发表机构
Institute for Theoretical Physics, Heidelberg University; Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University（海德堡大学理论物理研究所; 海德堡大学跨学科科学计算中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出离散去噪扩散模型juniper，以辛醇-水分配自由能为条件实现跨化学空间的粗粒化珠子到分子的组成反向映射，生成分子有效性达93%、独特性达92%，分布与目标自由能线性相关。
AI 中文摘要
可迁移粗粒化（CG）力场压缩了化学空间：通过将原子聚合成一组简化的相互作用珠子，如MARTINI这类模型可将可区分化合物的数量减少约三个数量级，使软物质热力学性质的高通量筛选变得可行，药物-膜渗透性就是一个成熟的例子。这种压缩是有损的，且目前是单向的：筛选返回一组珠子组合，但没有确定的途径回到其代表的化合物。恢复这些化合物的组成反向映射是一个一对多的逆映射，与研究更充分的、从已知映射重建原子坐标的构象问题不同。本文中，我们将组成反向映射表述为条件图生成，引入juniper，这是一种基于分子图的离散去噪扩散模型，以辛醇-水分配自由能ΔG_W→O为条件，该自由能是MARTINI珠子类型分配的主要驱动因素，因此是珠子身份的代理。juniper在训练时使用最多含9个重原子且映射到1或2个珠子的分子，对于双珠子目标，其生成的分子有效性达93%，独特性达92%，且其ΔG_W→O分布与目标CG ΔG_W→O呈线性相关（R²≥0.96），仅在疏水和亲水尾部有偏差。尽管模型仅接收单个标量之外的化学信息，官能团仍会随施加的自由能系统地变化，从非极性端的支链烃到极性端的酰胺、酰亚胺和异氰酸酯。因此，粗粒化筛选标记的珠子组合可转化为候选分子，用于原子级研究或合成。
英文摘要
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 magnitude, making high-throughput screening of thermodynamic properties tractable across soft matter, with drug--membrane permeability as a well-developed example. The compression is lossy and, so far, one-way: a screen returns a combination of beads, with no established route back to the compounds it stands for. Recovering those compounds--compositional backmapping--is a one-to-many inverse map, distinct from the better-studied conformational problem of rebuilding atomic coordinates from a known mapping. Here we formulate compositional backmapping as conditional graph generation by introducing juniper, a discrete denoising diffusion model over molecular graphs conditioned on the octanol--water partition free energy $ΔG_{\mathrm{W} \mapsto \mathrm{O}}$, the principal driver of MARTINI bead type assignment and hence a proxy for bead identity. Trained on molecules of up to 9 heavy atoms mapped onto one or two beads, juniper generates molecules that are 93\% valid and 92\% unique for two-bead targets, and whose $ΔG_{\mathrm{W} \mapsto \mathrm{O}}$ distributions track the target $ΔG^{\mathrm{CG}}_{\mathrm{W} \mapsto \mathrm{O}}$ linearly ($r^{2} \geq 0.96$), departing only in the hydrophobic and hydrophilic tails. Although the model receives no chemical information beyond a single scalar, the functional groups shift systematically with the imposed free energy, from branched hydrocarbons at the apolar end to amides, imides, and isocyanates at the polar end. A bead combination flagged by a CG screen can therefore be turned into candidate molecules for atomistic study or synthesis.
