Computational methods in drug discovery.

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• Computer-aided drug discovery (CADD) has been instrumental in developing small molecule therapeutics for over 30 years.
• CADD methods are broadly categorized into structure-based and ligand-based approaches, each with distinct requirements and applications.

Purpose of the Study:

• To provide a comprehensive overview of the most important CADD methods.
• To discuss recent successful applications and essential tools for implementing CADD in drug discovery campaigns.
• To cover computational methods for toxicity prediction and optimization of pharmacokinetic properties.

Main Methods:

• Structure-based methods: ligand docking, pharmacophore modeling, ligand design.
• Ligand-based methods: ligand-based pharmacophores, molecular descriptors, quantitative structure-activity relationships (QSAR).
• Supporting tools: target/ligand databases, homology modeling, ligand fingerprinting.

Main Results:

• Detailed discussion of the theoretical underpinnings of key CADD techniques.
• Illustrative examples of successful applications of CADD in drug discovery from scientific literature.
• Exploration of computational approaches for predicting toxicity and optimizing drug-like properties.

Conclusions:

• CADD methods, encompassing both structure-based and ligand-based strategies, are indispensable in modern drug discovery.
• The integration of various computational tools and techniques is vital for successful drug design and optimization.
• Computational approaches significantly contribute to predicting and mitigating potential toxicity and improving physiological properties of drug candidates.