From Static Ranking to Active Planning: Feedback-Conditioned Candidate Verification for Molecule Identification with Neural Spectrum Prediction

Molecule identification with neural spectrum prediction ranks candidate molecular structures by agreement between their predicted spectra and observed experimental spectra, then verifies candidates in this order. However, simply following this static ranking overlooks spectral similarity feedback obtained during candidate verification. We introduce ActiveMS, an active planning framework for MS/MS-based molecule identification that incorporates this feedback to determine which unverified candidate structures to verify next. ActiveMS-GP models feedback residuals with a Gaussian process, while ActiveMS-FCP learns an amortized feedback-conditioned posterior over candidate structures across verification rollouts. For evaluation, we construct ActiveMS Bench from the SpectraVerse and NIST spectral libraries as two separate benchmarks, with feedback supplied by an independent non-neural simulator. On ActiveMS Bench, both planners consistently outperform static-ranking verification with MassFormer and ICEBERG spectrum predictors. With either predictor, ActiveMS-GP and ActiveMS-FCP reduce mean verification rounds by 33.3–46.9% and 38.4–52.2% respectively. ActiveMS-FCP further raises identification success within 16 verification rounds: with MassFormer, from 50.9% to 77.7% on SpectraVerse and from 70.9% to 86.0% on NIST; with ICEBERG, from 87.2% to 93.7% on SpectraVerse and from 88.6% to 95.0% on NIST. These results indicate that feedback-conditioned planning and neural spectrum prediction improve identification along complementary axes: better predictions sharpen the initial ranking, while feedback during verification further reduces the rounds needed to reach the target.