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Project Ariadne: Prompt-Conditioned Route Generation for Synthesis Planning

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Computer Science > Machine Learning

arXiv:2606.24184 (cs)
[Submitted on 23 Jun 2026]

Title:Project Ariadne: Prompt-Conditioned Route Generation for Synthesis Planning

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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.
Comments: Code is available at this https URL
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2606.24184 [cs.LG]
  (or arXiv:2606.24184v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2606.24184
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Anton Morgunov [view email]
[v1] Tue, 23 Jun 2026 06:12:11 UTC (61 KB)
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