DTPPI: predicting drug interactions using a weighted drug-protein network

Abstract

Polypharmacy, the practice of using multiple drugs to treat complex diseases, poses a significant risk of drug-drug interactions (DDIs), which can lead to unanticipated adverse drug reactions (ADRs) and toxicity. Identifying and understanding these DDIs is crucial to ensuring the safety of polypharmacy. Traditional laboratory-based methods for detecting DDI are costly and time consuming, prompting the development of computational approaches. However, many of these methods face limitations, mainly the lack of utilization of biological networks to model drug mechanics. Such an approach could lead to a new technique with better and more accurate DDI predictions. In response to these challenges, we propose the DTPPI network, a novel machine learning approach that leverages a drug-target-protein-protein interaction network to improve DDI prediction. By extracting topological features and combining them with biological drug features, the DTPPI method enhances the performance of a multilayer perceptron model. The evaluation results showed an AUC of 0.64 for topological characteristics alone, 0.89 for biological characteristics, and 0.91 for combined features, demonstrating that integrating topological and biological data significantly improves the prediction accuracy of DDI. Materials and implementations are available at: https://github.com/Golnazthr/DTPPI HighlightsO_LIConstructs a weighted graph network to model interactions among drugs, proteins, and targets, applicable to all drug types. C_LIO_LIExtracts six universal topological features from the graph, independent of chemical structure. C_LIO_LIEnhances DDI prediction by using topological features alone or alongside traditional drug features. C_LIO_LIIncorporates an MLP model for superior predictive accuracy using combined features. C_LI