  Recent advancements in structure-based drug design (SBDD) have significantly
enhanced the efficiency and precision of drug discovery by generating molecules
tailored to bind specific protein pockets. Despite these technological strides,
their practical application in real-world drug development remains challenging
due to the complexities of synthesizing and testing these molecules. The
reliability of the Vina docking score, the current standard for assessing
binding abilities, is increasingly questioned due to its susceptibility to
overfitting. To address these limitations, we propose a comprehensive
evaluation framework that includes assessing the similarity of generated
molecules to known active compounds, introducing a virtual screening-based
metric for practical deployment capabilities, and re-evaluating binding
affinity more rigorously. Our experiments reveal that while current SBDD models
achieve high Vina scores, they fall short in practical usability metrics,
highlighting a significant gap between theoretical predictions and real-world
applicability. Our proposed metrics and dataset aim to bridge this gap,
enhancing the practical applicability of future SBDD models and aligning them
more closely with the needs of pharmaceutical research and development.
