DeepPurpose: a deep learning library for drug–target interaction prediction

Abstract Summary Accurate prediction of drug–target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use DL library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art performance of DeepPurpose on several benchmark datasets. Availability and implementation https://github.com/kexinhuang12345/DeepPurpose. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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

Current Protein and Peptide Science ◽

10.2174/1389203720666190123164310 ◽

2019 ◽

Vol 20 (6) ◽

pp. 492-494 ◽

Cited By ~ 7

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.

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Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction

Current Drug Metabolism ◽

10.2174/1389200219666180821094047 ◽

2019 ◽

Vol 20 (3) ◽

pp. 194-202 ◽

Cited By ~ 14

Author(s):

Wen Zhang ◽

Weiran Lin ◽

Ding Zhang ◽

Siman Wang ◽

Jingwen Shi ◽

...

Keyword(s):

Machine Learning ◽

Computational Methods ◽

Drug Target ◽

Prediction Models ◽

Prediction Methods ◽

Crucial Issue ◽

Target Interaction ◽

Interaction Prediction ◽

Recent Advances ◽

Drug Similarity

Background:The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods.Results:In the paper, we review the recent advances in machine learning-based drug-target interaction prediction. First, we briefly introduce the datasets and data, and summarize features for drugs and targets which can be extracted from different data. Since drug-drug similarity and target-target similarity are important for many machine learning prediction models, we introduce how to calculate similarities based on data or features. Different machine learningbased drug-target interaction prediction methods can be proposed by using different features or information. Thus, we summarize, analyze and compare different machine learning-based prediction methods.Conclusion:This study provides the guide to the development of computational methods for the drug-target interaction prediction.

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Deep learning-based transcriptome data classification for drug-target interaction prediction

BMC Genomics ◽

10.1186/s12864-018-5031-0 ◽

2018 ◽

Vol 19 (S7) ◽

Cited By ~ 22

Author(s):

Lingwei Xie ◽

Song He ◽

Xinyu Song ◽

Xiaochen Bo ◽

Zhongnan Zhang

Keyword(s):

Deep Learning ◽

Drug Target ◽

Data Classification ◽

Transcriptome Data ◽

Target Interaction ◽

Interaction Prediction

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Drug—target interaction prediction with a deep-learning-based model

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) ◽

10.1109/bibm.2017.8217693 ◽

2017 ◽

Cited By ~ 4

Author(s):

Lingwei Xie ◽

Zhongnan Zhang ◽

Song He ◽

Xiaochen Bo ◽

Xinyu Song

Keyword(s):

Deep Learning ◽

Drug Target ◽

Target Interaction ◽

Interaction Prediction

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Drug-target Interaction Prediction via Multiple Output Deep Learning

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) ◽

10.1109/bibm49941.2020.9313488 ◽

2020 ◽

Author(s):

Qing Ye ◽

Xiaolong Zhang ◽

Xiaoli Lin

Keyword(s):

Deep Learning ◽

Drug Target ◽

Target Interaction ◽

Interaction Prediction

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Drug-Target Interaction Prediction Based on Multisource Information Weighted Fusion

Contrast Media & Molecular Imaging ◽

10.1155/2021/6044256 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Shuaiqi Liu ◽

Jingjie An ◽

Jie Zhao ◽

Shuhuan Zhao ◽

Hui Lv ◽

...

Keyword(s):

Drug Target ◽

Operating Characteristic ◽

State Of The Art ◽

Characteristic Curve ◽

Relationship Matrix ◽

Target Interaction ◽

Interaction Prediction ◽

Weighted Fusion ◽

Precision Recall Curve ◽

The Relationship

Recently, in most existing studies, it is assumed that there are no interaction relationships between drugs and targets with unknown interactions. However, unknown interactions mean the relationships between drugs and targets have just not been confirmed. In this paper, samples for which the relationship between drugs and targets has not been determined are considered unlabeled. A weighted fusion method of multisource information is proposed to screen drug-target interactions. Firstly, some drug-target pairs which may have interactions are selected. Secondly, the selected drug-target pairs are added to the positive samples, which are regarded as known to have interaction relationships, and the original interaction relationship matrix is revised. Finally, the revised datasets are used to predict the interaction derived from the bipartite local model with neighbor-based interaction profile inferring (BLM-NII). Experiments demonstrate that the proposed method has greatly improved specificity, sensitivity, precision, and accuracy compared with the BLM-NII method. In addition, compared with several state-of-the-art methods, the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPR) of the proposed method are excellent.

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Drug-Target Interaction Prediction: End-to-End Deep Learning Approach

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2020.2977335 ◽

2020 ◽

pp. 1-1 ◽

Cited By ~ 4

Author(s):

Nelson R. C. Monteiro ◽

Bernardete Ribeiro ◽

Joel Arrais

Keyword(s):

Deep Learning ◽

Drug Target ◽

Learning Approach ◽

Target Interaction ◽

Interaction Prediction ◽

End To End

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DEEPScreen: high performance drug–target interaction prediction with convolutional neural networks using 2-D structural compound representations

Chemical Science ◽

10.1039/c9sc03414e ◽

2020 ◽

Vol 11 (9) ◽

pp. 2531-2557 ◽

Cited By ~ 14

Author(s):

Ahmet Sureyya Rifaioglu ◽

Esra Nalbat ◽

Volkan Atalay ◽

Maria Jesus Martin ◽

Rengul Cetin-Atalay ◽

...

Keyword(s):

Neural Networks ◽

Small Molecules ◽

Drug Target ◽

High Performance ◽

Prediction Models ◽

Drug Candidate ◽

Binding Properties ◽

Target Interaction ◽

Interaction Prediction ◽

Specific Prediction

The DEEPScreen system is composed of 704 target protein specific prediction models, each independently trained using experimental bioactivity measurements against many drug candidate small molecules, and optimized according to the binding properties of the target proteins.

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