Polypharmacy side-effect prediction with enhanced interpretability based on graph feature attention network

Bang, Sunjoo; Jhee, Jong Ho; Shin, Hyunjung

doi:10.1093/bioinformatics/btab174

Cited by 20 publications

(8 citation statements)

References 17 publications

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“…For instance, Zhang Z. et al (2021) used fragments containing functional groups to represent molecular maps for molecular property prediction through a fragment-oriented multi-scale graph attention model. Bang et al (2021) made the prediction of polypharmacy side effects with enhanced interpretability based on graph feature attention network. Constructing a bipartite network is the most popular approach to represent associations between two types of nodes.…”

Section: Introductionmentioning

confidence: 99%

NNAN: Nearest Neighbor Attention Network to Predict Drug–Microbe Associations

Zhu

Zhao

et al. 2022

Front. Microbiol.

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Many drugs can be metabolized by human microbes; the drug metabolites would significantly alter pharmacological effects and result in low therapeutic efficacy for patients. Hence, it is crucial to identify potential drug–microbe associations (DMAs) before the drug administrations. Nevertheless, traditional DMA determination cannot be applied in a wide range due to the tremendous number of microbe species, high costs, and the fact that it is time-consuming. Thus, predicting possible DMAs in computer technology is an essential topic. Inspired by other issues addressed by deep learning, we designed a deep learning-based model named Nearest Neighbor Attention Network (NNAN). The proposed model consists of four components, namely, a similarity network constructor, a nearest-neighbor aggregator, a feature attention block, and a predictor. In brief, the similarity block contains a microbe similarity network and a drug similarity network. The nearest-neighbor aggregator generates the embedding representations of drug–microbe pairs by integrating drug neighbors and microbe neighbors of each drug–microbe pair in the network. The feature attention block evaluates the importance of each dimension of drug–microbe pair embedding by a set of ordinary multi-layer neural networks. The predictor is an ordinary fully-connected deep neural network that functions as a binary classifier to distinguish potential DMAs among unlabeled drug–microbe pairs. Several experiments on two benchmark databases are performed to evaluate the performance of NNAN. First, the comparison with state-of-the-art baseline approaches demonstrates the superiority of NNAN under cross-validation in terms of predicting performance. Moreover, the interpretability inspection reveals that a drug tends to associate with a microbe if it finds its top-l most similar neighbors that associate with the microbe.

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Section: Introductionmentioning

confidence: 99%

NNAN: Nearest Neighbor Attention Network to Predict Drug–Microbe Associations

Zhu

Zhao

et al. 2022

Front. Microbiol.

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“…Drug-Drug Interaction (DDI) model introduces a robust computational framework that is adept at precisely predicting interactions between pairs of drugs and pairs comprising drugs and food components [33]. All of this is accomplished through the utilization of deep neural networks, contributing to optimized prediction performance.…”

Section: Recent Developments In Predicting Polypharmacy Side Effectsmentioning

confidence: 99%

Predicting Big data Drug Interactions and associated side effects by Using Artificial Neural Networks (ANN) over Traditional Graph Convolutional Networks (GCNs)

IBRAHIM,

Mohammed,

DARA

2024

Preprint

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Rapid advances in machine learning have enabled the prediction of complex drug-drug interactions (DDIs) and associated harmful effects. This study aims to develop a neural network model that can predict drug-drug interactions (DDIs) for various side effects. Our study intends to create a reliable and easy-to-understand tool that can transform pharmaceutical research and healthcare by lowering polypharmacy risks. Our method begins with the careful selection and compilation of large datasets on medicine combinations, side effects, drug-side effect connections, and drug-protein interactions. We use an adjacency matrix to establish a drug-protein network. Then, we use PCA to shrink the network. Using artificial neural networks, our neural network is designed for binary categorization. This model is rigorously trained, validated, and tested using performance metrics to ensure its strength and adaptability. Our model has remarkable accuracy, with AUC-ROC scores of 98.67% for certain interactions. Reading and handling structured input is a major advantage of Artificial Neural Networks (ANNs) over Graph Convolutional Networks (GCNs). The findings demonstrate our approach's versatility in pharmaceutical research and healthcare, including medication development and real-time clinical decision help. To conclude, this study advances DDI prediction and management. Resilience, comprehensibility, and accuracy make the model a flexible polypharmacy solution. By solving DDI prediction and side effect control, our strategy might improve pharmaceutical research, patient safety, and healthcare results. This study shows how advanced machine learning may be used in pharmaceuticals.

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“…The model predicts associations between pairs of drugs and the specific side effects in the pair as a link prediction task. Since the Decagon model was proposed, other models have been developed for specific DDIs prediction (Nováček and Mohamed, 2020 ; Bang et al, 2021 ; Masumshah et al, 2021 ).…”

Section: Current Approach To Precision Medicine In Polypharmacymentioning

confidence: 99%

Polypharmacy and precision medicine

Fujita

Masnoon

Mach

et al. 2023

Camb. prisms Precis. med.

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Precision medicine is an approach to maximise the effectiveness of disease treatment and prevention and minimise harm from medications by considering relevant demographic, clinical, genomic and environmental factors in making treatment decisions. Precision medicine is complex, even for decisions about single drugs for single diseases, as it requires expert consideration of multiple measurable factors that affect pharmacokinetics and pharmacodynamics, and many patient-specific variables. Given the increasing number of patients with multiple conditions and medications, there is a need to apply lessons learned from precision medicine in monotherapy and single disease management to optimise polypharmacy. However, precision medicine for optimisation of polypharmacy is particularly challenging because of the vast number of interacting factors that influence drug use and response. In this narrative review, we aim to provide and apply the latest research findings to achieve precision medicine in the context of polypharmacy. Specifically, this review aims to (1) summarise challenges in achieving precision medicine specific to polypharmacy; (2) synthesise the current approaches to precision medicine in polypharmacy; (3) provide a summary of the literature in the field of prediction of unknown drug–drug interactions (DDI) and (4) propose a novel approach to provide precision medicine for patients with polypharmacy. For our proposed model to be implemented in routine clinical practice, a comprehensive intervention bundle needs to be integrated into the electronic medical record using bioinformatic approaches on a wide range of data to predict the effects of polypharmacy regimens on an individual. In addition, clinicians need to be trained to interpret the results of data from sources including pharmacogenomic testing, DDI prediction and physiological-pharmacokinetic-pharmacodynamic modelling to inform their medication reviews. Future studies are needed to evaluate the efficacy of this model and to test generalisability so that it can be implemented at scale, aiming to improve outcomes in people with polypharmacy.

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Polypharmacy side-effect prediction with enhanced interpretability based on graph feature attention network

Cited by 20 publications

References 17 publications

NNAN: Nearest Neighbor Attention Network to Predict Drug–Microbe Associations

NNAN: Nearest Neighbor Attention Network to Predict Drug–Microbe Associations

Predicting Big data Drug Interactions and associated side effects by Using Artificial Neural Networks (ANN) over Traditional Graph Convolutional Networks (GCNs)

Polypharmacy and precision medicine

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