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 DeepDTA: deep drug–target binding affinity prediction

2018 ◽  
Vol 34 (17) ◽  
pp. i821-i829 ◽  
Author(s):  
Hakime Öztürk ◽  
Arzucan Özgür ◽  
Elif Ozkirimli
Keyword(s):  
Binding Affinity ◽  
Drug Target ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Target Binding

scholarly journals AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks

2020 ◽  
Vol 21 (22) ◽  
pp. 8424
Author(s):  
Yongbeom Kwon ◽  
Woong-Hee Shin ◽  
Junsu Ko ◽  
Juyong Lee
Keyword(s):  
Neural Network ◽  
Binding Affinity ◽  
Pearson Correlation ◽  
Complex Structure ◽  
Rational Drug Design ◽  
Scoring Functions ◽  
Binding Affinities ◽  
Ligand Complex ◽  
Binding Affinity Prediction ◽  
Affinity Prediction

Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to predict affinity. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3-D convolutional neural network layers. Our model was trained using the 3772 protein-ligand complexes from the refined set of the PDBbind-2016 database and tested using the core set of 285 complexes. The benchmark results show that the Pearson correlation coefficient between the predicted binding affinities by our model and the experimental data is 0.827, which is higher than the state-of-the-art binding affinity prediction scoring functions. Additionally, our method ranks the relative binding affinities of possible multiple binders of a protein quite accurately, comparable to the other scoring functions. Last, we measured which structural information is critical for predicting binding affinity and found that the complementarity between the protein and ligand is most important.

scholarly journals DeepFrag: A Deep Convolutional Neural Network for Fragment-based Lead Optimization

2021 ◽  
Author(s):  
Harrison Green ◽  
David Ryan Koes ◽  
Jacob D Durrant
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Convolutional Neural Network ◽  
Binding Affinity ◽  
Lead Optimization ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Computer Aided

Machine learning has been increasingly applied to the field of computer-aided drug discovery in recent years, leading to notable advances in binding-affinity prediction, virtual screening, and QSAR. Surprisingly, it is...

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