De novo drug design principles MCQs With Answer

Introduction: De novo drug design principles MCQs With Answer is an essential study resource for B. Pharm students learning modern drug discovery. This topic covers structure-based and ligand-based design, pharmacophore modeling, fragment-based approaches, scoring functions, molecular docking, generative algorithms, ADMET prediction, and lead optimization. Understanding concepts such as scaffold hopping, synthetic accessibility, QSAR descriptors, force fields, and multi-parameter optimization prepares students for computational workflows and rational design decisions. These MCQs emphasize core principles, practical challenges, common pitfalls, and validation metrics to build a strong foundation in computer-aided drug design. Now let’s test your knowledge with 30 MCQs on this topic.

Q1. What is the primary goal of de novo drug design?

• To identify natural products in biological extracts
• To predict patient-specific drug dosing
• To generate novel chemical structures predicted to bind a biological target
• To sequence the genome of a target organism

Correct Answer: To generate novel chemical structures predicted to bind a biological target

Q2. Which strategy uses the 3D structure of the target protein to build new ligands?

• Ligand-based design
• Structure-based design
• High-throughput screening
• Pharmacokinetic modeling

Correct Answer: Structure-based design

Q3. Which method relies on known active ligands rather than the receptor structure?

• Structure-based design
• Fragment-based de novo design
• Ligand-based design
• Molecular dynamics-driven design

Correct Answer: Ligand-based design

Q4. What is a pharmacophore model?

• A 3D arrangement of steric and electronic features necessary for biological activity
• A chemical synthesis route for a lead compound
• A scoring function for docking
• An ADMET prediction algorithm

Correct Answer: A 3D arrangement of steric and electronic features necessary for biological activity

Q5. Fragment-based de novo design primarily involves which process?

• Linking or growing small chemical fragments inside the binding site
• Screening millions of commercial compounds without decomposition
• Predicting toxicology from whole-organism models
• Simulating full-length proteins in membrane environments

Correct Answer: Linking or growing small chemical fragments inside the binding site

Q6. Which of the following is a common scoring function category used in de novo design?

• Empirical, knowledge-based, and force-field-based scoring functions
• Quantum-only scoring
• Clinical endpoint scoring
• Petri-net scoring

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

Q7. What does “synthetic accessibility” refer to in de novo design?

• The likelihood a candidate can be synthesized with practical chemistry
• The probability of a compound being orally absorbed in humans
• The chance of a molecule passing regulatory approval
• The number of chiral centers a molecule contains

Correct Answer: The likelihood a candidate can be synthesized with practical chemistry

Q8. Which rule set is commonly used to evaluate drug-likeness early in design?

• Le Chatelier’s principle
• Lipinski’s Rule of Five
• Henderson-Hasselbalch rules
• Michaelis-Menten guidelines

Correct Answer: Lipinski’s Rule of Five

Q9. In de novo design, what is “scaffold hopping”?

• Replacing a core scaffold to find new chemotypes retaining activity
• Using high-temperature reactions to create scaffolds
• Moving a scaffold physically between labs
• Scaling up synthesis for lead manufacturing

Correct Answer: Replacing a core scaffold to find new chemotypes retaining activity

Q10. Which computational approach generates novel molecules using neural networks or GANs?

• Empirical scoring
• Generative deep learning models
• Quantum annealing
• Classical force-field minimization alone

Correct Answer: Generative deep learning models

Q11. What does ADMET stand for and why is it important in de novo design?

• Absorption, Distribution, Metabolism, Excretion, Toxicity — evaluates pharmacokinetics and safety
• Affinity, Docking, Electrostatics, Modeling, Torsion — molecular mechanics terms
• Assay, Design, Evaluation, Manufacturing, Testing — development workflow
• Analytical, Diagnostic, Medicinal, Experimental, Therapeutic — clinical categories

Correct Answer: Absorption, Distribution, Metabolism, Excretion, Toxicity — evaluates pharmacokinetics and safety

Q12. Which metric assesses how well a predicted ligand pose matches the experimental pose?

• RMSD (Root Mean Square Deviation)
• pKa value
• Log P
• Minimal inhibitory concentration (MIC)

Correct Answer: RMSD (Root Mean Square Deviation)

Q13. Which pitfall is a known limitation of many scoring functions?

• Perfect prediction of ADMET properties
• Inability to rank binding affinities accurately due to entropic effects
• Always predicting correct synthetic routes
• Generating unlimited conformers instantly

Correct Answer: Inability to rank binding affinities accurately due to entropic effects

Q14. In fragment growing, what is a crucial consideration for linking fragments?

• Ensuring the linker maintains favorable geometry and interactions without strain
• Maximizing molecular weight regardless of binding
• Always using rigid aromatic linkers only
• Avoiding any hydrogen bond donors

Correct Answer: Ensuring the linker maintains favorable geometry and interactions without strain

Q15. What role does molecular dynamics (MD) play in structure-based de novo design?

