CADi

CADi stands for computer-aided drug design. It is an umbrella term for computational methods used to discover and optimize pharmaceutical compounds. It encompasses structure-based and ligand-based approaches, and integrates computational chemistry, biology, and cheminformatics.

Common methodologies include molecular docking to predict how a compound binds to a target, de novo design to generate novel chemotypes, pharmacophore modeling, and QSAR to relate chemical features to activity. Molecular dynamics may refine binding modes; virtual screening screens large compound libraries. Machine learning and AI are increasingly used to predict properties and guide design. Data sources include public and private chemical libraries and protein structures.

The typical CADi workflow involves selecting a target, preparing molecular models, performing screening or design, evaluating hits through in silico predictions of activity and ADMET properties, and planning synthesis and experimental validation. Iterative cycles optimize potency, selectivity, and pharmacokinetic attributes.

History of CADi: CADi emerged from early computational chemistry in the 1980s and 1990s with docking, QSAR, and molecular mechanics, expanding with high-throughput virtual screening in the 2000s and AI-driven methods in the 2010s to present. It is widely used in pharmaceutical R&D and academia to reduce cost and time in lead discovery.

Limitations: In silico predictions depend on data quality and model assumptions; scoring functions may misrank poses; results require experimental validation. Ongoing developments aim to improve accuracy, interpretability, and integration with synthesis planning.

See also: Computer-aided drug design, QSAR, molecular docking, pharmacophore modeling, chemoinformatics, ADMET, machine learning in drug discovery.