English(EN) Molecular Embedding-Based Algorithm Selection in Protein-Ligand Docking

新的MolAS模型改进了蛋白质-配体对接算法选择

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-02 04:00

研究人员开发了MolAS，一个旨在改进蛋白质-配体对接算法选择的新模型。MolAS利用预训练的蛋白质和配体嵌入来预测不同对接方法的性能，在单一最佳求解器方面取得了显著改进。该模型的有效性与其在特定工作流程中求解器排名的稳定性相关，表明其可用作固定流程选择器和评估对接问题适定性的诊断工具。 AI

影响通过优化蛋白质-配体对接的算法选择，增强了计算生物学工具。

排序理由这是一篇详细介绍特定科学任务新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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

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arXiv cs.LG TIER_1 English(EN) · Jiabao Brad Wang, Siyuan Cao, Hongxuan Wu, Yiliang Yuan, Mustafa Misir · 2026-06-02 04:00

基于分子嵌入的蛋白质-配体对接算法选择

arXiv:2512.02328v2 Announce Type: replace-cross Abstract: Selecting an effective docking algorithm is highly context-dependent, and no single method performs reliably across structural, chemical, and protocol regimes. MolAS is a lightweight algorithm-selection model that predicts…