让合成路线规划同时优化成本、毒性等多目标，更贴近真实化工决策。
From Feasible to Practical: Pareto-Optimal Synthesis Planning
- 用多目标搜索生成多种权衡方案，而非单一最优路径。
- 在多个基准上生成高质量帕累托前沿，发现传统方法忽略的可行解。
- 适合工业界做综合成本与安全评估的合成路线设计。
当前计算机辅助合成规划（CASP）方法一旦找到一条可行路线就认为问题已解决，主要关注收敛性或最短路径指标。这种做法与实际化学工作脱节，因化学家需平衡成本、可持续性、毒性及总产率等多重目标。为此，本文将合成规划建模为多目标搜索问题，提出MORetro*算法，通过加权标量化与贝叶斯优化引导采样，在组合搜索空间中高效寻找用户定义标准间的帕累托前沿。基于多目标A*搜索，该算法在单步模型可接受条件下，保证恢复真实帕累托前沿。在多个逆合成基准测试中，MORetro*生成了多样化且高质量的帕累托前沿，揭示了单目标方法遗漏的解决方案，使CASP输出更符合工业决策需求。
原文摘要 · Abstract (English)
Current computer-aided synthesis planning (CASP) methods often treat retrosynthesis as solved once a single feasible route is identified, focusing primarily on convergence or shortest-path metrics. This view is misaligned with real-world practice, where chemists must balance competing objectives such as cost, sustainability, toxicity, and overall yield. To address this, we formulate synthesis planning as a multi-objective search problem and introduce MORetro*, an algorithm that generates a Pareto front of synthesis routes to explicitly capture trade-offs among user-defined criteria. MORetro* uses weighted scalarization and BO-informed sampling to efficiently navigate the combinatorial search space and prioritize promising trade-offs. Building on multi-objective A*-search, we provide optimality guarantees showing that, for a fixed single-step model, MORetro* recovers the true Pareto front under admissibility. Across multiple retrosynthesis benchmarks, MORetro* produces diverse, high-quality Pareto fronts, uncovering solutions overlooked by single-objective approaches and better aligning CASP outputs with industrial decision-making.
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