🤖 AI Summary
Traditional retrosynthetic planning struggles to flexibly accommodate constraints such as pathway depth and specified starting materials. This work proposes a prompt-conditioned, end-to-end method for retrosynthetic route generation, introducing for the first time a prompting mechanism into this domain. The approach unifies the target molecule, user-defined constraints, and the complete synthetic route into a single prompt-completion sequence, which is directly decoded by a 24-layer decoder-only architecture to produce structured pathways. Notably, the model supports diverse planning constraints without requiring task-specific retraining. Experimental results demonstrate significant improvements under depth and specified starting material constraints, with Solv-0 scores increasing by 13.7 and 31.2 points, respectively. Moreover, compared to the DESP planner, the proposed method achieves superior Top-10 and Solv-0 performance while using less GPU time.
📝 Abstract
Retrosynthetic planning seeks to connect a target molecule to commercially available starting materials through a multistep route. Classical planners construct such routes by iteratively applying single-step reaction models within a search procedure; constrained variants often require specialized algorithms or architectural changes. Direct route generation reframes retrosynthesis as sequence generation, but existing direct-generation methods still train separate models for different planning specifications. We introduce Ariadne, a decoder-only route generator that represents the target, optional constraints, and route in one prompt-completion sequence. On the RetroCast/PaRoutes mkt-cnv-160 benchmark family, one 24-layer checkpoint follows route-depth and required-starting-material prompts: adding the corresponding prompt fields raises Solv-0 by 13.7 points for depth constraints and 31.2 points for required-leaf constraints. Ariadne also improves over DESP, a bidirectional search planner, on required-leaf Top-10 and Solv-0 in 24 GPU-minutes versus 6.8 GPU-hours. On standard reconstruction, Ariadne is comparable to DMS Explorer XL at about half the reported inference time. Across additional target-only benchmarks, Ariadne's clearest gains are on route-holdout reconstruction, whereas AiZynthFinder MCTS remains stronger on several Solv-0 comparisons. These results extend sequence generation from specialist retrosynthesis models to prompt-conditioned structural route generation. We release the codebase and training scripts to support further work, but do not introduce Tier-1--3 route checkers; those remain the main bottleneck before models of this kind can become useful to experimental chemists.