UncertFP: Uncertainty-Aware Multi-View Evidence Fusion for Molecular Fingerprint Prediction from Tandem Mass Spectra

Molecular fingerprint prediction from tandem mass spectrometry (MS/MS) is hindered by incomplete fragmentation and by the fact that distinct molecular structures can produce similar spectra. Existing methods rely mainly on a single view and thus ignore complementary compositional cues. Moreover, similar bit-presence probabilities can reflect different evidence strengths, obscuring whether a prediction has weak or substantial support. Multiple views provide additional structural information, but shared observations can inflate accumulated evidence and conflicting views can undermine prediction. We propose UncertFP, an uncertainty-aware multi-view evidence fusion framework that infers per-bit positive and negative evidence from fragment-peak, neutral-loss, and peak-difference views. Specifically, UncertFP adjusts the contribution of each view through bit reliability and uses a learnable fusion coefficient to control evidence accumulation by interpolating between direct accumulation and weighted averaging. Disagreement discounting further reduces fused evidence when strongly supported views favor opposing bit states. Subsequently, the fused evidence and the shared prior jointly yield a probabilistic fingerprint and an uncertainty mass that quantifies evidence insufficiency. We leverage the relative ranking of whole-fingerprint prediction errors to supervise spectrum-level uncertainty, thereby enabling selective prediction. Across three datasets, two encoders, and three fingerprint dimensionalities, UncertFP improves Soft Tanimoto by 0.07-0.16 over baselines. The complete prediction and scoring pipeline also improves candidate-structure ranking, while uncertainty-based spectrum selection prioritizes predictions with lower average error.