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English(EN) CliffRank: A Dual-Branch Framework for Activity-Cliff Ranking Prediction

CliffRank框架改进了活性-崖值排序预测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了CliffRank，一个新颖的双分支框架，旨在改进化学化合物活性-崖值排序的预测。该方法结合了绝对活性回归和排序一致性学习，利用阈值列表损失和成对偏好一致性（PPC）来更好地利用可用的活性数据。在抗菌肽和小分子数据集上进行测试时，CliffRank表现强劲，实现了高Spearman相关性和有竞争力的Recall@50分数，尽管最佳配置因数据集和模型骨干的不同而异。 AI

影响 引入了一个用于预测化学活性崖值的新颖框架，有望改进药物发现和材料科学。

排序理由 该集群包含一篇详细介绍活性-崖值排序预测新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

CliffRank框架改进了活性-崖值排序预测

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该集群包含一篇详细介绍活性-崖值排序预测新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kewei Li, Rongying Zhang, Peiyu Yang, Zhongjian Wang, Qiuchen Zhao, Lan Huang, Fengfeng Zhou ·

    CliffRank：用于活动-悬崖排名预测的双分支框架

    arXiv:2609.01673v1 Announce Type: cross Abstract: Activity-cliff ranking remains difficult because local structural changes can cause large activity differences, while high-quality data that resolve the underlying mechanisms remain limited. To use available activity labels more e…

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