Advancing mutagenicity predictions in drug discovery with an explainable few-shot deep learning framework

Abstract

The Ames mutagenicity test serves as a cornerstone for evaluating the mutagenic potential of chemical compounds, which is critical in drug discovery and safety assessments. However, existing computational methods struggle to utilize the contribution of individual bacterial strains used in the Ames test, limiting the accuracy of overall mutagenicity predictions. To address this, we introduce Meta-GTMP, a few-shot learning framework that combines graph neural networks (GNNs) and Transformers to integrate the local molecular graph structure with the global information in graph embedding representations for mutagenicity prediction using limited labeled data. A multi-task meta-learning strategy further optimizes the model parameters across individual strain-specific few-shot tasks, leveraging their complementarity to predict the overall Ames result. Computational experiments conducted on the ISSSTY v1-a dataset demonstrate that Meta-GTMP outperforms standard graph-based models, achieving notable improvements in sensitivity (+6.82%) and ROC-AUC score (+2.50%). Laboratory validation tests using six chemically diverse compounds with unknown mutagenicity labels confirmed the model's effectiveness, achieving high accuracy in distinguishing mutagenic and non-mutagenic samples. Importantly, Meta-GTMP makes explainable predictions through a node-edge attribute masking strategy, identifying significant molecular substructures responsible for mutagenicity. These insights are essential in drug discovery, positioning Meta-GTMP as a robust and explainable tool for using mutagenicity predictions to enhance the identification, selection and rational design of safer and more effective potential drug candidates.