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.

通过评估生成分子与已知活性化合物的相似性，引入基于虚拟筛选的实际部署能力度量，以更严格地重新评估结合亲和性，我们提出了一个综合评估框架来解决结构基药物设计在实际应用中的限制，研究结果显示，尽管当前的结构基药物设计模型在 Vina 评分上表现出色，但在实际可用性指标上存在不足，突显了理论预测与实际应用之间的巨大差距，我们提出的指标和数据集旨在填补这一差距，提高未来结构基药物设计模型的实际适用性，使其更贴合制药研究与开发的需求。