Logistic matrix factorisation and generative adversarial neural network-based method for predicting drug-target interactions

2021 ◽  
Author(s):  
Sarra Itidal Abbou ◽  
Hafida Bouziane ◽  
Abdallah Chouarfia
Keyword(s):  
Neural Network ◽  
Drug Target ◽  
Matrix Factorisation

scholarly journals FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction

Heliyon ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e03444 ◽  
Author(s):  
Farshid Rayhan ◽  
Sajid Ahmed ◽  
Zaynab Mousavian ◽  
Dewan Md Farid ◽  
Swakkhar Shatabda
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Drug Target ◽  
Deep Convolutional Neural Network ◽  
Target Interaction ◽  
Interaction Prediction

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.

Predicting Drug-target Interactions via FM-DNN Learning

2020 ◽  
Vol 15 (1) ◽  
pp. 68-76 ◽  
Author(s):  
Jihong Wang ◽  
Hao Wang ◽  
Xiaodan Wang ◽  
Huiyou Chang
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Drug Target ◽  
Expert Knowledge ◽  
Drug Repositioning ◽  
Feature Learning ◽  
Second Order ◽  
High Order ◽  
Feature Engineering ◽  
Basic Features

Background: Identifying Drug-Target Interactions (DTIs) is a major challenge for current drug discovery and drug repositioning. Compared to traditional experimental approaches, in silico methods are fast and inexpensive. With the increase in open-access experimental data, numerous computational methods have been applied to predict DTIs. Methods: In this study, we propose an end-to-end learning model of Factorization Machine and Deep Neural Network (FM-DNN), which emphasizes both low-order (first or second order) and high-order (higher than second order) feature interactions without any feature engineering other than raw features. This approach combines the power of FM and DNN learning for feature learning in a new neural network architecture. Results: The experimental DTI basic features include drug characteristics (609), target characteristics (1819), plus drug ID, target ID, total 2430. We compare 8 models such as SVM, GBDT, WIDE-DEEP etc, the FM-DNN algorithm model obtains the best results of AUC(0.8866) and AUPR(0.8281). Conclusion: Feature engineering is a job that requires expert knowledge, it is often difficult and time-consuming to achieve good results. FM-DNN can auto learn a lower-order expression by FM and a high-order expression by DNN.FM-DNN model has outstanding advantages over other commonly used models.

Predicting drug-target affinity based on recurrent neural networks and graph convolutional neural networks

2021 ◽  
Vol 24 ◽  
Author(s):  
Qingyu Tian ◽  
Mao Ding ◽  
Hui Yang ◽  
Caibin Yue ◽  
Yue Zhong ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Drug Target ◽  
Channel Model ◽  
Search Space ◽  
Topological Features ◽  
Latent Features ◽  
Chemical Background

Background: Drug development requires a lot of money and time, and the outcome of the challenge is unknown. So, there is an urgent need for researchers to find a new approach that can reduce costs. Therefore, the identification of drug-target interactions (DTIs) has been a critical step in the early stages of drug discovery. These computational methods aim to narrow the search space for novel DTIs and to elucidate the functional background of drugs. Most of the methods developed so far use binary classification to predict the presence or absence of interactions between the drug and the target. However, it is more informative, but also more challenging, to predict the strength of the binding between a drug and its target. If the strength is not strong enough, such a DTI may not be useful. Hence, the development of methods to predict drug-target affinity (DTA) is of significant importance. Method: We have improved the Graph DTA model from a dual-channel model to a triple-channel model. We interpreted the target/protein sequences as time series and extracted their features using the LSTM network. For the drug, we considered both the molecular structure and the local chemical background, retaining the four variant networks used in Graph DTA to extract the topological features of the drug and capturing the local chemical background of the atoms in the drug by using BiGRU. Thus, we obtained the latent features of the target and two latent features of the drug. The connection of these three feature vectors is then input into a 2-layer FC network, and a valuable binding affinity is output. Result: We use the Davis and Kiba datasets, using 80% of the data for training and 20% of the data for validation. Our model shows better performance by comparing it with the experimental results of Graph DTA. Conclusion: In this paper, we altered the Graph DTA model to predict drug-target affinity. It represents the drug as a graph, and extracts the two-dimensional drug information using a graph convolutional neural network. Simultaneously, the drug and protein targets are represented as a word vector, and the convolutional neural network is used to extract the time series information of the drug and the target. We demonstrate that our improved method has better performance than the original method. In particular, our model has better performance in the evaluation of benchmark databases.

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.

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