Retrosynthesis planning aims to decompose target molecules into available building blocks, forming a synthetic tree where each internal node represents an intermediate compound and each leaf ideally corresponds to a purchasable reactant. However, this tree becomes invalid if any leaf node is not a valid building block, making the planning process vulnerable to the "weakest link" in the synthetic route. Existing methods often optimise for average performance across branches, failing to account for this worst-case sensitivity. In this paper, we reframe retrosynthesis as a worst-path optimisation problem within tree-structured Markov Decision Processes (MDPs). We prove that this formulation admits a unique optimal solution and provides monotonic improvement guarantees. Building on this insight, we introduce Interactive Retrosynthesis Planning (InterRetro), a method that interacts with the tree MDP, learns a value function for worst-path outcomes, and improves its policy through self-imitation, preferentially reinforcing past decisions with high estimated advantage. Empirically, InterRetro achieves state-of-the-art results - solving 100% of targets on the Retro*-190 benchmark, shortening synthetic routes by 4.9%, and achieving promising performance using only 10% of the training data.

翻译：逆合成规划旨在将目标分子分解为可获得的构建模块，形成一个合成树，其中每个内部节点代表一个中间化合物，每个叶节点理想情况下对应一个可购买的原料。然而，若任一叶节点不是有效的构建模块，该合成树即失效，使得规划过程易受合成路线中“最薄弱环节”的影响。现有方法通常优化各分支的平均性能，未能考虑这种最坏情况敏感性。本文中，我们将逆合成重新表述为树状马尔可夫决策过程（MDPs）中的最差路径优化问题。我们证明该表述具有唯一最优解，并提供单调改进保证。基于这一见解，我们提出了交互式逆合成规划方法（InterRetro），该方法与树状MDP交互，学习最差路径结果的价值函数，并通过自我模仿改进其策略，优先强化具有高估计优势的历史决策。实验表明，InterRetro取得了最先进的成果——在Retro*-190基准测试中解决了100%的目标分子，将合成路线缩短了4.9%，且仅使用10%的训练数据即实现了有前景的性能。