The reduced graph descriptor in virtual screening and data-driven clustering of high-throughput screening data

Area of Science:

• Computational chemistry
• Cheminformatics
• Drug discovery

Background:

• Virtual screening and high-throughput screening are crucial for identifying drug leads.
• Existing methods for reduced graph similarity searching have limitations, particularly in representing molecular features.

Purpose of the Study:

• To improve similarity searching methods for ligand-based virtual screening using reduced graphs.
• To introduce a novel application of reduced graphs for clustering high-throughput screening data.

Main Methods:

• Extended the definition of reduced graphs to include ionizable groups.
• Developed a new similarity measure based on weighted edit distance for reduced graphs.
• Implemented a database system for iterative querying with multiple molecular representations.

Main Results:

• Introduced a novel data-driven clustering method for high-throughput screening data using reduced graphs.
• Demonstrated more flexible query capabilities beyond simple similarity searching.
• Showcased the utility of reduced graphs in capturing ligand-receptor interaction features.

Conclusions:

• Reduced graphs offer an informative approach to analyzing high-throughput screening data.
• The new methods enhance lead discovery by improving similarity searching and data clustering.
• Data-driven clustering effectively identifies clusters of active compounds from screening data.