From activity cliffs to target-specific scoring models and pharmacophore hypotheses.

Area of Science:

• Computational chemistry and drug discovery
• Structural biology and medicinal chemistry

Background:

• Activity cliffs, where small structural changes cause large activity differences, complicate Quantitative Structure-Activity Relationship (QSAR) modeling.
• Ligand-based QSAR methods often struggle with predictive power in the presence of activity cliffs.

Purpose of the Study:

• To introduce a novel approach for identifying structure-based activity cliffs (ISAC).
• To leverage activity cliff information for structure-based drug design and analysis of protein-ligand interactions.

Main Methods:

• Analysis of interaction energies in protein-ligand complexes to identify activity cliffs.
• Development of the Identification of Structure-based Activity Cliffs (ISAC) method.
• Visualization of protein active site 'hot spots' based on the frequency of atom involvement in activity cliff events.

Main Results:

• The ISAC approach effectively identifies structure-based activity cliffs and visualizes key interacting atoms (hot spots) in protein binding sites.
• ISAC facilitates the development of pharmacophore hypotheses and target-specific scoring functions.
• Virtual screening experiments using the ISAC-derived approach demonstrated improved enrichment compared to generic scoring functions.

Conclusions:

• The ISAC method provides valuable insights into structure-activity relationships by analyzing activity cliffs.
• Hot spot visualization aids medicinal chemists in early-stage drug discovery and lead optimization.
• Activity-cliff-based scoring functions enhance virtual screening performance for various protein targets.