Faculty Opinions recommendation of Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity.

2020 ◽  
Author(s):  
Ram Samudrala ◽  
Zackary Falls
Keyword(s):  
Neural Network ◽  
Drug Interaction ◽  
Drug Drug Interaction

scholarly journals Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Narjes Rohani ◽  
Changiz Eslahchi
Keyword(s):  
Neural Network ◽  
Drug Interaction ◽  
Side Effect ◽  
Network Architecture ◽  
Selection Process ◽  
Superior Performance ◽  
Multiple Drug ◽  
Interaction Prediction ◽  
Benchmark Datasets ◽  
Drug Drug Interaction

Abstract Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting unknown DDI with high precision is challenging. We proposed "NDD: Neural network-based method for drug-drug interaction prediction" for predicting unknown DDIs using various information about drugs. Multiple drug similarities based on drug substructure, target, side effect, off-label side effect, pathway, transporter, and indication data are calculated. At first, NDD uses a heuristic similarity selection process and then integrates the selected similarities with a nonlinear similarity fusion method to achieve high-level features. Afterward, it uses a neural network for interaction prediction. The similarity selection and similarity integration parts of NDD have been proposed in previous studies of other problems. Our novelty is to combine these parts with new neural network architecture and apply these approaches in the context of DDI prediction. We compared NDD with six machine learning classifiers and six state-of-the-art graph-based methods on three benchmark datasets. NDD achieved superior performance in cross-validation with AUPR ranging from 0.830 to 0.947, AUC from 0.954 to 0.994 and F-measure from 0.772 to 0.902. Moreover, cumulative evidence in case studies on numerous drug pairs, further confirm the ability of NDD to predict unknown DDIs. The evaluations corroborate that NDD is an efficient method for predicting unknown DDIs. The data and implementation of NDD are available at https://github.com/nrohani/NDD.

scholarly journals MDNN: A Multimodal Deep Neural Network for Predicting Drug-Drug Interaction Events

2021 ◽  
Author(s):  
Tengfei Lyu ◽  
Jianliang Gao ◽  
Ling Tian ◽  
Zhao Li ◽  
Peng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Drug Interaction ◽  
Deep Neural Network ◽  
Multimodal Fusion ◽  
Drug Knowledge ◽  
Appropriate Therapy ◽  
Multimodal Representations ◽  
Drug Drug Interaction ◽  
Multiple Drugs ◽  
Heterogeneous Feature

The interaction of multiple drugs could lead to serious events, which causes injuries and huge medical costs. Accurate prediction of drug-drug interaction (DDI) events can help clinicians make effective decisions and establish appropriate therapy programs. Recently, many AI-based techniques have been proposed for predicting DDI associated events. However, most existing methods pay less attention to the potential correlations between DDI events and other multimodal data such as targets and enzymes. To address this problem, we propose a Multimodal Deep Neural Network (MDNN) for DDI events prediction. In MDNN, we design a two-pathway framework including drug knowledge graph (DKG) based pathway and heterogeneous feature (HF) based pathway to obtain drug multimodal representations. Finally, a multimodal fusion neural layer is designed to explore the complementary among the drug multimodal representations. We conduct extensive experiments on real-world dataset. The results show that MDNN can accurately predict DDI events and outperform the state-of-the-art models.

scholarly journals KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction

2020 ◽  
Author(s):  
Xuan Lin ◽  
Zhe Quan ◽  
Zhi-Jie Wang ◽  
Tengfei Ma ◽  
Xiangxiang Zeng
Keyword(s):  
Neural Network ◽  
Drug Interaction ◽  
Computational Models ◽  
High Order ◽  
Knowledge Graph ◽  
Long Distance ◽  
Neighborhood Information ◽  
Multiple Data ◽  
Drug Drug Interaction ◽  
The Rich

Drug-drug interaction (DDI) prediction is a challenging problem in pharmacology and clinical application, and effectively identifying potential DDIs during clinical trials is critical for patients and society. Most of existing computational models with AI techniques often concentrate on integrating multiple data sources and combining popular embedding methods together. Yet, researchers pay less attention to the potential correlations between drug and other entities such as targets and genes. Moreover, recent studies also adopted knowledge graph (KG) for DDI prediction. Yet, this line of methods learn node latent embedding directly, but they are limited in obtaining the rich neighborhood information of each entity in the KG. To address the above limitations, we propose an end-to-end framework, called Knowledge Graph Neural Network (KGNN), to resolve the DDI prediction. Our framework can effectively capture drug and its potential neighborhoods by mining their associated relations in KG. To extract both high-order structures and semantic relations of the KG, we learn from the neighborhoods for each entity in the KG as their local receptive, and then integrate neighborhood information with bias from representation of the current entity. This way, the receptive field can be naturally extended to multiple hops away to model high-order topological information and to obtain drugs potential long-distance correlations. We have implemented our method and conducted experiments based on several widely-used datasets. Empirical results show that KGNN outperforms the classic and state-of-the-art models.

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