DrugFreq: Predicting Drug Side Effect Frequencies using Multiple Kernel Learning and Neighborhood Interaction Aware Network

Abstract

Drug side effects are a significant challenge that urgently needs to be addressed in the process of drug treatment. Clinical trials and post-market drug monitoring systems primarily rely on clinical experience and historical data, making it difficult to address the practical challenges posed by the increasingly diverse range of drugs and the increasingly complex side-effect information, necessitating robust computational methods. To address this issue, this study proposes DrugFreq, a drug side-effect frequency prediction model based on Multiple Kernel Learning (MKL) and a Graph Neural Network (GNN). To capture more granular topological features, a neighborhood interaction layer is integrated into the Graph Neural Network (GNN) architecture. Furthermore, the challenge of data imbalance is mitigated by incorporating a regularization term into the cross-entropy loss function; specifically, a Gaussian distribution constrains the prediction scores of unobserved drug–side effect associations. Experimental results show that DrugFreq outperforms several state-of-the-art models in predicting drug-side effect frequencies across various evaluation metrics. It demonstrates strong generalization ability and robustness in handling sparse and imbalanced datasets. Despite DrugFreq’s promising performance, its predictive upper bound is limited by severe data sparsity. Future work will enhance DrugFreq by integrating heterogeneous network information (e.g., drug targets and pathways) and leveraging drug representations from compoundfocused pre-trained models. DrugFreq is an effective tool for investigating adverse drug effects, offering new perspectives for drug discovery and design.