scholarly journals Generating novel molecule for target protein (SARS-CoV-2) using drug–target interaction based on graph neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Amit Ranjan ◽  
Shivansh Shukla ◽  
Deepanjan Datta ◽  
Rajiv Misra
Keyword(s):  
Neural Network ◽  
Drug Target ◽  
Target Protein ◽  
Target Interaction

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 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.

scholarly journals AttentionSiteDTI: Attention Based Model for Predicting Drug-Target Interaction Using 3D Structure of Protein Binding Sites

2021 ◽  
Author(s):  
Mehdi Yazdani-Jahromi ◽  
Niloofar Yousefi ◽  
Aida Tayebi ◽  
Ozlem Ozmen Garibay ◽  
Sudipta Seal ◽  
...  
Keyword(s):  
Deep Learning ◽  
Computational Methods ◽  
Binding Sites ◽  
Drug Target ◽  
Critical Role ◽  
3D Structure ◽  
Target Protein ◽  
Binding Interaction ◽  
Target Interaction ◽  
Proposed Model

Investigating drug-target interactions plays a critical role in drug design and discovery. The vast chemical and proteomic space, along with the cost associated with invirto experiments motivate the use of computational methods to narrow down the search space for novel interaction of drug target pairs. Among all computational methods, deep learning algorithms have gained increased attention due to their power in automatically learning and extracting feature representations, and therefore identifying, processing and extrapolating complex hidden interactions between drugs and targets. In this study, we introduce and implement a new graph-based prediction model called AttentionSiteDTI. Our proposed model utilize the binding sites (pockets) of the proteins as the input for the target protein, and it uses a self-attention mechanism to make the model learn which binding sites of the protein interact with a given ligand. This, indeed, complements the black-box nature of deep learning-based methods and enables interpretability, while achieving state of the art results in drug target interaction prediction task on three datasets. The AttentionSite DTI achieves AUC of 0.97 (for seen proteins), 0.94 (for unseen proteins) in the customized BindingDB dataset, 0.971 in the DUD-E dataset, and 0.991 in the human dataset. In general, the prediction results on these datasets show the superiority of our AttentionSiteDTI compared to previous graph-based models, and our ablation studies proves the effectiveness of our proposed model in prediction of drug-target interactions. In addition, through multidisciplinary collaboration in this work, we further experimentally evaluate the practical potential of our proposed approach. To achieve this, we first computationally predict binding interaction of some candidate compounds with a target protein, and then experimentally validate the binding interactions for these pairs in the laboratory. The high agreement between the computationally-predicted and experimentally observed (measured) drug-target interactions illustrates the potential of our AttentionSiteDTI as effective pre-screening tool in drug repurposing applications.

Predicting Drug–Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation

2019 ◽  
Vol 59 (9) ◽  
pp. 3981-3988 ◽  
Author(s):  
Jaechang Lim ◽  
Seongok Ryu ◽  
Kyubyong Park ◽  
Yo Joong Choe ◽  
Jiyeon Ham ◽  
...  
Keyword(s):  
Neural Network ◽  
Drug Target ◽  
3D Structure ◽  
Graph Representation ◽  
Target Interaction ◽  
Embedded Graph
Sign in / Sign up

Export Citation Format

Share Document