Structure-Based Predictions of Activity Cliffs

In drug discovery, it is generally accepted that neighboring molecules in a given descriptor's space display similar activities. However, even in regions that provide strong predictability, structurally similar molecules can occasionally display largo differences in potency. In QSAR jargon, these discontinuities in the activity landscape are known as activity cliffs. In this study, we assessed the reliability of ligand docking and virtual ligand screening schemes in predicting activity cliffs. We performed our calculations on a diverse, independently collected database of cliff-forming cocrystals. Starting from ideal Situations, which allowed us to establish our baseline, we progressively moved toward Simulating more realistic scenarios. Ensemble- and template-docking achieved a significant level of accuracy, suggesting that, despite the well-known limitations of empirical scoring Schemes, activity cliffs can be accurately predicted by advanced structure-based methods.