Modeling the interactions between drugs, targets, and diseases has significant implications for drug discovery, precision medicine and personalized treatments. Current computational approaches consider pairwise interaction, including drug-target or drug-disease interaction individually. On the other hand, within human metabolic systems, the interaction of drugs with protein targets in cells influences target activities. Moving beyond binary relationships and exploring tighter relationships together as triple is essential to understanding drugs' mechanism of action (MoAs). Moreover, considering the heterogeneity of drugs, targets, and diseases, along with their distinct characteristics, it is critical to model these complex interactions appropriately. To address these challenges, we develop a novel Heterogeneous Graph Triplet Attention Network (HeTan)by modeling the interconnectedness of all entities in a heterogeneous graph. HeTAN introduces a novel triplet message passing and triplet-wise attention mechanism within this heterogeneous graph structure. In contrast to focusing only on pairwise attention as the importance of an entity for the other, we define triplet attention to model the importance of pairs for the other in the drug-target-disease triplet prediction problem. We perform extensive experiments on real-world datasets and our results show that HeTAN outperforms several baselines, demonstrating its superior performance in uncovering novel drug-target-disease relationships.

Drug-drug interaction (DDI) indicates where a particular drug's desired course of action is modified when taken with other drug (s). DDIs may hamper, enhance, or reduce the expected effect of either drug or, in the worst possible scenario, cause an adverse side effect. While it is crucial to identify drug-drug interactions, it is quite impossible to detect all possible DDIs for a new drug during the clinical trial. Therefore, many computational methods are proposed for this task. This paper presents a novel method based on a heterogeneous information network (HIN), which consists of drugs and other biomedical entities like proteins, pathways, and side effects. Afterward, we extract the rich semantic relationships among these entities using different meta-path-based topological features and facilitate DDI prediction. In addition, we present a heterogeneous graph attention network-based end-to-end model for DDI prediction in the heterogeneous graph. Experimental results show that our proposed method accurately predicts DDIs and outperforms the baselines significantly.

Extracting meaningful features from sequences and devising effective similarity measures are vital for sequence data mining tasks, particularly sequence classification. While neural network models are commonly used to automatically learn sequence features, they are limited to capturing adjacent structural connection information and ignoring global, higher-order information between the sequences. To address these challenges, we propose a novel Hypergraph Attention Network model, namely Seq-HyGAN for sequence classification problems. To capture the complex structural similarity between sequence data, we create a novel hypergraph model by defining higher-order relations between subsequences extracted from sequences. Subsequently, we introduce a Sequence Hypergraph Attention Network that learns sequence features by considering the significance of subsequences and sequences to one another. Through extensive experiments, we demonstrate the effectiveness of our proposed Seq-HyGAN model in accurately classifying sequence data, outperforming several state-of-the-art methods by a significant margin.

Drug-Drug Interactions (DDIs) may hamper the functionalities of drugs, and in the worst scenario, they may lead to adverse drug reactions (ADRs). Predicting all DDIs is a challenging and critical problem. Most existing computational models integrate drug-centric information from different sources and leverage them as features in machine learning classifiers to predict DDIs. However, these models have a high chance of failure, especially for new drugs when all the information is not available. This paper proposes a novel Hypergraph Neural Network (HyGNN) model based on only the Simplified Molecular Input Line Entry System (SMILES) string of drugs, available for any drug, for the DDI prediction problem. To capture the drug chemical structure similarities, we create a hypergraph from drugs’ chemical substructures extracted from the SMILES strings. Then, we develop HyGNN consisting of a novel attention-based hypergraph edge encoder to get the representation of drugs as hyperedges and a decoder to predict the interactions between drug pairs. Furthermore, we conduct extensive experiments to evaluate our model and compare it with several state-of-the-art methods. Experimental results demonstrate that our proposed HyGNN model effectively predicts DDIs and impressively outperforms the baselines with a maximum F1 score, ROC-AUC, and PR-AUC of 94.61%, 98.69%, and 98.68%, respectively. Finally, we show that our models also work well for new drugs.

A drug-drug interaction (DDI) occurs when a drug is combined with other drug(s). DDIs have the potential to obstruct, increase, or diminish the intended impact of a drug or, in the worst-case scenario, induce an undesirable side effect. While it is critical to discover DDIs during clinical trials, it is impractical and expensive to detect all possible DDIs for a drug. Although several computational approaches for this problem have been developed, many of these methods need external biomedical knowledge that makes them difficult to generalize to drugs in early development phase. In this paper, we propose a novel method for predicting DDIs based on the vital chemical substructure of drugs extracted from their SMILES strings. We construct a graph that connects drugs based on their common functional chemical substructures. Furthermore, we apply different well-known graph neural network (GNN) methods to generate drug embeddings. Drug embeddings of individual drugs are concatenated to generate features of drug pairs. Finally, drug pair features are fed to different machine learning (ML) classifiers for DDI prediction. We evaluate our model on DrugBank dataset. Our result shows promising results and our model outperforms a baseline model based on different DDI representation creation methods.

Drug-drug interaction (DDI) indicates the event where a particular drug's desired course of action is modified when taken together with other drugs (s). DDIs may hamper, enhance, or reduce the expected effect of either drug or, at the worst possible scenario, cause an adverse side effect. While it is crucial to identify drug-drug interactions, it is quite impossible to detect all possible DDIs for a new drug during the clinical trial. Therefore, many computational methods are proposed for this task. In this paper, we propose a novel method, HIN-DDI for discovering DDIs. This method considers drugs and other biomedical entities like proteins, pathways, and side effects, for DDI prediction. We design a heterogeneous information network (HIN) to model relations between these entities. Afterward, we extract the rich semantic relationships among these entities using different meta-path-based topological features. An extensive set of features are fed to different classifiers for DDI prediction. Moreover, we run extensive experiments to compare and evaluate the effectiveness of HIN-DD I with other methods. Results exhibit that HIN-DDI is quite effective in predicting new drugs as well as existing drugs. Unlike existing works, HIN-DDI can predict new drugs, and more importantly, it can impressively outmatch baseline methods by up to 63%.