Multitype drug interaction prediction based on the deep fusion of drug features and topological relationships

Abstract

Drug–drug interaction (DDI) prediction has received considerable attention from industry and academia. Most existing methods predict DDIs from drug attributes or relationships with neighbors, which does not guarantee that informative drug embeddings for prediction will be obtained. To address this limitation, we propose a multitype drug interaction prediction method based on the deep fusion of drug features and topological relationships, abbreviated DM-DDI. The proposed method adopts a deep fusion strategy to combine drug features and topologies to learn representative drug embeddings for DDI prediction. Specifically, a deep neural network model is first used on the drug feature matrix to extract feature information, while a graph convolutional network model is employed to capture structural information from the adjacency matrix. Then, we adopt delivery operations that allow the two models to exchange information between layers, as well as an attention mechanism for a weighted fusion of the two learned embeddings before the output layer. Finally, the unified drug embeddings for the downstream task are obtained. We conducted extensive experiments on real-world datasets, the experimental results demonstrated that DM-DDI achieved more accurate prediction results than state-of-the-art baselines. Furthermore, in two tasks that are more similar to real-world scenarios, DM-DDI outperformed other prediction methods for unknown drugs.

A low-cost machine learning framework for predicting drug–drug interactions based on fusion of multiple features and a parameter self-tuning strategy

“…However, chemical substructures, targets, pathways and enzymes contain more valuable information, making them beneficial for the analysis and commonly selected for comprehensive learning. 41,56…”

“…However, chemical substructures, targets, pathways and enzymes contain more valuable information, making them beneficial for the analysis and commonly selected for comprehensive learning. 41,56 In this study, we focus on building a machine learning model to learn patterns and relationships between four specific features and DDI types, without discussing other influencing factors such as administration routes. However, the results obtained from this model provide theoretical and data support for subsequent research on designing more complex models and incorporating additional influencing features.…”

Application of Artificial Intelligence in Drug–Drug Interactions Prediction: A Review

“…However, it is important to note that drug−target interaction prediction using machine learning is a dichotomous problem and cannot accurately predict the specific type of action of a drug target. Nonetheless, this approach remains a promising avenue for accelerating the 31 Chemical structural, Side effects Similarity, Logistic regression Undirected DDI Ferdousi et al 32 Carriers, Transporters Enzymes, Targets Similarity, Conventional classifier Undirected DDI Li et al 35 Chemical structural Similarity, Bayesian network Undirected DDI Kim et al 36 Medical terms, Semantic information SVM classifier, Huber loss function, Undirected DDI Cheng et al 37 Chemical structural Phenotypic Similarity, Conventional classifier Undirected DDI Park et al 45 Drug, Protein Similarity, Random walk Undirected DDI Zhang et al 61 Drug features Similarity, Matrix factorization Undirected DDI Rohani et al 51 Drug features Similarity, Matrix factorization Undirected DDI Kastrin et al 62 Chemical structural, Enzymes, Targets, Pathway Neighborhood recommendation, Random walk Undirected DDI Yan et al 38 Chemical structural, Biological, Phenotypic Similarity, RLS classifier Undirected DDI Ryu et al 78 Chemical structural Deep Neural Network, Multitask DDI events Lee et al 79 Chemical structural, Target gene, GO terms Deep Neural Network, Autoencoders DDI events Hou et al 100 Chemical structural Deep Neural Network DDI events Huang et al 101 Chemical structural Similarity, Graph neural networks DDI events Deng et al 12 Chemical structural, Enzymes, Targets, Pathway Similarity, Deep Neural Network DDI events Wang et al 102 Chemical structural, Enzymes, Targets Transformer, Autoencoders DDI events Lyu et al 103 Chemical structural, Enzymes, Targets Knowledge graph, Graph neural networks DDI events Zhu et al 114 Chemical structural Dual-view, Graph neural networks DDI events Deng et al 104 Chemical structural Small-sample learning DDI events Kang et al 105 Chemical structural, Enzymes, Targets, Pathway Deep Neural Network, Graph neural networks DDI events Shao et al 106 Chemical structural, Semantic information Pretrained transformer DDI events Lin et al 107 Drug features Attention, Contrastive learning DDI events Feng et al 29 Chemical drug discovery process and identifying potential therapeutic targets. 109 Zhang et al proposed a method for predicting drug target interactions us...…”

“…Kang et al 105 improved upon the work of Deng et al 12 by proposing a deep learning-based multitype drug−drug interaction (DDI) prediction model called DM-DDI, which mixes topological linkages and pharmacological characteristics. The method for DDI prediction utilizes a deep fusion approach that integrates both drug features and topological structures in order to learn relevant feature vectors.…”

MFDA: Multiview fusion based on dual-level attention for drug interaction prediction

“…For DDI prediction, feature or topological similarity-based approaches are the most common. Many methods have been developed based on these methods in different ways, including aggregating multiple types of drug features ( Gottlieb et al, 2012 ), predicting multi-class drug reaction events ( Ryu et al, 2018 ), and integrating drug attribute features with drug interaction relationship ( Kang et al, 2022 ). These optimized methods are effective and make the DDI predictions more complete.…”