Drug-motif-based diverse monomer selection: method and application in combinatorial chemistry

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Combinatorial chemistry relies on diverse monomer building blocks for creating novel molecular libraries.
• Integrating drug motif knowledge early in monomer selection can improve the drug-likeness of synthesized compounds.
• Efficiently exploring vast chemical spaces for suitable monomers is a significant challenge.

Purpose of the Study:

• To develop and describe a computational strategy for exploring monomer diversity.
• To incorporate drug motif knowledge into the design and selection process of monomers.
• To facilitate the identification of drug-like monomers for combinatorial chemistry applications.

Main Methods:

• Assembled monomer databases based on common functional groups (carboxylic acids, aldehydes, nitriles, primary/secondary amines).
• Utilized fingerprint and cluster analysis (Jarvis-Patrick algorithm) for monomer profiling.
• Calculated molecular profiles including weight, H-bond counts, and rotatable bond flexibility.
• Compared cluster representatives with drug molecules from the Standard Derwent File (SDF) to identify drug motif-based monomers.

Main Results:

• Created five specialized DAYLIGHT databases for monomer exploration.
• Established a method for clustering monomers and storing their profiles for similarity and diversity searches.
• Demonstrated the application of the strategy using an aldehyde set, identifying drug motif-based monomers.
• Enabled profile-based prescreening of monomers for purchase or synthesis.

Conclusions:

• The described strategy effectively integrates drug motif knowledge into monomer selection for combinatorial chemistry.
• Computational analysis of monomer databases allows for efficient exploration of chemical diversity.
• This approach aids in the rational design of focused chemical libraries with enhanced drug-like properties.