新的MLIPDiff方法通过确保物理合理性来改进AI药物设计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了MLIPDiff，一种用于改进基于结构的药物设计中的去噪扩散模型的新方法。该技术使用来自UMA（通用原子模型）的机器学习原子间势来指导扩散过程，确保生成的配体姿态在物理上是合理的，并避免与蛋白质发生碰撞。MLIPDiff通过消除碰撞，同时保留局部配体几何形状并实现优于预测的结合亲和力，显著优于现有模型，而且无需对基础扩散模型进行额外训练。 AI

影响通过生成更符合物理现实的分子结构，增强了AI在药物发现中的能力。

排序理由该集群描述了一篇详细介绍AI驱动的药物设计新方法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yiyan Liao, Amin Tavakoli, Yu Zhang, Anima Anandkumar · 2026-10-28 04:00

MLIPDiff：通过机器学习的原子间势导引去噪扩散模型用于基于结构的药物设计

arXiv:2610.33315v1 Announce Type: new Abstract: Diffusion models have become a dominant approach to structure-based drug design. However, because they are trained primarily to fit reference structures, they often place ligands in physically implausible poses that clash with the p…

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MLIPDiff：通过机器学习的原子间势导引去噪扩散模型用于基于结构的药物设计

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