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English(EN) Fixed-Dimensional Latent Flow for Generating Variable-Size 3D Molecules

AI 模型推动 3D 分子生成以用于药物发现

作者 PulseAugur 编辑部 · [3 个来源] ·

两篇新研究论文介绍了用于 3D 分子生成的先进 AI 模型。第一篇，Equivariant-Free Transformer-Autoencoded Latent Flow Matching (EF-TALFM)，使用固定维度的潜在表示来生成可变尺寸的分子，并在 PCQM4Mv2 等基准测试中实现了高新颖性和计算吞吐量。第二篇，NEAT-POCKET，是 NEAT 模型的扩展，可在蛋白质结合口袋内逐个原子生成分子，为 CrossDocked 和 SPINDR 等数据集上的药物发现提供了更快的采样和片段完成能力。 AI

影响 AI 驱动的分子生成领域的这些进步可以显著加速药物发现和先导化合物优化过程。

排序理由 arXiv 上发表了两篇研究论文，详细介绍了用于分子生成的新 AI 模型。

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

AI 模型推动 3D 分子生成以用于药物发现

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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Weichi Yao, Cameron Gruich, Bryan R. Goldsmith, Yixin Wang ·

    用于生成可变尺寸 3D 分子的固定维度潜在流

    arXiv:2609.08333v1 Announce Type: cross Abstract: In molecular discovery, molecule size is coupled to composition, structure, and other target properties. Yet most 3D generators require molecule size to be specified before generation. Here, we introduce Equivariant-Free Transform…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    用于生成可变尺寸 3D 分子的固定维度潜在流

    In molecular discovery, molecule size is coupled to composition, structure, and other target properties. Yet most 3D generators require molecule size to be specified before generation. Here, we introduce Equivariant-Free Transformer-Autoencoded Latent Flow Matching, a two-stage g…

  3. arXiv cs.AI TIER_1 English(EN) · Roxane Axel Jacob, Daniel Rose, Thierry Langer, Johannes Kirchmair ·

    NEAT-POCKET：具有邻域引导集变换器的口袋条件自回归三维分子生成

    arXiv:2609.05097v1 Announce Type: cross Abstract: AI-driven de novo molecular design offers a promising route to accelerate early-stage drug discovery by generating novel ligands directly within target protein binding pockets. We present NEAT-POCKET, a pocket-conditioned extensio…

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