Retrosynthesis, the process of deconstructing a target molecule into simpler precursors, is crucial for computer-aided synthesis planning (CASP). Widely adopted tree-search methods often suffer from exponential computational complexity. In this work, we introduce FragmentRetro, a novel retrosynthetic method that leverages fragmentation algorithms, specifically BRICS and r-BRICS, combined with stock-aware exploration and pattern fingerprint screening to achieve quadratic complexity. FragmentRetro recursively combines molecular fragments and verifies their presence in a building block set, providing sets of fragment combinations as retrosynthetic solutions. We present the first formal computational analysis of retrosynthetic methods, showing that tree search exhibits exponential complexity $O(b^h)$, DirectMultiStep scales as $O(h^6)$, and FragmentRetro achieves $O(h^2)$, where $h$ represents the number of heavy atoms in the target molecule and $b$ is the branching factor for tree search. Evaluations on PaRoutes, USPTO-190, and natural products demonstrate that FragmentRetro achieves high solved rates with competitive runtime, including cases where tree search fails. The method benefits from fingerprint screening, which significantly reduces substructure matching complexity. While FragmentRetro focuses on efficiently identifying fragment-based solutions rather than full reaction pathways, its computational advantages and ability to generate strategic starting candidates establish it as a powerful foundational component for scalable and automated synthesis planning.

翻译：逆合成是将目标分子解构为更简单前体的过程，对计算机辅助合成规划至关重要。广泛采用的树搜索方法常受指数级计算复杂度制约。本文提出FragmentRetro——一种基于碎片化算法（特别是BRICS与r-BRICS）的新型逆合成方法，结合库存感知探索与模式指纹筛选实现二次复杂度。该方法通过递归组合分子碎片并验证其在构建块集中的存在性，以碎片组合集合形式提供逆合成解。我们首次对逆合成方法进行形式化计算分析，证明树搜索具有指数复杂度$O(b^h)$，DirectMultiStep为$O(h^6)$，而FragmentRetro达到$O(h^2)$，其中$h$表示目标分子的重原子数，$b$为树搜索分支因子。在PaRoutes、USPTO-190及天然产物数据集上的评估表明，FragmentRetro在保持竞争力的运行时间内实现高求解率，包括树搜索失效的案例。指纹筛选机制显著降低了子结构匹配复杂度。虽然FragmentRetro侧重于高效识别基于碎片的解而非完整反应路径，但其计算优势与生成战略起始候选物的能力，使其成为可扩展自动化合成规划的强大基础组件。