CCLab--a multi-objective genetic algorithm based combinatorial library design software and an application for histone...

Area of Science:

• Computational Chemistry
• Medicinal Chemistry
• Drug Discovery

Background:

• Multi-objective optimization significantly impacts virtual combinatorial library design.
• Simultaneous consideration of factors like synthesis cost and drug-likeness can improve the identification of biologically active compounds.

Purpose of the Study:

• To introduce CCLab (Combinatorial Chemistry Laboratory), a software tool for combinatorial library design.
• To evaluate the software's performance using in silico tests and a real-world drug design case.

Main Methods:

• Development of CCLab software based on a multi-objective genetic algorithm.
• In silico assessment of convergence and building block retrieval.
• Application to design a 5x6 histone deacetylase (HDAC) inhibitor library.

Main Results:

• CCLab demonstrated effective convergence and building block utilization in silico.
• The designed HDAC inhibitor library yielded 14 compounds with >50% inhibition against 3 HDAC enzymes.
• Three compounds exhibited IC50 values comparable to SAHA, a known HDAC inhibitor.

Conclusions:

• CCLab software enhances hit rates for designed libraries.
• The tool is beneficial for medicinal chemists in developing focused libraries for drug development.