LC-HRMS Known- and Unknown-Feature Prioritization Based on Machine-Learning-Predicted Ready Biodegradability

This gap limits our ability to identify persistent organic contaminants. Models have been developed to predict the persistence for already identified chemicals, but no models are able to prioritize unknown features detected in high-resolution mass spectrometry (HRMS).

Here, we present two complementary machine-learning models that connect molecular structure and tandem mass-spectrometry fragmentation behavior to ready biodegradability. The first integrates molecular fingerprints with synthetic and retrosynthetic accessibility and molecular complexity metrics, achieving a cross-validated balanced accuracy of 88% for 4,840 curated chemicals. The second model extends predictions to unknowns by using cumulative neutral losses (CNLs) derived from HRMS/MS data and trained in a semisupervised manner guided by the structure-based model, reaching 82% balanced accuracy.