Structure-based predictions of activity cliffs.

Area of Science:

• Computational Chemistry
• Drug Discovery
• Medicinal Chemistry

Background:

• Drug discovery assumes molecular similarity correlates with similar activity.
• Activity cliffs, where structurally similar compounds show vastly different potencies, challenge this assumption.
• Accurate prediction of activity cliffs is vital for efficient drug development.

Purpose of the Study:

• To evaluate the reliability of ligand docking and virtual screening in predicting activity cliffs.
• To assess structure-based methods for identifying discontinuities in molecular activity landscapes.

Main Methods:

• Utilized a diverse database of cocrystals known to form activity cliffs.
• Performed ligand docking and virtual ligand screening calculations.
• Simulated progressively more realistic scenarios, starting from ideal conditions.

Main Results:

• Ensemble- and template-docking methods demonstrated significant predictive accuracy for activity cliffs.
• Structure-based approaches proved reliable despite limitations of scoring functions.
• Established baseline performance in ideal scenarios before moving to realistic simulations.

Conclusions:

• Advanced structure-based methods, including ensemble and template docking, can reliably predict activity cliffs.
• These computational approaches offer valuable tools for drug discovery, overcoming limitations of traditional methods.
• Accurate prediction of activity cliffs enhances the efficiency and success rate of drug development.