Virtual darwinian drug design: QSAR inverse problem, virtual combinatorial chemistry, and computational screening

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Evolutionary principles of diversity generation and selection are mirrored in modern drug design.
• Drug design methodologies can be broadly categorized into "Darwinian" (diversity-based) and structure-based approaches.
• Quantitative Structure-Activity Relationship (QSAR) models are crucial for predicting molecular properties.

Purpose of the Study:

• To review graph theoretical approaches in drug design.
• To highlight "Darwinian" methods, including inverse QSAR, virtual combinatorial synthesis, and virtual screening.
• To provide a survey of other simulation methods in drug design.

Main Methods:

• Focus on graph theoretical drug design.
• Discussion of inverse QSAR for computational generation and selection of chemical structures.
• Exploration of virtual combinatorial synthesis and virtual screening concepts.

Main Results:

• Darwinian methods, exemplified by inverse QSAR, offer a distinct paradigm from structure-based drug design.
• Virtual combinatorial synthesis enables the computational generation of large virtual libraries.
• Virtual screening facilitates the selection of promising compounds from these libraries for synthesis and testing.

Conclusions:

• "Darwinian" drug design, utilizing computational generation and selection, is a powerful strategy.
• Graph theoretical approaches and simulation methods are integral to modern drug discovery pipelines.
• The integration of virtual synthesis and screening accelerates the identification of novel drug candidates.