of different scores.

Q12. Which of the following is the correct summary of Lipinski’s Rule of Five?

• Rule of five predicts oral bioavailability; no more than 5 H‑bond donors, 10 H‑bond acceptors, molecular weight <500 Da and logP <5.
• Rule of five requires compounds to have exactly five rotatable bonds and five rings for oral drugs.
• Rule of five mandates pKa values must be multiples of five for oral absorption.
• Rule of five applies only to injectable biologics and large peptides.

Correct Answer: Rule of five predicts oral bioavailability; no more than 5 H‑bond donors, 10 H‑bond acceptors, molecular weight <500 Da and logP <5.

Q13. Which approach is commonly used for virtual combinatorial library enumeration?

• Reaction‑based enumeration using building blocks and defined chemical transformation rules to generate synthetically tractable libraries.
• Random SMILES mutation without consideration of synthetic feasibility.
• Only using natural product isolation to create virtual libraries.
• Generating 3D protein conformations and treating them as libraries.

Correct Answer: Reaction‑based enumeration using building blocks and defined chemical transformation rules to generate synthetically tractable libraries.

Q14. How can explicit water molecules influence docking results?

• Water can mediate hydrogen bonds between ligand and protein, stabilize poses or be displaced contributing favorably or unfavorably to binding energetics.
• Water molecules always destabilize ligand binding and should never be included.
• Water only influences ligand photostability and is irrelevant for docking.
• Explicit water molecules are only used to calculate NMR chemical shifts, not docking.

Correct Answer: Water can mediate hydrogen bonds between ligand and protein, stabilize poses or be displaced contributing favorably or unfavorably to binding energetics.

Q15. What is the objective of cross‑docking experiments in computational screening?

• Docking a set of ligands into multiple receptor conformations to assess robustness and account for receptor flexibility and induced fit.
• Using one ligand to dock into thousands of unrelated proteins to find off‑targets exclusively.
• Cross‑docking refers to docking peptides across membranes in molecular dynamics only.
• It is a method to convert 2D pharmacophores into 1D fingerprints.

Correct Answer: Docking a set of ligands into multiple receptor conformations to assess robustness and account for receptor flexibility and induced fit.

Q16. Which role can machine learning (ML) play in virtual screening workflows?

• ML can predict compound activity from molecular descriptors, prioritize hits, generate improved scoring functions, and design novel molecules.
• ML eliminates the need for any chemical descriptors and uses only protein sequence to predict exact binding energies.
• ML is only useful for image recognition and has no place in virtual screening.
• ML replaces laboratory assays by directly measuring compound potency in silico with 100% accuracy.

Correct Answer: ML can predict compound activity from molecular descriptors, prioritize hits, generate improved scoring functions, and design novel molecules.

Q17. What special considerations apply to covalent docking compared to non‑covalent docking?

• Covalent docking must model formation of a covalent bond, consider warhead reactivity, reaction mechanism and proper covalent geometry in scoring.
• Covalent docking ignores ligand reactivity and only uses standard non‑covalent scoring functions.
• Covalent docking is identical to fragment docking and uses fragments exclusively.
• Covalent docking does not require receptor structure because covalent bonds form independently in solution.

Correct Answer: Covalent docking must model formation of a covalent bond, consider warhead reactivity, reaction mechanism and proper covalent geometry in scoring.

Q18. Which RMSD value between a predicted and experimental pose is commonly considered acceptable for successful pose prediction?

• An RMSD ≤ 2.0 Å is typically considered a near‑native (acceptable) pose.
• An RMSD ≥ 5.0 Å indicates a successful pose prediction.
• RMSD must be exactly 0.0 Å to be acceptable.
• RMSD is irrelevant for docking and is not used.

Correct Answer: An RMSD ≤ 2.0 Å is typically considered a near‑native (acceptable) pose.

Q19. What criterion is important when selecting decoys for benchmarking a virtual screening method?

• Decoys should match the actives’ physical properties (e.g., MW, logP) but be topologically dissimilar so they are likely inactive.
• Decoys must be structural isomers of the actives to ensure confusion.
• Decoys should be the most potent known inhibitors to test false negatives.
• Decoys must always be peptides regardless of the active class.

Correct Answer: Decoys should match the actives’ physical properties (e.g., MW, logP) but be topologically dissimilar so they are likely inactive.

Q20. Which set of metrics are commonly used to validate a virtual screening campaign for early enrichment?

• Enrichment factor, ROC AUC, BEDROC and early enrichment metrics (e.g., EF1%, EF5%).
• Only molecular weight and logP are used as validation metrics.
• Full quantum mechanical binding energy is the only required metric.
• Number of rotatable bonds and ring count alone validate screening quality.

Correct Answer: Enrichment factor, ROC AUC, BEDROC and early enrichment metrics (e.g., EF1%, EF5%).

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