发表机构
Department of Computer Science and Engineering; Chalmers University of Technology; University of Gothenburg; Molecular AI Discovery Sciences, R&D（计算机科学与工程系; 楚姆勒技术大学; 哥德堡大学; 分子AI发现科学，研发）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对分子设计中3D属性是玻尔兹曼分布期望的问题，提出基于解耦退火流的玻尔兹曼期望设计方法，通过交替两个条件流模型优化分子图，以系综统计为目标，实验验证其有效性，还可扩展到多目标及更高阶矩设计。
AI 中文摘要
与分子设计相关的大多数3D属性，包括自由能和形状描述符，都是分子图3D构型上玻尔兹曼分布的期望。现有属性引导生成模型将每个属性与单个结构绑定，忽略了潜在系综。本文将3D分子设计重塑为玻尔兹曼期望设计，并通过解耦退火流（DECAF）实现。它将图和坐标上的联合分布分解为两个条件流模型：图条件流和坐标条件流。通过交替这两个流，DECAF以模拟退火接受规则优化分子图，以系综统计而非单构象属性为设计目标。在GEOM-Drugs上的实验表明，系综感知优化能使分子的回转半径和溶剂可及表面积均值向目标移动，而单构象优化在较大类药物分子上效果不佳。DECAF还可扩展到多目标权衡以及更高阶矩设计，并通过全原子MD模拟验证了更高阶矩设计的构象分布。
英文摘要
Most 3D properties relevant to molecular design, including free energies and shape descriptors, are $\textit{expectations}$ over the Boltzmann distribution over 3D configurations of a molecular graph. However, existing property-guided generative models tie each property to a single structure, ignoring the underlying ensemble. We recast 3D molecular design as $\textbf{Boltzmann-expected design}$ and realise it with $\textbf{DECAF}$ (Decoupled Annealing Flows), which factorise the joint distribution over graphs and coordinates into two conditional flow models: a graph-conditioned flow $p(x\mid\mathcal{G})$, acting as a $\textit{Boltzmann emulator}$, and a coordinate-conditioned flow $p(\mathcal{G}\mid x)$, proposing new graphs from 3D information. By alternating the two flows, DECAF optimises molecular graphs with a simulated-annealing acceptance rule whose scoring function is evaluated on ensembles drawn from $p(x\mid\mathcal{G})$, making ensemble statistics, not single-conformer properties, the design target. The resulting loop requires no retraining to change objectives. On GEOM-Drugs, we show that ensemble-aware optimisation produces graphs whose mean radius of gyration and solvent-accessible surface area consistently shift toward targets, while single-conformer optimisation degrades on larger drug-like molecules where Boltzmann distributions are broadest. DECAF extends to multi-objective trade-offs and, uniquely among 3D generative models, to $\textbf{higher-moment design}$: jointly optimising an ensemble property's variance and skewness to produce flexible molecules biased to a prescribed conformational regime: we verify the conformational distributions of these higher-moment designs with all-atom MD simulations.
