Activity cliffs and activity cliff generators based on chemotype-related activity landscapes

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Activity cliffs significantly impact drug discovery, necessitating effective detection and quantification methods.
• Existing methods for analyzing structure-activity relationships (SAR) can be enhanced by incorporating chemotype information.

Purpose of the Study:

• To introduce the activity cliff enrichment factor metric.
• To expand the activity cliff generator concept by integrating chemotype information.
• To characterize molecular databases and identify chemotypes enriched in activity cliffs.

Main Methods:

• Grouping compounds into chemotype classes within molecular databases.
• Calculating pairwise structure similarities and activity differences.
• Constructing chemotype-based structure-activity similarity (SAS) maps.
• Applying the activity cliff enrichment factor and activity cliff generator concepts.

Main Results:

• Observed distinct activity landscape distributions across different chemotypes on SAS maps.
• Identified several chemotype classes with a higher proportion of activity cliffs than the overall database.
• Detected chemotype classes exhibiting smooth structure-activity relationships (SAR).

Conclusions:

• The activity cliff enrichment factor effectively detects chemotypes with a high prevalence of activity cliffs.
• Integrating chemotype information provides valuable insights into SAR and aids in drug discovery.
• The enhanced activity cliff generator concept facilitates the extraction of crucial SAR information.