English(EN) CausalBind: Causal Modeling and Learning for Protein-Molecule Virtual Screening

CausalBind 使用因果建模进行蛋白质-分子虚拟筛选

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员开发了 CausalBind，一种利用因果建模和学习的蛋白质-分子虚拟筛选新方法。该方法旨在识别和利用蛋白质与分子之间稀疏的相互作用模式，超越可能将不变的结合决定因素与无关紧要的相关性纠缠在一起的密集整体对齐。理论结果表明，揭示这些稀疏相互作用对于泛化至关重要，而 CausalBind 的变体在 DUD-E 和 LIT-PCBA 基准测试中表现出色，尤其是在早期富集和分布外泛化方面。 AI

影响引入了一种新颖的因果建模方法，可以提高药物发现中虚拟筛选的准确性和泛化能力。

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arXiv cs.AI TIER_1 English(EN) · Loka Li, Jin Tian, Kun Zhang · 2026-10-07 04:00

CausalBind：用于蛋白质-分子虚拟筛选的因果建模与学习

arXiv:2610.07340v1 Announce Type: cross Abstract: Protein-molecule virtual screening is increasingly cast as a problem of representation learning in a shared embedding space. Existing methods rely on dense holistic alignment, entangling invariant binding determinants with nuisanc…

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CausalBind：用于蛋白质-分子虚拟筛选的因果建模与学习

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