Systematic computational analysis of structure-activity relationships: concepts, challenges and recent advances

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Structure-activity relationships (SARs) are crucial in medicinal chemistry, traditionally analyzed using chemical intuition.
• Quantitative (Q)SAR methods have been used since the 1960s for SAR exploration and compound optimization.
• Existing computational methods face limitations due to compound class dependence and data heterogeneity.

Purpose of the Study:

• To introduce novel SAR analysis functions to complement existing computational methods.
• To enable large-scale evaluation and comparison of SAR features.
• To systematically profile and compare SARs across different datasets and identify promising optimization targets.

Main Methods:

• Development and application of advanced SAR analysis functions.
• Large-scale evaluation and comparison of SAR features.
• Systematic profiling and characterization of global and local SAR features.
• Integration of numerical analysis with graphical representations of SAR landscapes.

Main Results:

• New functions facilitate large-scale SAR feature evaluation and comparison.
• SAR information can be extracted from compound datasets to prioritize optimization targets.
• Systematic profiling and characterization of both global and local SAR features are enabled.
• Graphical representations complement numerical SAR analysis.

Conclusions:

• Novel SAR analysis functions extend the computational toolkit for medicinal chemistry.
• These functions allow for a more systematic and comprehensive exploration of SARs.
• The approach aids in identifying and prioritizing key SARs for drug optimization efforts.