Transfer Learning Empowers the Development of Better-Performing Models for In Vivo Cardiotoxicity by Learning from In...

Evaluating chemical hazards, such as cardiotoxicity, is critical for sound chemical management but is challenging due to the labor-intensive and time-consuming nature of in vivo testing. In silico models can efficiently and cost-effectively screen potential cardiotoxic compounds from a large number of chemicals. However, the available small-scale in vivo toxicity data sets prevent models from achieving superior prediction accuracy. Herein, a transfer learning-based graph neural network (TL-GNN) model for predicting in vivo cardiotoxicity was developed by fine-tuning from a pretrained model on a data set regarding the human ether-a-go-go-related gene binding, a molecular initiating event that may ultimately cause cardiotoxicity. The TL-GNN model not only outperformed the GNN without TL and classic machine learning models, but also those fine-tuned from GNN models pretrained on a nonmechanistic octanol-water partition coefficient data set and Bidirectional Encoder Representations from Transformers models pretrained on a data set with over a million unlabeled compounds. A structure-activity landscape-based method was adopted to characterize the applicability domains of the TL-GNN model. The TL-GNN model with defined applicability domains was applied to screen approximately 600,000 chemicals across diverse categories, identifying over 25,000 as cardiotoxic. The robustness of the TL-GNN model suggests the potential of the modeling strategy for complex in vivo toxicity by learning from adverse outcome pathway (AOP)-related in vitro targets.