English(EN) Recovering molecules from coarse-grained beads: free-energy-conditioned generative backmapping across chemical space

新AI模型从粗粒化表示中重建分子

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 04:00

研究人员开发了一种新的组合反映射方法，该方法可以从粗粒化（CG）表示中重建分子。该技术使用一种离散去噪扩散模型Juniper，该模型以辛醇-水分配自由能（$\Delta G_{\mathrm{W} \mapsto \mathrm{O}}$）为条件。该模型在映射到一个或两个珠子的分子上进行训练，生成有效且独特的分子，其分布紧密跟踪目标自由能。这一进展使得CG筛选结果能够转化为候选分子，用于进一步研究或合成。 AI

影响能够将粗粒化模拟结果转化为可用于合成或原子尺度研究的特定分子候选。

排序理由该集群包含一篇arXiv预印本，详细介绍了一种用于科学研究问题的新AI模型。[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) · Luis Itza Vazquez-Salazar, Tristan Bereau · 2026-09-07 04:00

从粗粒化珠子中恢复分子：跨化学空间的自由能条件生成反向映射

arXiv:2609.04432v1 Announce Type: cross Abstract: Transferable coarse-grained (CG) force fields compress chemical space: by aggregating atoms into a reduced set of interaction beads, models such as MARTINI reduce the number of distinguishable compounds by roughly three orders of …

本文如何被排名

报道来源 [1]

从粗粒化珠子中恢复分子：跨化学空间的自由能条件生成反向映射

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