Systematic Extraction of Analogue Series from Large Compound Collections Using a New Computational Compound-Core...

Area of Science:

• Organic Chemistry
• Medicinal Chemistry
• Computational Chemistry

Background:

• Chemical optimization generates analogue series (AS).
• ASs are vital for structure-activity relationship (SAR) studies and drug development.
• Identifying ASs in large databases without query compounds is challenging.

Purpose of the Study:

• To develop a systematic computational approach for identifying analogue series in compound collections.
• To enable large-scale extraction of ASs from chemical databases.

Main Methods:

• Compound decomposition using retrosynthetic rules.
• Organization of compound-core relationships.
• Identification of analogues sharing a common core structure.

Main Results:

• A novel computational method for systematic AS identification was developed.
• The method was applied to the ChEMBL database.
• Over 30,000 distinct analogue series were extracted.

Conclusions:

• The computational approach effectively identifies and extracts analogue series from large chemical databases.
• This method facilitates SAR studies and accelerates drug discovery pipelines.