Simplified activity cliff network representations with high interpretability and immediate access to SAR information.

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Activity cliffs (ACs) represent structurally similar compounds with significant potency differences, highlighting discontinuities in structure-activity relationships (SARs).
• Coordinated formation of ACs within compound datasets offers rich SAR information but complicates analysis due to network complexity.

Purpose of the Study:

• To develop a simplified method for analyzing complex activity cliff networks.
• To enhance the interpretability of SAR information derived from activity cliffs.

Main Methods:

• Introduction of reduced network representations for activity cliff networks.
• Transformation of complex AC networks into an interpretable format.
• Generation of R-group tables from simplified network variants.

Main Results:

• Reduced network representations significantly improve the interpretability of large and complex AC networks.
• The simplified approach facilitates the extraction of SAR information.
• Facilitates SAR exploration by making complex networks more navigable.

Conclusions:

• Reduced network representations offer a powerful tool for simplifying and interpreting complex activity cliff networks.
• This method substantially supports SAR exploration in drug discovery.
• Enables more efficient extraction of valuable SAR insights from compound data.