  Structure-based drug design (SBDD) stands at the forefront of drug discovery,
emphasizing the creation of molecules that target specific binding pockets.
Recent advances in this area have witnessed the adoption of deep generative
models and geometric deep learning techniques, modeling SBDD as a conditional
generation task where the target structure serves as context. Historically,
evaluation of these models centered on docking scores, which quantitatively
depict the predicted binding affinity between a molecule and its target pocket.
Though state-of-the-art models purport that a majority of their generated
ligands exceed the docking score of ground truth ligands in test sets, it begs
the question: Do these scores align with real-world biological needs? In this
paper, we introduce the delta score, a novel evaluation metric grounded in
tangible pharmaceutical requisites. Our experiments reveal that molecules
produced by current deep generative models significantly lag behind ground
truth reference ligands when assessed with the delta score. This novel metric
not only complements existing benchmarks but also provides a pivotal direction
for subsequent research in the domain.
