用流模型实现分子粗粒度到原子级的精确回映射
Split-Flows: Measure Transport and Information Loss Across Molecular Resolutions
- 将回映射视为跨分辨率的概率传输过程，建立直接概率关联
- 首次可计算映射熵，量化粗粒度导致的信息损失
- 适用于蛋白质、脂质膜等系统，适合需要高精度建模的研究者
通过降低分辨率，粗粒度模型大幅加速分子模拟，使长时序现象成为可能，但牺牲了微观细节。恢复这些原子级信息对依赖原子精度的任务至关重要，因此回映射成为分子建模的核心挑战。我们提出split-flows，一种基于流的新方法，将回映射重新诠释为跨分辨率的连续时间测度传输。与现有生成策略不同，split-flows在分辨率间建立直接概率联系，支持原子结构的高表达性条件采样，并首次提供计算映射熵的可行路径——这一信息论指标可衡量粗粒度过程中不可逆损失的细节。我们在多种分子系统（包括chignolin、脂质双层和丙氨酸二肽）上验证了该方法，证明其在精确回映射与系统评估粗粒度模型方面的潜力。
原文摘要 · Abstract (English)
By reducing resolution, coarse-grained models greatly accelerate molecular simulations, unlocking access to long-timescale phenomena, though at the expense of microscopic information. Recovering this fine-grained detail is essential for tasks that depend on atomistic accuracy, making backmapping a central challenge in molecular modeling. We introduce split-flows, a novel flow-based approach that reinterprets backmapping as a continuous-time measure transport across resolutions. Unlike existing generative strategies, split-flows establish a direct probabilistic link between resolutions, enabling expressive conditional sampling of atomistic structures and -- for the first time -- a tractable route to computing mapping entropies, an information-theoretic measure of the irreducible detail lost in coarse-graining. We demonstrate these capabilities on diverse molecular systems, including chignolin, a lipid bilayer, and alanine dipeptide, highlighting split-flows as a principled framework for accurate backmapping and systematic evaluation of coarse-grained models.
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