Introduction to CADD: history and techniques MCQs With Answer

Introduction to CADD: history and techniques MCQs With Answer

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Introduction: Introduction to CADD: history and techniques MCQs With Answer is designed for M.Pharm students to reinforce core concepts of Computer-Aided Drug Design (CADD). This concise quiz set covers historical milestones, fundamental algorithms, and practical techniques used in modern drug discovery, including ligand- and structure-based approaches, QSAR, pharmacophore modeling, molecular docking, virtual screening, molecular dynamics, and ADME/T considerations. Questions aim to deepen conceptual understanding, link theory to practice, and clarify strengths and limitations of different methods. Use these MCQs to assess preparedness for advanced coursework, seminars, and research in computational pharmaceutics and rational drug design.

Q1. What historical development is most directly responsible for enabling modern structure-based drug design?

  • Discovery of penicillin
  • Advances in X-ray crystallography and protein structure determination
  • Invention of high-performance liquid chromatography (HPLC)
  • Development of patch-clamp electrophysiology

Correct Answer: Advances in X-ray crystallography and protein structure determination

Q2. Which technique primarily uses known active ligands to build predictive models when target structure is unavailable?

  • Structure-based virtual screening
  • Ligand-based drug design (e.g., QSAR, pharmacophore modeling)
  • Molecular dynamics simulation
  • Homology modeling

Correct Answer: Ligand-based drug design (e.g., QSAR, pharmacophore modeling)

Q3. Which of the following best describes QSAR?

  • A method to visualize protein-ligand binding modes
  • A statistical approach correlating molecular descriptors with biological activity
  • A technique to predict 3D protein structures from sequence
  • A quantum mechanical method for computing electron density

Correct Answer: A statistical approach correlating molecular descriptors with biological activity

Q4. In molecular docking, the scoring function primarily estimates:

  • The synthetic accessibility of a ligand
  • The binding affinity or complementarity between ligand and receptor
  • The molecular weight distribution in a chemical library
  • The ADME properties of a ligand

Correct Answer: The binding affinity or complementarity between ligand and receptor

Q5. Homology modeling is most reliable when the template protein has what level of sequence identity to the target?

  • Less than 20%
  • 20–35%
  • Above 50% sequence identity
  • No sequence similarity required

Correct Answer: Above 50% sequence identity

Q6. Which force field component accounts for bond stretching in molecular mechanics?

  • Van der Waals term
  • Electrostatic term
  • Bond force (harmonic potential)
  • Dihedral (torsional) term only

Correct Answer: Bond force (harmonic potential)

Q7. What is the main purpose of pharmacophore modeling?

  • To calculate explicit solvent effects around a protein
  • To identify spatial arrangement of features necessary for biological activity
  • To perform quantum chemical calculations on ligands
  • To optimize synthetic routes for drug molecules

Correct Answer: To identify spatial arrangement of features necessary for biological activity

Q8. Fragment-based drug design (FBDD) primarily differs from high-throughput screening because it:

  • Uses larger libraries of full-size drug-like molecules
  • Starts with small, low-affinity fragments and grows or links them
  • Exclusively uses in vivo assays
  • Relies solely on ligand-based similarity metrics

Correct Answer: Starts with small, low-affinity fragments and grows or links them

Q9. Which method provides dynamic insight into protein-ligand interactions over time?

  • Molecular dynamics (MD) simulation
  • 2D QSAR
  • Rigid-body docking
  • Rule-based ADMET filters

Correct Answer: Molecular dynamics (MD) simulation

Q10. In virtual screening, enrichment factor (EF) is used to evaluate:

  • Speed of docking algorithm
  • Ability of screening to prioritize actives over decoys compared to random selection
  • Number of rotatable bonds in compounds
  • Toxicity risk predicted by rule-based models

Correct Answer: Ability of screening to prioritize actives over decoys compared to random selection

Q11. Which descriptor category is commonly used in QSAR models?

  • Topological, electronic, and physicochemical descriptors
  • Only macroscopic melting point data
  • Synthetic route complexity metrics exclusively
  • Clinical trial endpoints

Correct Answer: Topological, electronic, and physicochemical descriptors

Q12. Which statement about scoring functions is correct?

  • All scoring functions incorporate explicit solvent and entropy equally well
  • Empirical, knowledge-based, and force-field scoring functions each have different strengths and limitations
  • Scoring functions are unnecessary if ligand similarity is high
  • They always provide quantitatively accurate binding free energies

Correct Answer: Empirical, knowledge-based, and force-field scoring functions each have different strengths and limitations

Q13. Which computational approach uses quantum mechanics to model electronic structure for small molecules or active sites?

  • Classical molecular docking
  • QM/MM or pure quantum mechanical calculations
  • 2D fingerprint similarity
  • Ligand-based pharmacophore mapping

Correct Answer: QM/MM or pure quantum mechanical calculations

Q14. Lipinski’s Rule of Five is primarily applied to predict:

  • Carrier-mediated transport across membranes
  • Oral bioavailability likelihood for small molecules
  • Metabolic stability in hepatocytes
  • Potential for phototoxicity

Correct Answer: Oral bioavailability likelihood for small molecules

Q15. Which similarity measure is commonly used for comparing molecular fingerprints in ligand-based screening?

  • Euclidean distance
  • Tanimoto coefficient
  • Root mean square deviation (RMSD)
  • pKa difference

Correct Answer: Tanimoto coefficient

Q16. De novo drug design algorithms aim to:

  • Predict clinical trial outcomes
  • Construct novel chemical structures computationally that fit a binding site
  • Only perform retrosynthetic analysis
  • Replace all experimental assays

Correct Answer: Construct novel chemical structures computationally that fit a binding site

Q17. What is the primary limitation of rigid receptor docking?

  • Inability to handle large ligand libraries
  • Neglect of protein flexibility and induced fit effects
  • Excessive computational cost compared to MD
  • Inaccurate ligand 2D descriptors

Correct Answer: Neglect of protein flexibility and induced fit effects

Q18. Which database is widely used as a source of experimentally measured bioactivity data for ligand-based modeling?

  • Protein Data Bank (PDB)
  • ChEMBL
  • GenBank
  • KEGG Pathway Database

Correct Answer: ChEMBL

Q19. Which metric combines potency and molecular size to help compare lead compounds?

  • Partition coefficient (log P)
  • Ligand efficiency (LE)
  • pKa
  • Number of hydrogen bond donors

Correct Answer: Ligand efficiency (LE)

Q20. Which practice improves reliability of virtual screening results?

  • Using a single rigid ligand conformation for all compounds
  • Consensus scoring and post-docking filtering (e.g., rescoring, MD refinement)
  • Ignoring ADME properties during screening
  • Relying solely on 1D descriptors

Correct Answer: Consensus scoring and post-docking filtering (e.g., rescoring, MD refinement)

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