Computer-Aided Drug Design (CADD)
Start with the big picture
CADD brings together tools that model target–ligand interactions, relate molecular features to biological activity, and help prioritize compounds for further study. Structure-based CADD uses a target structure obtained experimentally or through homology modeling; ligand-based CADD is useful when that structure is unavailable and active ligands are known. Methods such as docking, pharmacophore modeling, QSAR, virtual screening, de novo design, and molecular dynamics address different parts of the design process. Computational predictions—including ADMET estimates—can help identify candidates for follow-up, but they support rather than replace experimental evaluation. The broader lesson follows CADD through its approaches, methods, workflow, advantages, limitations, software, and regulatory considerations.
What you'll learn
- Distinguish structure-based CADD from ligand-based CADD.
- Describe how target structures can be obtained for structure-based design.
- Explain the roles of docking, pharmacophores, QSAR, and virtual screening.
- Identify how de novo design and molecular dynamics contribute to CADD.
- Summarize how computational predictions support lead prioritization and optimization.
Continue your study
Work through the complete notes and reinforce the topic with the study tools available in the full lesson.
