Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion
This addresses a critical gap in drug design for pharmaceutical applications, though it is an incremental improvement over existing generative models.
The paper tackled the problem of low specificity in structure-based drug design, where generated molecules bind non-specifically to many proteins, by introducing a Delta Score metric and an energy-guided diffusion method, resulting in improved specificity while maintaining docking scores.
In the field of Structure-based Drug Design (SBDD), deep learning-based generative models have achieved outstanding performance in terms of docking score. However, further study shows that the existing molecular generative methods and docking scores both have lacked consideration in terms of specificity, which means that generated molecules bind to almost every protein pocket with high affinity. To address this, we introduce the Delta Score, a new metric for evaluating the specificity of molecular binding. To further incorporate this insight for generation, we develop an innovative energy-guided approach using contrastive learning, with active compounds as decoys, to direct generative models toward creating molecules with high specificity. Our empirical results show that this method not only enhances the delta score but also maintains or improves traditional docking scores, successfully bridging the gap between SBDD and real-world needs.