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.

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.

Computational Model Development of Drug-Target Interaction Prediction: A Review

2019 ◽  
Vol 20 (6) ◽  
pp. 492-494 ◽  
Author(s):  
Qi Zhao ◽  
Haifan Yu ◽  
Mingxuan Ji ◽  
Yan Zhao ◽  
Xing Chen
Keyword(s):  
Drug Target ◽  
Prediction Models ◽  
Model Development ◽  
Computational Prediction ◽  
Medical Field ◽  
Research Directions ◽  
Target Interaction ◽  
Protein Target ◽  
Interaction Prediction ◽  
The Past

In the medical field, drug-target interactions are very important for the diagnosis and treatment of diseases, they also can help researchers predict the link between biomolecules in the biological field, such as drug-protein and protein-target correlations. Therefore, the drug-target research is a very popular study in both the biological and medical fields. However, due to the limitations of manual experiments in the laboratory, computational prediction methods for drug-target relationships are increasingly favored by researchers. In this review, we summarize several computational prediction models of the drug-target connections during the past two years, and briefly introduce their advantages and shortcomings. Finally, several further interesting research directions of drug-target interactions are listed.

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 SPVec: A Word2vec-Inspired Feature Representation Method for Drug-Target Interaction Prediction

2020 ◽  
Vol 7 ◽  
Author(s):  
Yu-Fang Zhang ◽  
Xiangeng Wang ◽  
Aman Chandra Kaushik ◽  
Yanyi Chu ◽  
Xiaoqi Shan ◽  
...  
Keyword(s):  
Drug Target ◽  
Feature Representation ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Representation Method

scholarly journals AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Seyedeh Zahra Sajadi ◽  
Mohammad Ali Zare Chahooki ◽  
Sajjad Gharaghani ◽  
Karim Abbasi
Keyword(s):  
Drug Discovery ◽  
Unsupervised Learning ◽  
Computational Methods ◽  
Drug Target ◽  
Experimental Results ◽  
Interaction Matrix ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Lab Experiments ◽  
Wet Lab

Abstract Background Drug–target interaction (DTI) plays a vital role in drug discovery. Identifying drug–target interactions related to wet-lab experiments are costly, laborious, and time-consuming. Therefore, computational methods to predict drug–target interactions are an essential task in the drug discovery process. Meanwhile, computational methods can reduce search space by proposing potential drugs already validated on wet-lab experiments. Recently, deep learning-based methods in drug-target interaction prediction have gotten more attention. Traditionally, DTI prediction methods' performance heavily depends on additional information, such as protein sequence and molecular structure of the drug, as well as deep supervised learning. Results This paper proposes a method based on deep unsupervised learning for drug-target interaction prediction called AutoDTI++. The proposed method includes three steps. The first step is to pre-process the interaction matrix. Since the interaction matrix is sparse, we solved the sparsity of the interaction matrix with drug fingerprints. Then, in the second step, the AutoDTI approach is introduced. In the third step, we post-preprocess the output of the AutoDTI model. Conclusions Experimental results have shown that we were able to improve the prediction performance. To this end, the proposed method has been compared to other algorithms using the same reference datasets. The proposed method indicates that the experimental results of running five repetitions of tenfold cross-validation on golden standard datasets (Nuclear Receptors, GPCRs, Ion channels, and Enzymes) achieve good performance with high accuracy.

scholarly journals Machine Learning for Drug-Target Interaction Prediction

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2208 ◽  
Author(s):  
Ruolan Chen ◽  
Xiangrong Liu ◽  
Shuting Jin ◽  
Jiawei Lin ◽  
Juan Liu
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Drug Target ◽  
High Efficiency ◽  
Hierarchical Classification ◽  
Computational Prediction ◽  
Learning Approaches ◽  
Comprehensive Overview ◽  
Target Interaction

Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve as promising strategies for drug-target interaction (DTI) prediction. In this review, our goal is to focus on machine learning approaches and provide a comprehensive overview. First, we summarize a brief list of databases frequently used in drug discovery. Next, we adopt a hierarchical classification scheme and introduce several representative methods of each category, especially the recent state-of-the-art methods. In addition, we compare the advantages and limitations of methods in each category. Lastly, we discuss the remaining challenges and future outlook of machine learning in DTI prediction. This article may provide a reference and tutorial insights on machine learning-based DTI prediction for future researchers.

scholarly journals ELECTRA-DTA: A New Compound-protein Binding Affinity Prediction Model based on the Contextualized Sequence Encoding

2021 ◽  
Author(s):  
junjie wang ◽  
NaiFeng Wen ◽  
Chunyu Wang ◽  
Lingling Zhao ◽  
Liang Cheng
Keyword(s):  
Drug Discovery ◽  
Binding Affinity ◽  
Drug Target ◽  
Large Scale ◽  
Prediction Models ◽  
Target Selection ◽  
Drug Repurposing ◽  
Representation Learning ◽  
Search Space ◽  
Feature Representation

Abstract Motivation: Drug-target binding affinity (DTA) reflects the strength of the drug-target interaction; therefore, predicting the DTA can considerably benefit drug discovery by narrowing the search space and pruning drug-target (DT) pairs with low binding affinity scores. Representation learning using deep neural networks has achieved promising performance compared with traditional machine learning methods; hence, extensive research efforts have been made in learning the feature representation of proteins and compounds. However, such feature representation learning relies on a large-scale labelled dataset, which is not always available.Results: We present an end-to-end deep learning framework, ELECTRA-DTA, to predict the binding affinity of drug-target pairs. This framework incorporates an unsupervised learning mechanism to train two ELECTRA-based contextual embedding models, one for protein amino acids and the other for compound SMILES string encoding. In addition, ELECTRA-DTA leverages a squeeze-and-excitation (SE) convolutional neural network block stacked over three fully connected layers to further capture the sequential and spatial features of the protein sequence and SMILES for the DTA regression task. Experimental evaluations show that ELECTRA-DTA outperforms various state-of-the-art DTA prediction models, especially with the challenging, interaction-sparse BindingDB dataset. In target selection and drug repurposing for COVID-19, ELECTRA-DTA also offers competitive performance, suggesting its potential in speeding drug discovery and generalizability for other compound- or protein-related computational tasks.

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