Systematic characterization of structure-activity relationships and ADMET compliance: a case study

Area of Science:

• Medicinal Chemistry
• Computational Drug Discovery
• Pharmacology

Background:

• Traditional activity landscape modeling primarily analyzes Structure-Activity Relationships (SAR).
• Lead optimization in drug discovery requires simultaneous improvement of compound activity and ADMET properties.
• Integrating ADMET considerations into landscape modeling is crucial for effective drug development.

Purpose of the Study:

• To systematically analyze activity landscapes incorporating ADMET considerations.
• To investigate the impact of structural modifications on ADMET property violations.
• To identify compound pairs with similar or different drug-likeness within activity landscapes.

Main Methods:

• Utilized a dataset of 166 compounds screened for kappa-opioid receptor activity.
• Performed pairwise analysis of MACCS/Tanimoto structure similarities.
• Analyzed property similarities using 33 ADMET descriptors and a 35-dimensional 'violation bit vector' for drug-likeness.

Main Results:

• Characterized the range of ADMET property violations resulting from subtle and significant structural changes.
• Identified compound pairs with identical, comparable, and significantly different drug-likeness.
• Mapped these findings across three informative regions of the structure-activity landscape.

Conclusions:

• ADMET property analysis can be effectively integrated into activity landscape modeling.
• Understanding the relationship between structural changes and ADMET violations is key for lead optimization.
• This approach aids in identifying compounds with desirable activity and drug-like properties.