针对难成药靶点，构建了5780个浅口袋靶点基准测试集。
ShallowBench: Benchmarking Generative Drug Design Models on Shallow-Pocket Targets
- 从CrossDocked2020中提取浅口袋靶点，用阿尔法形状体积差筛选低凹度结构。
- 评估显示现有生成模型在低凹度界面预测结合亲和力显著下降。
- 适合关注药物设计生成模型局限性及新架构研发的研究者。
尽管生成式AI在基于结构的药物设计中取得显著进展，但其主要依赖深口袋靶点，难以有效采样适用于挑战性低口袋性靶点（如历史上“不可成药”的癌症靶点KRAS和MYC）的配体。为填补这一空白，我们提出了ShallowBench，一个严格筛选的5,780个浅口袋靶点基准数据集，源自CrossDocked2020。通过计算阿尔法形状“顶盖”体积与蛋白质原子体素体积之差，成功分离出低凹度靶点，同时确保足够的结合表面积。对多种先进生成模型的评估表明，这些模型在低凹度界面的预测结合亲和力明显较弱。ShallowBench为生成生物学模型提供了严格的基准测试，并凸显了开发新型网络架构或损失函数以应对此类挑战靶点的必要性。
原文摘要 · Abstract (English)
While generative AI models have demonstrated remarkable success in structure-based drug design, they predominantly rely on deep binding pockets and struggle to sample effective ligands for challenging low-pocketability targets, such as the historically "undruggable" oncology targets KRAS and MYC. To address this gap, we introduce ShallowBench, a strictly curated benchmark of 5,780 shallow-pocket targets extracted from CrossDocked2020. By computing the difference between an Alpha Shape "lid" volume and the underlying protein atom voxel volume, we successfully isolated targets with low concavity while ensuring sufficient surface area for binding. Evaluating various state-of-the-art generative models reveals weaker predicted binding affinity on these low-concavity interfaces. ShallowBench therefore provides a rigorous benchmark for generative biology models and highlights the necessity of new architectural innovations or loss functions capable of navigating these challenging targets.
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