Machine Learning for Enhanced Identification Probability in RPLC/HRMS Nontargeted Workflows.

ABSTRACT: In HRMS-based nontargeted analysis (NTA), spectral matching is crucial for chemical identification, particularly in the absence of retention information. This study introduces class probability of true positives (P(TP)) as an innovative approach, leveraging data from MS/MS spectra and calibrant-free predicted retention time indices (RTIs) through 3 machine learning (ML) models to enhance identification probability (IP). The first model is a molecular fingerprint (MF)-to-RTI model trained on 4713 calibrants. The second model, a cumulative neutral loss (CNL)-to-RTI model, utilized 485,577 experimental spectra. The final model, a binary classification model, was trained using 1,686,319 TP and semisynthetic true negative (TN) spectral matches. High correlations betw

SUBMITTER: Ngan HL 

PROVIDER: S-EPMC12392258 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature