scholarly journals Drug–target affinity prediction using graph neural network and contact maps

RSC Advances ◽  
2020 ◽  
Vol 10 (35) ◽  
pp. 20701-20712 ◽  
Author(s):  
Mingjian Jiang ◽  
Zhen Li ◽  
Shugang Zhang ◽  
Shuang Wang ◽  
Xiaofeng Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Drug Target ◽  
Affinity Prediction ◽  
Contact Maps ◽  
Protein Graphs

Prediction of drug–target affinity by constructing both molecule and protein graphs.

scholarly journals MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction

2022 ◽  
Author(s):  
Ziduo Yang ◽  
Weihe Zhong ◽  
Lu Zhao ◽  
Calvin Yu-Chian Chen
Keyword(s):  
Neural Network ◽  
Binding Affinity ◽  
Drug Target ◽  
Global Structure ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Target Binding

MGraphDTA is designed to capture the local and global structure of a compound simultaneously for drug–target affinity prediction and can provide explanations that are consistent with pharmacologists.

scholarly journals Prediction of drug–target binding affinity using similarity-based convolutional neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jooyong Shim ◽  
Zhen-Yu Hong ◽  
Insuk Sohn ◽  
Changha Hwang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Binding Affinity ◽  
Drug Target ◽  
Binary Classification ◽  
Binding Affinities ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Target Binding ◽  
Public Datasets

AbstractIdentifying novel drug–target interactions (DTIs) plays an important role in drug discovery. Most of the computational methods developed for predicting DTIs use binary classification, whose goal is to determine whether or not a drug–target (DT) pair interacts. However, it is more meaningful but also more challenging to predict the binding affinity that describes the strength of the interaction between a DT pair. If the binding affinity is not sufficiently large, such drug may not be useful. Therefore, the methods for predicting DT binding affinities are very valuable. The increase in novel public affinity data available in the DT-related databases enables advanced deep learning techniques to be used to predict binding affinities. In this paper, we propose a similarity-based model that applies 2-dimensional (2D) convolutional neural network (CNN) to the outer products between column vectors of two similarity matrices for the drugs and targets to predict DT binding affinities. To our best knowledge, this is the first application of 2D CNN in similarity-based DT binding affinity prediction. The validation results on multiple public datasets show that the proposed model is an effective approach for DT binding affinity prediction and can be quite helpful in drug development process.

scholarly journals Detecting distant-homology protein structures by aligning deep neural-network based contact maps

2019 ◽  
Vol 15 (10) ◽  
pp. e1007411 ◽  
Author(s):  
Wei Zheng ◽  
Qiqige Wuyun ◽  
Yang Li ◽  
S. M. Mortuza ◽  
Chengxin Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Protein Structures ◽  
Contact Maps

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

Using entropy of drug and protein graphs to predict FDA drug-target network: Theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica

2011 ◽  
Vol 46 (4) ◽  
pp. 1074-1094 ◽  
Author(s):  
Francisco Prado-Prado ◽  
Xerardo García-Mera ◽  
Paula Abeijón ◽  
Nerea Alonso ◽  
Olga Caamaño ◽  
...  
Keyword(s):  
Experimental Study ◽  
Drug Target ◽  
Fasciola Hepatica ◽  
Mao Inhibitors ◽  
Target Network ◽  
Protein Graphs
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