Assessing how well a modeling protocol captures a structure-activity landscape.

Area of Science:

• Computational chemistry
• Cheminformatics
• Medicinal chemistry

Background:

• Structure-activity relationships (SAR) are crucial for drug discovery.
• Activity cliffs, pairs of similar molecules with differing activities, pose challenges.
• Previous work introduced the Structure-Activity Landscape Index (SALI) parameter to identify these cliffs.

Purpose of the Study:

• Introduce Structure-Activity Landscape Index (SALI) curves to assess modeling protocols.
• Evaluate the predictive performance of various quantitative structure-activity relationship (QSAR) and structure-based models.
• Correlate SALI curve characteristics with model utility in SAR studies.

Main Methods:

• Developed SALI curves to quantify a model's ability to predict pairwise molecular activity orderings.
• Applied SALI curves to assess diverse models, including 2D-QSAR, 3D-QSAR, and structure-based design models.
• Analyzed the integral of SALI curves (SCI) as a performance metric, ranging from -1.0 to 1.0.

Main Results:

• SALI curve characteristics correlate with the empirical utility of different modeling approaches.
• The integral of SALI curves (SCI) approaches 1.0 for known prospectively useful literature models.
• This indicates SALI curves can effectively benchmark and compare SAR modeling strategies.

Conclusions:

• SALI curves provide a novel and effective method for evaluating SAR models.
• The SCI metric, derived from SALI curves, serves as a reliable indicator of a model's predictive power.
• This approach enhances the assessment of modeling protocols in drug discovery and development.