English(EN) Predicting Quantifiability from Primary Screens to Prioritize Dose-Response Profiling

AI模型根据筛选数据预测药物化合物的可量化性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-28 04:00

研究人员开发了一个框架，用于预测在药物发现中筛选出的化合物是否能产生可量化的效力估计值。这种“可量化性”不同于生物活性，可以从初筛数据中预测，大部分预测信息来自观察到的筛选特征而非分子结构。该模型在不同的化学骨架和检测类型中均表现出稳健性，表明根据预测的可量化性来优先排序化合物可以优化昂贵的剂量-效应分析的资源分配。 AI

影响该框架可以通过更好地分配化合物测试资源来提高药物发现的效率。

排序理由该集群包含一篇学术论文，详细介绍了药物发现的新预测框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Sean Lim · 2026-08-28 04:00

从初筛预测可量化性以优先进行剂量-反应分析

arXiv:2608.26538v1 Announce Type: new Abstract: 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 c…

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从初筛预测可量化性以优先进行剂量-反应分析

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