Delta Score: Improving the Binding Assessment of Structure-Based Drug Design Methods
This addresses the need for more biologically relevant evaluation in drug discovery, though it is incremental as it adds a complementary metric rather than a new method.
The paper tackles the problem of evaluating structure-based drug design methods by introducing the delta score, a new metric based on pharmaceutical requirements, and finds that current deep generative models perform significantly worse than ground truth ligands when assessed with it.
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.