提出六阶段筛选框架，精准评估药物生成模型真实可用性。
HEDGEHOG: Hierarchical Evaluation of Drug Generators Through Rigorous Filtration
- 构建六阶段过滤流程，模拟工业药物筛选真实路径
- 23万分子中仅0.65%通过全部筛选，暴露生成模型缺陷
- 适合药物发现、生成模型评估研究人员参考
生成式分子模型可通过从头设计新候选化合物来支持早期药物研发。实际应用中，有效候选需兼顾靶点活性、合成可行性、理化性质等多重设计约束。然而，现有评估指标难以反映生成分子在药物化学上的合理性及下游计算适用性，易导致评估误判、错误假设和计算资源浪费。本文提出HEDGEHOG，一种受工业先导化合物识别流程启发的统一六阶段过滤基准：(i) 预处理；(ii) 理化描述符筛选；(iii) 结构警报与图合理性检查；(iv) 合成可行性评估；(v) 分子对接与结合亲和力估计；(vi) 三维构象与相互作用验证。我们在标准化协议下对三类共23个分子生成模型进行评估。在23万生成分子中，仅有0.65%通过所有阶段。结果揭示当前分子生成模型的核心局限：看似满足单一条件的分子，极少能同时通过药物化学、合成性、对接及三维构象筛选。
原文摘要 · Abstract (English)
Generative molecular models can support early drug discovery by proposing new candidate compounds de novo. In practice, useful candidates must balance target-relevant activity, synthetic accessibility, physicochemical properties, and other multiparameter design constraints. However, metrics commonly used to evaluate molecular generators only weakly reflect whether the generated compounds are medicinally plausible and suitable for downstream computation. This can produce false positives in model evaluation, incorrect assumptions, and inefficient use of computational resources. We introduce HEDGEHOG, a unified six-stage filtration benchmark that is inspired by industrial hit identification workflows: (i) preprocessing; (ii) physicochemical descriptor screening; (iii) structural alerts and graph-sanity checks; (iv) synthesis feasibility; (v) docking and binding affinity estimation; and (vi) three-dimensional pose and interaction checks. We evaluate 23 molecular generators across three model classes under a standardized protocol. Across 230,000 generated molecules, only 0.65% of initial molecules survive all stages. Our results expose a central limitation of current molecular generators: molecules that appear acceptable under isolated criteria rarely satisfy medicinal chemistry, synthesis, docking, and 3D pose filters simultaneously.
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