Graph Machine Learning Can Estimate Drug Concentrations in Whole Blood from Forensic Screening Results.

LC-HRMS is widely used in forensic toxicology for broad-scope screening. When a newly emerging or rarely encountered compound is tentatively identified, toxicologists must decide whether it may be relevant to a case and, if so, quantify it. However, acquiring reference material for quantification is costly and time-consuming. A rapid semiquantitative estimation method would help prioritize only compounds above the toxic threshold. This study presents a machine-learning (ML) framework that estimates drug concentrations in whole blood using molecular structure information and LC-HRMS signals. Using a data set of 191 drugs spiked into whole blood at multiple concentration levels, we trained and evaluated several ML models. Standard models, including Random Forests, achieved moderate performance. In contrast, a recently reported Graph Neural Network (GNN) leveraging atomic features and global molecular properties consistently produced the highest accuracy. Under cross-validation, the GNN predicted signal-to-concentration ratios for 79% of all molecules, corresponding to concentration estimates between 50% and 200% of the true value. Toxicological thresholds often span multiple orders of magnitude, making this precision acceptable. The GNN model was additionally evaluated on an external benchmark data set of ionization efficiencies (