Prediction of Drug-Target Binding Affinity by An Ensemble Learning System with Network Fusion Information

2021 ◽  
Vol 16 ◽  
Author(s):  
Cheng Lin Zhang ◽  
You Zhi Zhang ◽  
Bing Wang ◽  
Peng Chen
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Drug Target ◽  
Integrated System ◽  
Learning System ◽  
New Drugs ◽  
Subsequent Work ◽  
Network Information ◽  
Similarity Networks ◽  
Target Binding

Background: Verifying interactions between drugs and targets is key to discover new drugs. Many computational methods have been developed to predict drug-target interactions and performed successfully, but challenges still exist in the field. Objective: We try to develop a machine learning method to predict drug-target affinity, which can determine the strength of the binding relationship between drug and target. Method: This paper proposes an integrated machine learning system for drug-target binding affinity prediction based on network fusion. First, multiple similarity networks representing drugs or targets are calculated. Second, multiple networks representing drugs (targets) are fused separately. Finally, the characteristic information of splicing drugs and targets was used for model construction and training. By integrating multiple similarity networks, the model fully embodies the complementarity of network information, and the most complete features of information can be obtained after the redundancy is removed. Results: Experimental results showed that our model obtained good results for DTI binding affinity. Conclusion: It is still challenging to predict drug-target affinity. This paper proposes to use an integrated system of fusion network information for addressing the issue, and the proposed method performs well, which can provide a certain data basis for the subsequent work. Website: https://www.dlearningapp.com/web/inmpba.htm

scholarly journals GraphDTA: Predicting drug–target binding affinity with graph neural networks

2020 ◽  
Author(s):  
Thin Nguyen ◽  
Hang Le ◽  
Thomas P Quinn ◽  
Tri Nguyen ◽  
Thuc Duy Le ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Binding Affinity ◽  
Drug Target ◽  
Interaction Strength ◽  
Drug Repurposing ◽  
New Drugs ◽  
Learning Models ◽  
Target Binding ◽  
Graph Neural Networks

Abstract   The development of new drugs is costly, time consuming, and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to repurpose drugs effectively, it is useful to know which proteins are targeted by which drugs. Computational models that estimate the interaction strength of new drug–target pairs have the potential to expedite drug repurposing. Several models have been proposed for this task. However, these models represent the drugs as strings, which is not a natural way to represent molecules. We propose a new model called GraphDTA that represents drugs as graphs and uses graph neural networks to predict drug–target affinity. We show that graph neural networks not only predict drug–target affinity better than non-deep learning models, but also outperform competing deep learning methods. Our results confirm that deep learning models are appropriate for drug–target binding affinity prediction, and that representing drugs as graphs can lead to further improvements. Availability of data and materials The proposed models are implemented in Python. Related data, pre-trained models, and source code are publicly available at https://github.com/thinng/GraphDTA. All scripts and data needed to reproduce the post-hoc statistical analysis are available from https://doi.org/10.5281/zenodo.3603523.

scholarly journals Revealing new therapeutic opportunities through drug target prediction: a class imbalance-tolerant machine learning approach

2020 ◽  
Vol 36 (16) ◽  
pp. 4490-4497
Author(s):  
Siqi Liang ◽  
Haiyuan Yu
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Drug Targets ◽  
Class Imbalance ◽  
Target Prediction ◽  
Drug Repurposing ◽  
New Drugs ◽  
Supplementary Information ◽  
Training Scheme ◽  
Drug Target Prediction

Abstract Motivation In silico drug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing targeted therapies for diseases. Results Here, we develop a robust method for drug target prediction by leveraging a class imbalance-tolerant machine learning framework with a novel training scheme. We incorporate novel features, including drug–gene phenotype similarity and gene expression profile similarity that capture information orthogonal to other features. We show that our classifier achieves robust performance and is able to predict gene targets for new drugs as well as drugs that potentially target unexplored genes. By providing newly predicted drug–target associations, we uncover novel opportunities of drug repurposing that may benefit cancer treatment through action on either known drug targets or currently undrugged genes. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals GANsDTA: Predicting Drug-Target Binding Affinity Using GANs

2020 ◽  
Vol 10 ◽  
Author(s):  
Lingling Zhao ◽  
Junjie Wang ◽  
Long Pang ◽  
Yang Liu ◽  
Jun Zhang
Keyword(s):  
Binding Affinity ◽  
Drug Target ◽  
Target Binding

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 Adding stochastic negative examples into machine learning improves molecular bioactivity prediction

2020 ◽  
Author(s):  
Elena L. Cáceres ◽  
Nicholas C. Mew ◽  
Michael J. Keiser
Keyword(s):  
Machine Learning ◽  
Drug Screening ◽  
Drug Target ◽  
Deep Neural Networks ◽  
Bioactivity Prediction ◽  
Screening Performance ◽  
Classification And Regression ◽  
Target Binding ◽  
Test Scenarios ◽  
Augmentation Procedure

ABSTRACTMultitask deep neural networks learn to predict ligand-target binding by example, yet public pharmacological datasets are sparse, imbalanced, and approximate. We constructed two hold-out benchmarks to approximate temporal and drug-screening test scenarios whose characteristics differ from a random split of conventional training datasets. We developed a pharmacological dataset augmentation procedure, Stochastic Negative Addition (SNA), that randomly assigns untested molecule-target pairs as transient negative examples during training. Under the SNA procedure, ligand drug-screening benchmark performance increases from R2 = 0.1926 ± 0.0186 to 0.4269±0.0272 (121.7%). This gain was accompanied by a modest decrease in the temporal benchmark (13.42%). SNA increases in drug-screening performance were consistent for classification and regression tasks and outperformed scrambled controls. Our results highlight where data and feature uncertainty may be problematic, but also show how leveraging uncertainty into training improves predictions of drug-target relationships.

scholarly journals GraphDTA: Predicting drug–target binding affinity with graph neural networks

2019 ◽  
Author(s):  
Thin Nguyen ◽  
Hang Le ◽  
Thomas P. Quinn ◽  
Tri Nguyen ◽  
Thuc Duy Le ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Binding Affinity ◽  
Drug Target ◽  
Interaction Strength ◽  
Drug Repurposing ◽  
Learning Models ◽  
Link Type ◽  
Target Binding ◽  
Graph Neural Networks

AbstractThe development of new drugs is costly, time consuming, and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to repurpose drugs effectively, it is useful to know which proteins are targeted by which drugs. Computational models that estimate the interaction strength of new drug--target pairs have the potential to expedite drug repurposing. Several models have been proposed for this task. However, these models represent the drugs as strings, which is not a natural way to represent molecules. We propose a new model called GraphDTA that represents drugs as graphs and uses graph neural networks to predict drug--target affinity. We show that graph neural networks not only predict drug--target affinity better than non-deep learning models, but also outperform competing deep learning methods. Our results confirm that deep learning models are appropriate for drug--target binding affinity prediction, and that representing drugs as graphs can lead to further improvements.Availability of data and materialsThe proposed models are implemented in Python. Related data, pre-trained models, and source code are publicly available at https://github.com/thinng/GraphDTA. All scripts and data needed to reproduce the post-hoc statistical analysis are available from https://doi.org/10.5281/[email protected]

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