RECIPE: Learning to Rank Complete Precursor Sets for Inorganic Retrosynthesis

Single-step inorganic retrosynthesis is evaluated by whether a complete precursor set is recovered, yet many data-driven systems first rank individual precursors and then rely on threshold or count heuristics to assemble sets. This mismatch is especially severe when the true set size is unknown: individually plausible precursors can combine into incomplete, over-complete, or otherwise wrong near-miss sets. We reformalize the task as variable-size set-level ranking and introduce RECIPE, a target-conditioned framework that separates precursor recall from final set-level scoring. The Precursor Candidate Generator learns formula-level compatibility to build a high-recall precursor pool, while the Complete-Set Reranker compares variable-size candidate precursor sets directly. On the Retrieval-Retro year-split benchmark, the generator improves Combo@20 from 69.00 to 72.75. Compared with Retrieval-Retro, the reranker improves Combo@1 by 11.30 points to 71.70, Combo@20 by 20.82 points to 89.82, and Combo MRR by 14.14 points to 77.43. A preliminary 20-case out-of-distribution evaluation shows the same direction of improvement. These results suggest that set-level prioritization remains important after strong precursor recall, and that optimizing ranked complete precursor sets can reduce the inspection burden in inorganic synthesis planning.