Adverse Drug Reactions prediction by combining wide & deep learning and POLY2

Accurate prediction of Adverse Drug Reactions (ADRs) holds immense importance in the field of clinical medicine and drug development. The requirement of accurate prediction spans various stages, ranging from drug design and clinical trials to marketing monitoring. The traditional ADR forecasting method has the disadvantage that it requires a lot of computing resources and is not suitable for large-scale forecasting. To address this issue, this study introduces the Wide & Deep model. This model combines the abilities of memorization and generalization to enhance the accuracy of ADR predictions. Additionally, we identify a shortcoming in the wide component of the traditional Wide & Deep model the lack of nonlinear transformation. Therefore, we propose the inclusion of POLY2 in the Wide & Deep model to rectify this shortcoming. By incorporating POLY2, our aim is to retain the models memorization and generalization abilities, leverage the nonlinear relationship between features, and capture the interaction effect between drug chemical substructures for better model performance. To validate our proposed method, we conduct experiments on two datasets: the FDA Adverse Event Reporting System (FAERS) and PubChem. The evaluation metric utilized is the Area Under the Curve (AUC) score, which demonstrates that our method outperforms the original model. The results indicate that by combining POLY2 feature crosses with the Wide & Deep model, we have achieved significant improvements in the prediction of ADRs.