Enhancing multi-task in vivo toxicity prediction via integrated knowledge transfer of chemical knowledge and in vitro toxicity information

Abstract

The evaluation of potential drug toxicity is a crucial step in early drug development. in vivo toxicity assessment represents a key challenge that must be addressed before advancing to clinical trials. However, traditional in vivo experiments primarily rely on animal models, raising concerns regarding cost, time efficiency, and ethical considerations. To address these challenges, various computational approaches have been developed to support in vivo toxicity evaluations, though these methods often demonstrate limited generalizability due to data scarcity. In this study, we propose MT-Tox, a knowledge transfer-based multi-task learning model specifically designed for in vivo toxicity prediction that overcomes data scarcity. Our model implements a sequential knowledge transfer strategy across three stages: general chemical knowledge pretraining, in vitro toxicological auxiliary training, and in vivo toxicity fine-tuning. This hierarchical approach significantly improves model performance by systematically leveraging information from both chemical structure and toxicity data sources. MT-Tox outperforms baseline models across three in vivo toxicity endpoints: carcinogenicity, drug-induced liver injury (DILI), and genotoxicity. Through ablation studies and attention analyses, we demonstrate that each knowledge transfer technique makes meaningful contributions to the prediction process. Finally, we demonstrate the real-world application of our model as a prediction tool for early-stage drug discovery through comprehensive DrugBank database screening. Scientific contribution: We propose a knowledge transfer framework that integrates chemical and in vitro toxicological information to enhance in vivo toxicity prediction in low-data regimes. Our model provides dual-level interpretability across chemical and biological domains through attention mechanism. Moreover, we demonstrate our model’s applicability by screening the DrugBank database, simulating practical toxicity screening scenarios in drug development. © The Author(s) 2025.