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新的BOBa框架优化海量化学空间中的候选选择

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了BOBa，一个新颖的、由多臂老虎机引导的代理优化框架，旨在解决从海量离散空间（如药物发现）中识别高价值候选者的计算挑战。该框架通过在不同动作空间分区之间自适应地分配计算资源，消除了对全库代理推断的需求。通过将这些分区视为多臂老虎机中的臂，BOBa将推断和评估集中在最有希望的分区上，同时确保原则性的探索，从而在筛选性能和计算成本之间提供可调的权衡。 AI

影响 该框架可以通过提高搜索海量化学或分子库的效率来加速科学发现。

排序理由 该集群包含一篇详细介绍优化问题新计算框架的研究论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的BOBa框架优化海量化学空间中的候选选择

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Mohammad Haddadnia, Yuvan Chali, Abhilash Jayaraj, Constance Kraay, Joana Reis, Felix Strieth-Kalthoff, Haribabu Arthanari ·

    面向化学空间的靶点感知老虎机分配用于可扩展代理优化

    arXiv:2606.26657v1 Announce Type: new Abstract: Identifying high-utility candidates from massive discrete spaces under expensive evaluations is a recurring challenge across the sciences, with structure-based drug discovery as a prominent example. While surrogate-based optimizatio…

  2. arXiv cs.LG TIER_1 English(EN) · Haribabu Arthanari ·

    面向化学空间的靶点感知多臂老虎机分配用于可扩展代理优化

    Identifying high-utility candidates from massive discrete spaces under expensive evaluations is a recurring challenge across the sciences, with structure-based drug discovery as a prominent example. While surrogate-based optimization can increase sample efficiency by reducing the…

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