Hybrid Graph Neural Network Ensemble with Stacked Meta-Learning for Laboratory-Conditioned Chromatographic Retention-Time Prediction

Abstract Accurate prediction of chromatographic retention time (RT) is valuable for compound identification in untargeted metabolomics and forensic toxicology, yet validation must account for repeated molecular structures, chemical-space coverage, and heterogeneous laboratory sources. Here we present a hybrid ensemble that combines Graph Attention Networks (GAT), Graph Convolutional Networks (GCN), and a fingerprint neural network (FPNN) with an ExtraTrees meta-learner and learned laboratory embeddings. The HighResNPS-derived modeling table contains 3776 structure–laboratory observations from 23 forensic toxicology laboratories, corresponding to 1357 InChIKey connectivity groups. Under molecular-identity-grouped outer validation repeated three times, the final ensemble achieved R2 = 0.977 ± 0.002 and MAE = 0.360 ± 0.016 min; under scaffold-aware validation, it achieved R2 = 0.928 ± 0.004 and MAE = 0.670 ± 0.013 min. Paired cluster-bootstrap comparisons show that combining the three networks lowers MAE versus each alone, whereas the meta-learner improves on simple averaging only for identity-grouped prediction. Prospective maximum-Tanimoto diagnostics define structural similarity to the development set, while molecule-aware perturbation analysis is reported as the sensitivity of the GAT component rather than a mechanism of the complete ensemble. The resulting scope is laboratory-conditioned RT prediction for new chemical groups within the 23 represented laboratory labels. A browser-based demonstration is available at https://huggingface.co/spaces/AI4deeperScience/chromatography-rt-predictor.