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English(EN) D2ML: Models for Direct-to-Biology Small Molecule Screening

新的D2ML模型增强了直接生物筛选小分子的能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为D2ML的新机器学习方法，用于直接生物筛选小分子。该方法旨在应对D2B数据的独特挑战，例如不确定的浓度，以改进预测并指导发现更有效的化合物。D2ML模型即使在粗略的剂量反应分析中，也能从单点筛选和跨靶点的相对效力进行预测。在实验中，D2ML指导选择了新的化合物，与现有分子相比，这些化合物表现出更高的效力和选择性。 AI

影响 通过提高小分子筛选的效率和有效性，增强了AI在药物发现中的作用。

排序理由 该集群包含一篇详细介绍新机器学习模型和方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的D2ML模型增强了直接生物筛选小分子的能力

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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · AkshatKumar Nigam, Neri Amara, Hillary Dequina, Dyana Kenanova, Seth Malmersjo, Fiorella Ruggiu, Raymond V. Fucini, Jack Sadowsky, Stig K. Hansen, Imran S. Haque ·

    D2ML: Models for Direct-to-Biology Small Molecule Screening

    arXiv:2610.41070v1 Announce Type: cross Abstract: Direct-to-biology (D2B) screening enables rapid testing of newly synthesized molecules without purification. However, uncertain concentrations in crude mixtures and limited purified-compound measurements complicate the use of thes…

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