HumanToxPredict: An interpretable data-driven framework for human oral TDLo prediction and toxicity assessment of...

Predicting the human toxic dose low (TDLo) is critically important for pharmaceutical safety assessment and regulatory toxicology. A global computational toxicology framework that integrates toxicity data for children, infants, men, and women was developed using indicator-variable-based demographic encoding. Initially, Partial Least Squares (PLS)-based quantitative structure-activity relationship (QSAR) and quantitative read-across structure-activity relationship (q-RASAR) models were developed using interpretable molecular descriptors and similarity-based descriptors. A structure-level group-wise partitioning strategy was adopted. The q-RASAR model demonstrated improved predictive performance compared with the corresponding QSAR model. Additionally, Random Forest (RF) and Support Vector Machine (SVM) models were explored to investigate non-linear relationships. To further improve practical applicability, classification models were also developed, with RF showing the strongest overall performance among the evaluated models based on multi-metric analysis. Model interpretation suggests that pTDLo values are influenced by substituted nitrogen environments, topological arrangements, and similarity-driven toxicological information. The final model was further applied to 3660 investigational drugs, with most compounds falling within the applicability domain. We also developed a user-friendly software tool, HumanToxPredict, to enable rapid toxicity prediction. Overall, the present study introduces an interpretable, population-integrated human toxicity prediction framework that integrates q-RASAR, machine learning, and large-scale chemical screening.