Computational Methods for the Prediction of Drug-Target Interactions from Drug Fingerprints and Protein Sequences by Stacked Auto-Encoder Deep Neural Network

2017 ◽  
pp. 46-58 ◽  
Author(s):  
Lei Wang ◽  
Zhu-Hong You ◽  
Xing Chen ◽  
Shi-Xiong Xia ◽  
Feng Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Computational Methods ◽  
Drug Target ◽  
Deep Neural Network ◽  
Protein Sequences

scholarly journals Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data

Pharmaceutics ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 377 ◽  
Author(s):  
Hanbi Lee ◽  
Wankyu Kim
Keyword(s):  
Neural Network ◽  
Drug Target ◽  
Large Scale ◽  
Deep Neural Network ◽  
Structural Information ◽  
Drug Repositioning ◽  
Expression Profiles ◽  
Ppi Network ◽  
Drug Induced ◽  
Integrated Network

Uncovering drug-target interactions (DTIs) is pivotal to understand drug mode-of-action (MoA), avoid adverse drug reaction (ADR), and seek opportunities for drug repositioning (DR). For decades, in silico predictions for DTIs have largely depended on structural information of both targets and compounds, e.g., docking or ligand-based virtual screening. Recently, the application of deep neural network (DNN) is opening a new path to uncover novel DTIs for thousands of targets. One important question is which features for targets are most relevant to DTI prediction. As an early attempt to answer this question, we objectively compared three canonical target features extracted from: (i) the expression profiles by gene knockdown (GEPs); (ii) the protein–protein interaction network (PPI network); and (iii) the pathway membership (PM) of a target gene. For drug features, the large-scale drug-induced transcriptome dataset, or the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset was used. All these features are closely related to protein function or drug MoA, of which utility is only sparsely investigated. In particular, few studies have compared the three types of target features in DNN-based DTI prediction under the same evaluation scheme. Among the three target features, the PM and the PPI network show similar performances superior to GEPs. DNN models based on both features consistently outperformed other machine learning methods such as naïve Bayes, random forest, or logistic regression.

scholarly journals Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network

2014 ◽  
Vol 35 (28) ◽  
pp. 2040-2046 ◽  
Author(s):  
James Lyons ◽  
Abdollah Dehzangi ◽  
Rhys Heffernan ◽  
Alok Sharma ◽  
Kuldip Paliwal ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Protein Sequences

scholarly journals A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network

2020 ◽  
Vol 21 (S13) ◽  
Author(s):  
Jiajie Peng ◽  
Jingyi Li ◽  
Xuequn Shang
Keyword(s):  
Neural Network ◽  
Drug Discovery ◽  
Drug Target ◽  
Deep Neural Network ◽  
Computational Prediction ◽  
Representation Learning ◽  
Feature Representation ◽  
New Drugs ◽  
Target Interaction ◽  
Interaction Prediction

Abstract Background Drug-target interaction prediction is of great significance for narrowing down the scope of candidate medications, and thus is a vital step in drug discovery. Because of the particularity of biochemical experiments, the development of new drugs is not only costly, but also time-consuming. Therefore, the computational prediction of drug target interactions has become an essential way in the process of drug discovery, aiming to greatly reducing the experimental cost and time. Results We propose a learning-based method based on feature representation learning and deep neural network named DTI-CNN to predict the drug-target interactions. We first extract the relevant features of drugs and proteins from heterogeneous networks by using the Jaccard similarity coefficient and restart random walk model. Then, we adopt a denoising autoencoder model to reduce the dimension and identify the essential features. Third, based on the features obtained from last step, we constructed a convolutional neural network model to predict the interaction between drugs and proteins. The evaluation results show that the average AUROC score and AUPR score of DTI-CNN were 0.9416 and 0.9499, which obtains better performance than the other three existing state-of-the-art methods. Conclusions All the experimental results show that the performance of DTI-CNN is better than that of the three existing methods and the proposed method is appropriately designed.

Predicting protein–protein interactions from protein sequences by a stacked sparse autoencoder deep neural network

2017 ◽  
Vol 13 (7) ◽  
pp. 1336-1344 ◽  
Author(s):  
Yan-Bin Wang ◽  
Zhu-Hong You ◽  
Xiao Li ◽  
Tong-Hai Jiang ◽  
Xing Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Protein Interactions ◽  
Deep Neural Network ◽  
Protein Sequences ◽  
Biological Processes ◽  
Protein Protein Interactions ◽  
P Protein ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

Protein–protein interactions (PPIs) play an important role in most of the biological processes.

