Improving adverse drug event (ADE) prediction is highly critical in pharmacovigilance research. We propose a novel information component guided pharmacological network model (IC-PNM) to predict drug-ADE signals. This new method combines the pharmacological network model and information component, a Bayes statistics method. We use 33,947 drug-ADE pairs from the FDA Adverse Event Reporting System (FAERS) 2010 data as the training data, and the new 21,065 drug-ADE pairs from FAERS 2011-2015 as the validations samples. The IC-PNM data analysis suggests that both large and small sample size drug-ADE pairs are needed in training the predictive model for its prediction performance to reach an area under the receiver operating characteristic curve (AUROC) = 0.82. On the other hand, the IC-PNM prediction performance improved to AUROC = 0.91 if we removed the small sample size drug-ADE pairs from the prediction model during validation.

Computational strategies play a vital role in the prediction of adverse drug events (ADEs) owing to their low cost and increased efficiency. In this study, we used the strengths of the Jaccard and Adamic–Adar indices to build feature fusion-based predictive network models (FFPNMs) with three different machine learning (ML) methods respectively to predict drug–ADE associations. Our FFPNM with the logistic regression (LR) model improved to an area under the receiver operating characteristic curve (AUROC) value of 0.849, while the corresponding AUROC values for the pharmacological network model (PNM) and model based on similarity measures were 0.824 and 0.821, respectively. FFPNM with random forest (RF) is the best model among them with an AUROC value of 0.856, and the performance of FFPNM with SVM is close to that of FFPNM with RF and higher than that of FFPNM with LR. In these models, the bipartite network consisted of 152 drugs and 633 ADEs, which were obtained from the FDA Adverse Event Reporting System (FAERS) 2010 dataset. To better evaluate the performance of FFPNMs, we performed model predictions by different network consisting of 1177 drugs and 97 ADEs which were from the data of the first 120 days of FAERS 2004. FFPNM with RF achieved the best predictive result with AUROC value of 0.913. The results show that FFPNMs with ML methods, specially RF, have a superior prediction performance and robustness using only the topology features of the drug–ADE network. From our findings, the optimal, concise, and efficient models as computational methods for drug-ADE association predictions, were revealed. Source codes of this paper are available on https://github.com/Coderljl/FFPNM.

Identifying side effects is crucial for drug development and postmarket surveillance. Several computational methods based on graph neural networks (GNNs) have been developed, leveraging the topological structure and node attributes in graphs with promising results. However, existing heterogeneous-network-based approaches often fail to fully capture the complex structure and rich semantic information within these networks. Furthermore, the oversmoothing problem in GNNs remains a major challenge. In this study, we propose AGRL-DSE, a novel adaptive graph representation learning framework designed to enhance node-feature learning for predicting drug side effects. First, we construct a heterogeneous graph with intra- and interlayer connections to represent similarities and associations between drugs and side effects, capturing hidden topological relationships in heterogeneous contexts. Second, we integrate three GNN modules in AGRL-DSE, graph convolutional network (GCN), graph sample and aggregation (GraphSAGE), and graph attention network (GAT) at the graph, node, and edge levels, respectively, with the aim of capturing semantic information at different levels in graph data in a hierarchical manner, gradually extracting and enhancing the features of the graph. Additionally, we introduce an adaptive layer attention mechanism that dynamically assigns weights to each layer's features to achieve adaptive fusion of multilevel features, thereby automatically adjusting the contribution of each layer to the final embedding. Experimental results demonstrate that AGRL-DSE outperforms state-of-the-art predictive models in both hot- and cold-start scenarios, highlighting its superiority and generalizability. AGRL-DSE's ability to capture complex relationships and provide deeper insights into drug-side effect interactions could transform drug evaluation, monitoring, and prescription, leading to better health outcomes and more efficient drug development processes.

Adverse drug reactions induced by drug-drug interactions are important causes of morbidity and mortality. To address the issues that the existing models ignore the multidirectional mutual semantic transmission and local-global feature interaction in drug pairs, we propose an effective knowledge graph embeddings based on CNN-LSTM for drug-drug interactions prediction (KGECL). This framework consists of four components: data preprocessing module, interaction feature extraction module, attention module and prediction modules. The data preprocessing module conducts low-dimensional embeddings of drug data. The interaction feature extraction module explores multidirectional semantics and local-global characteristics, and simulate the complex relationships and interactions across multiple adverse reactions effectively. The attention module enhances the ability of network to recognize the critical information. Finally, the prediction module uses a fully-connected layer for prediction. Comparative experiments on two available datasets demonstrates that KGECL achieves higher accuracy and helps to the improve drug safety.

Drug–drug interactions (DDIs) can severely affect patient health and safety. Predicting potential DDIs before administering medication to patients is crucial for drug development as it helps to prevent adverse drug reactions. Many effective DDI prediction methods have been proposed using graph neural networks; however, these methods only aggregate information from directly connected nodes restricted to a drug-related manner and fail to capture long-range dependencies in heterogeneous networks. To address this issue, we propose an enhanced multiple-feature hybrid graph method to predict DDIs (MFHG-DDI). Specifically, we constructed a heterogeneous network incorporating multiple drug features and DDI information. We employed an enhanced hybrid graph module that integrates a graph convolutional network, graph attention network, and global average pooling to learn latent features, ultimately applying a prediction function to predict DDIs. MFHG-DDI reframes the heterogeneous network as a graph classification task, capturing DDI information efficiently through an enhanced hybrid graph. Known DDI datasets were used to train and evaluate the proposed model. The experimental results indicate that integrating multiple drug features into the hybrid graph method can enhance the DDI prediction accuracy, increases the success rate of combination therapy, and has the potential to enhance drug safety.