  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.
