Docking algorithms and applications MCQs With Answer

Docking algorithms and applications MCQs With Answer

This quiz collection is designed for M.Pharm students to deepen understanding of molecular docking algorithms and their practical applications in drug discovery. The questions cover core concepts — search algorithms, scoring functions, receptor flexibility, treatment of water and metal ions, covalent docking, virtual screening metrics, and post-docking refinement methods such as MD and MM-PBSA. Each MCQ tests both theory and applied knowledge relevant to using popular tools (AutoDock, AutoDock Vina, Glide, CovDock) and best practices for validation and interpretation of results. Use these items to assess readiness for computational projects, critical reading of docking literature, and rational design workflows in pharmaceutical research.

Q1. What is the primary objective of molecular docking in structure-based drug design?

• Predict the metabolic stability of a compound
• Estimate ADME-Tox properties from structure
• Predict the preferred binding pose and estimate binding affinity between ligand and target
• Design synthetic routes for small molecules

Correct Answer: Predict the preferred binding pose and estimate binding affinity between ligand and target

Q2. Which classification best describes common docking algorithm approaches?

• Quantum mechanics, molecular dynamics, and cheminformatics
• Rigid docking, flexible-ligand docking, and induced-fit (flexible receptor) docking
• Homology modeling, threading, and ab initio folding
• 2D similarity search, pharmacophore mapping, and ADMET prediction

Correct Answer: Rigid docking, flexible-ligand docking, and induced-fit (flexible receptor) docking

Q3. Which physical terms are typically included in physics-based scoring functions?

• van der Waals, electrostatics, solvation, and sometimes entropy
• Only empirical regression coefficients without physical interaction terms
• Sequence conservation scores and domain annotations
• 2D fingerprint similarity scores

Correct Answer: van der Waals, electrostatics, solvation, and sometimes entropy

Q4. What defines a knowledge-based scoring function?

• It uses quantum mechanical calculations for every interaction
• It derives statistical potentials from known protein–ligand structures in databases
• It relies solely on hydrophobic surface area estimations
• It performs free energy perturbation calculations

Correct Answer: It derives statistical potentials from known protein–ligand structures in databases

Q5. Which search algorithm uses a population of candidate solutions, selection, crossover and mutation operators?

• Systematic grid search
• Simulated annealing
• Genetic algorithm
• Deterministic gradient descent

Correct Answer: Genetic algorithm

Q6. AutoDock 4 commonly employs which specific search method?

• Monte Carlo docking
• Lamarckian genetic algorithm
• Exhaustive rigid-body enumeration
• Deterministic Newton-Raphson optimizer

Correct Answer: Lamarckian genetic algorithm

Q7. In pose prediction benchmarking, which RMSD value (ligand heavy atoms) is commonly used as the success threshold?

• ≤0.5 Å
• ≤2.0 Å
• ≤5.0 Å
• ≤10.0 Å

Correct Answer: ≤2.0 Å

Q8. What does MM-PBSA or MM-GBSA analysis estimate in the context of docking follow-up?

• Protein tertiary structure from sequence
• Quantitative ADMET predictions
• Binding free energy using molecular mechanics plus continuum solvation
• 2D substructure similarity scores

Correct Answer: Binding free energy using molecular mechanics plus continuum solvation

Q9. What is the primary goal of consensus scoring in virtual screening?

• Reduce computational cost by using a single fast score
• Combine multiple scoring functions to improve hit ranking and reduce false positives
• Replace docking with ligand-based QSAR models
• Convert docking scores to pKa predictions

Correct Answer: Combine multiple scoring functions to improve hit ranking and reduce false positives

Q10. The term “induced fit” in docking refers to which phenomenon?

• Ligand rotating freely in an empty binding site
• Protein undergoing conformational changes upon ligand binding
• Ligand covalently attaching to protein
• Using fixed receptor rigid-body approximations

Correct Answer: Protein undergoing conformational changes upon ligand binding

Q11. How are structural water molecules most often treated in docking studies?

• Always deleted because water never plays a role in binding
• Included or excluded selectively because they can mediate hydrogen-bond bridges or be displaced with thermodynamic consequences
• Converted to ions for faster calculations
• Replaced by generalized Born radii automatically

Correct Answer: Included or excluded selectively because they can mediate hydrogen-bond bridges or be displaced with thermodynamic consequences

Q12. Which approach is required to model ligands that form covalent bonds with the target during docking?

• Standard non-covalent docking with rigid receptor
• Pharmacophore mapping only
• Specialized covalent docking protocols that explicitly model bond formation and reaction geometry
• 2D similarity searching against covalent inhibitors

Correct Answer: Specialized covalent docking protocols that explicitly model bond formation and reaction geometry

Q13. The main objective of virtual screening by docking is to:

• Determine absolute binding free energies to 0.1 kcal/mol accuracy
• Identify and prioritize a subset of potential binders from large compound libraries
• Predict clinical trial outcomes
• Replace all experimental binding assays

Correct Answer: Identify and prioritize a subset of potential binders from large compound libraries

Q14. Which metric specifically quantifies early enrichment of actives in a ranked screening list?

• Root-mean-square deviation (RMSD)
• Area under receiver operating characteristic curve (AUC-ROC)
• Enrichment Factor (EF) at a given percentage of the library
• pKa shift

Correct Answer: Enrichment Factor (EF) at a given percentage of the library

Q15. Protein–protein docking primarily aims to predict what?

• Small-molecule binding affinities
• Orientation and interface residues for two interacting proteins
• Membrane permeability of peptides
• Gene expression levels

Correct Answer: Orientation and interface residues for two interacting proteins

Q16. AutoDock Vina is known for which major improvement over older docking engines?

• It uses full quantum mechanics for scoring every pose
• Significantly improved speed and an efficient gradient-based conformational search with an empirical scoring function
• It only docks peptides and proteins, not small molecules
• Built-in synthesis planning for hits

Correct Answer: Significantly improved speed and an efficient gradient-based conformational search with an empirical scoring function

Q17. What is widely regarded as one of the most challenging aspects to model accurately in docking?

• Setting up 2D chemical diagrams
• Accounting for receptor flexibility and entropic effects upon binding
• Reading PDB file headers
• Converting SMILES strings to InChI

Correct Answer: Accounting for receptor flexibility and entropic effects upon binding

Q18. Which curated dataset is commonly used for benchmarking virtual screening and enrichment performance?

• ChEMBL full database
• PDBBind general set
• DUD-E (Directory of Useful Decoys, Enhanced)
• PubChem BioAssay raw data

Correct Answer: DUD-E (Directory of Useful Decoys, Enhanced)

Q19. After docking, which procedure is recommended to refine and obtain more reliable binding free energy estimates?

• Simple re-ranking by molecular weight
• Molecular dynamics simulation followed by MM-PBSA or MM-GBSA calculations
• Converting docking poses to 2D fingerprints
• Removing all charged residues from the binding site

Correct Answer: Molecular dynamics simulation followed by MM-PBSA or MM-GBSA calculations

Q20. Knowledge-based scoring functions are derived mainly from which source of information?

• High-throughput screening assay readouts
• Statistical analysis of observed atom–atom contacts and distances in experimental structural databases (e.g., PDB)
• Predicted protein sequences
• Clinical trial endpoints

Correct Answer: Statistical analysis of observed atom–atom contacts and distances in experimental structural databases (e.g., PDB)

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