Identifying drug–target interactions based on graph convolutional network and deep neural network

2020 ◽  
Author(s):  
Tianyi Zhao ◽  
Yang Hu ◽  
Linda R Valsdottir ◽  
Tianyi Zang ◽  
Jiajie Peng
Keyword(s):  
Neural Network ◽  
Drug Discovery ◽  
Drug Target ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Feature Representation ◽  
Convolutional Network ◽  
Computational Approaches ◽  
Large Margin ◽  
Multiple Drugs

Abstract Identification of new drug–target interactions (DTIs) is an important but a time-consuming and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers have sought to identify DTIs using computational approaches. However, most existing methods construct drug networks and target networks separately, and then predict novel DTIs based on known associations between the drugs and targets without accounting for associations between drug–protein pairs (DPPs). To incorporate the associations between DPPs into DTI modeling, we built a DPP network based on multiple drugs and proteins in which DPPs are the nodes and the associations between DPPs are the edges of the network. We then propose a novel learning-based framework, ‘graph convolutional network (GCN)-DTI’, for DTI identification. The model first uses a graph convolutional network to learn the features for each DPP. Second, using the feature representation as an input, it uses a deep neural network to predict the final label. The results of our analysis show that the proposed framework outperforms some state-of-the-art approaches by a large margin.

scholarly journals DNN-DTIs: improved drug-target interactions prediction using XGBoost feature selection and deep neural network

2020 ◽  
Author(s):  
Cheng Chen ◽  
Han Shi ◽  
Yu Han ◽  
Zhiwen Jiang ◽  
Xuefeng Cui ◽  
...  
Keyword(s):  
Neural Network ◽  
Drug Target ◽  
Deep Neural Network ◽  
Drug Repositioning ◽  
Feature Subset ◽  
Layer By Layer ◽  
New Ideas ◽  
Experimental Approaches ◽  
Scoring Matrix ◽  
Research Analysis

ABSTRACTResearch, analysis, and prediction of drug-target interactions (DTIs) play an important role in understanding drug mechanisms, drug repositioning and design. Machine learning (ML)-based methods for DTIs prediction can mitigate the shortcomings of time-consuming and labor-intensive experimental approaches, providing new ideas and insights for drug design. We propose a novel pipeline for predicting drug-target interactions, called DNN-DTIs. First, the target information is characterized by pseudo-amino acid composition, pseudo position-specific scoring matrix, conjoint triad, composition, transition and distribution, Moreau-Broto autocorrelation, and structure feature. Then, the drug compounds are encoded using substructure fingerprint. Next, we utilize XGBoost to determine nonredundant and important feature subset, then the optimized and balanced sample vectors could be obtained through SMOTE. Finally, a DTIs predictor, DNN-DTIs, is developed based on deep neural network (DNN) via layer-by-layer learning. Experimental results indicate that DNN-DTIs achieves outstanding performance than other predictors with the ACC values of 98.78%, 98.60%, 97.98%, 98.24% and 98.00% on Enzyme, Ion Channels (IC), GPCR, Nuclear Receptors (NR) and Kuang's dataset. Therefore, DNN-DTIs's accurate prediction performance on Network1 and Network2 make it logical choice for contributing to the study of DTIs, especially, the drug repositioning and new usage of old drugs.

scholarly journals DNN-DTIs: improved drug-target interactions prediction using XGBoost feature selection and deep neural network

2021 ◽  
pp. 104676
Author(s):  
Cheng Chen ◽  
Han Shi ◽  
Zhiwen Jiang ◽  
Adil Salhi ◽  
Ruixin Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Drug Target ◽  
Deep Neural Network

scholarly journals A Computational-Based Method for Predicting Drug–Target Interactions by Using Stacked Autoencoder Deep Neural Network

2018 ◽  
Vol 25 (3) ◽  
pp. 361-373 ◽  
Author(s):  
Lei Wang ◽  
Zhu-Hong You ◽  
Xing Chen ◽  
Shi-Xiong Xia ◽  
Feng Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Drug Target ◽  
Deep Neural Network ◽  
Stacked Autoencoder
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