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English(EN) Cross-Modality Controlled Molecule Generation with Diffusion Language Model

新框架赋能药物发现的可控分子生成

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个名为“基于扩散语言模型的跨模态可控分子生成”（CMCM-DLM）的新框架，用于生成具有特定性质的分子。这种模块化方法扩展了预训练的扩散模型，使其能够处理各种约束，而无需重新训练整个模型。CMCM-DLM采用分阶段设计，其中结构控制模块用于建立分子骨架，属性控制模块用于指导生成过程以获得所需的化学性质，并在药物发现应用中证明了其有效性。 AI

影响 该框架通过实现更精确、更高效地生成具有所需性质的新型分子，有望加速药物发现。

排序理由 该集群包含一篇详细介绍新分子生成方法的论文。[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 →

新框架赋能药物发现的可控分子生成

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该集群包含一篇详细介绍新分子生成方法的论文。[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) · Yunzhe Zhang, Yifei Wang, Khanh Vinh Nguyen, Pengyu Hong ·

    Cross-Modality Controlled Molecule Generation with Diffusion Language Model

    arXiv:2508.14748v2 Announce Type: replace-cross Abstract: The increasing variety of molecular data creates a need for generative models that can flexibly incorporate heterogeneous constraints across modalities. However, existing SMILES-based diffusion models are typically designe…

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