• Simulating receptor flexibility and ligand stability over time
• Automatically synthesizing designed compounds
• Predicting clinical trial outcomes directly
• Replacing quantum mechanics for electronic properties in all cases

Correct Answer: Simulating receptor flexibility and ligand stability over time

Q16. Which descriptor type is commonly used in QSAR models supporting de novo design?

• Topological, physicochemical, and electronic molecular descriptors
• Clinical trial phase descriptors
• Only synthetic route counts
• Patient demographic descriptors

Correct Answer: Topological, physicochemical, and electronic molecular descriptors

Q17. What is multi-parameter optimization (MPO) in de novo design?

• Optimizing multiple properties such as potency, ADMET, and synthetic feasibility simultaneously
• Maximizing only binding affinity at all costs
• Focusing solely on aesthetic molecular shapes
• Optimizing only solubility and ignoring potency

Correct Answer: Optimizing multiple properties such as potency, ADMET, and synthetic feasibility simultaneously

Q18. Which is an example of a rule used to filter out problematic chemical structures?

• PAINS (Pan-Assay INterference compoundS) filters
• Golden Rule of Pharmacology
• Leucine Z-rule
• HOMO-LUMO exclusion rule

Correct Answer: PAINS (Pan-Assay INterference compoundS) filters

Q19. Which algorithmic approach incrementally modifies molecules using mutation and crossover concepts?

• Genetic algorithms
• Deterministic linear programming only
• Single-point molecular algebra
• Fourier transform generation

Correct Answer: Genetic algorithms

Q20. What is the main advantage of graph-based de novo design methods?

• They directly manipulate molecular graphs, enabling chemically valid structure generation
• They always guarantee crystal structures for the ligand
• They avoid any need for scoring functions
• They produce only peptides

Correct Answer: They directly manipulate molecular graphs, enabling chemically valid structure generation

Q21. Why is conformational sampling important in de novo design?

• To explore possible ligand shapes and ensure favorable binding conformations are considered
• To increase molecular weight artificially
• To reduce calculation time to zero
• To guarantee a single rigid pose for every ligand

Correct Answer: To explore possible ligand shapes and ensure favorable binding conformations are considered

Q22. Which validation metric measures the ability of a virtual screening method to enrich actives early?

• Enrichment factor (EF)
• Partition coefficient
• Synthetic accessibility score
• Boiling point

Correct Answer: Enrichment factor (EF)

Q23. What is a common strategy to reduce false positives from docking in de novo design?

• Use consensus scoring and rescoring with more rigorous methods
• Ignore all docking scores and pick randomly
• Only accept molecules with more than 100 heavy atoms
• Always choose highest molecular weight compounds

Correct Answer: Use consensus scoring and rescoring with more rigorous methods

Q24. Which property is reflected by LogP and is relevant in de novo design?

• Lipophilicity, influencing permeability and solubility
• Optical rotation
• Number of hydrogen bond acceptors only
• Refractive index

Correct Answer: Lipophilicity, influencing permeability and solubility

Q25. In de novo design, which of the following best describes “bioisosteric replacement”?

• Replacing a functional group with another that preserves activity but alters properties
• Changing an atom only to isotopic variants
• Adding toxicophores to increase potency
• Switching from organic to inorganic scaffolds exclusively

Correct Answer: Replacing a functional group with another that preserves activity but alters properties

Q26. Which tool category specifically helps propose synthetic routes for designed molecules?

• Retrosynthetic analysis and synthesis planning software
• Docking engines only
• MD integrators exclusively
• Quantum hardware controllers

Correct Answer: Retrosynthetic analysis and synthesis planning software

Q27. How does incorporation of receptor flexibility improve de novo design outcomes?

• By allowing design to account for induced fit and alternative binding site conformations
• By making scoring functions unnecessary
• By decreasing computational cost astronomically
• By preventing any ligand from binding

Correct Answer: By allowing design to account for induced fit and alternative binding site conformations

Q28. What is the purpose of using knowledge-based potentials in scoring?

• To use statistical information from known protein–ligand complexes to estimate interaction favorability
• To run quantum mechanical calculations at zero cost
• To physically synthesize ligands in silico
• To ensure molecules violate drug-likeness rules

Correct Answer: To use statistical information from known protein–ligand complexes to estimate interaction favorability

Q29. Which practice helps ensure de novo designs are less likely to fail later due to toxicity?

• Early incorporation of in silico toxicity and off-target prediction models
• Maximizing aromatic rings without regard for metabolism
• Only optimizing for binding energy in vacuum
• Avoiding any ADMET assessment until clinical trials

Correct Answer: Early incorporation of in silico toxicity and off-target prediction models

Q30. Which outcome indicates a successful de novo design campaign?

• Designed molecules that are synthetically feasible, show predicted target activity, acceptable ADMET, and experimental validation
• Only molecules with the largest molecular weights regardless of properties
• Complete reliance on a single docking score with no experiments
• Designs that always fail to bind but are easy to synthesize

Correct Answer: Designed molecules that are synthetically feasible, show predicted target activity, acceptable ADMET, and experimental validation

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