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 (logIE), where it outperformed the current state of the art. Overall, the results demonstrate the feasibility of using graph-based ML to estimate drug concentrations in whole blood without reference material. This is a practical ML tool that can support decision-making in toxicological evaluation, particularly for newly emerging or rarely encountered drugs. The GNN model is open source, and the data set used for training and testing the models are publicly available.