发表机构
Rice University（莱斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出建模可量化性的框架，基于初筛特征预测后续剂量反应分析的效价可量化性，实现精准分流以优化药物筛选的资源分配。
AI 中文摘要
高通量药物筛选依赖低成本的初筛实验来优先选择化合物，以开展成本更高的剂量反应分析，而后者最终会对效价进行量化。当前的筛选策略主要聚焦于识别在后续实验中能确认生物活性的化合物，默认假设确认的活性也能产生可用的效价估计值。然而，筛选中确认的生物活性并不一定能转化为可量化的效价，因为活性化合物仍可能无法产生可报告的剂量反应估计值。因此，我们提出一个用于建模可量化性的框架，即后续测试是否会产生可用的效价估计值，将其作为与生物活性不同的单独分流目标。可量化性可通过此前的低成本筛选结果进行强预测，大部分预测信息来自观测到的筛选特征而非分子结构。基于反应的预测因子在未见过的化学骨架上仍保持稳健性，并在保留的实验机制家族间实现泛化，同时成功量化的概率随反应幅度和实验背景发生强烈变化。这些发现确立了与生物活性不同的实验可测量性是筛选结果的可预测属性，且表明感知可量化性的分流可优化成本高昂的剂量反应分析资源分配。
英文摘要
High-throughput drug screening relies on low-cost primary assays to prioritize compounds for more expensive dose-response profiling, where potency is ultimately quantified. Current screening strategies largely focus on identifying compounds that will confirm biological activity on follow-up, implicitly assuming that confirmed activity will also yield a usable potency estimate. However, confirmed biological activity in screening does not necessarily translate into a quantifiable potency, because active compounds can still fail to produce a reportable dose-response estimate. We therefore present a framework for modeling quantifiability, whether follow-up testing will yield a usable potency estimate, as a distinct triage objective from biological activity. Quantifiability was strongly predictable from the preceding low-cost screen, with most predictive information arising from the observed screening features rather than molecular structure. Response-based predictors remained robust on previously unseen chemical scaffolds and generalized across held-out assay-mechanism families, while the probability of successful quantification varied strongly with response amplitude and assay context. These findings establish experimental measurability, distinct from biological activity, as a predictable property of screening outcomes and show that quantifiability-aware triage can improve the allocation of costly dose-response profiling capacity.
