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

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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The Computational Models of Drug-target Interaction Prediction

Protein and Peptide Letters ◽

10.2174/0929866526666190410124110 ◽

2020 ◽

Vol 27 (5) ◽

pp. 348-358 ◽

Cited By ~ 3

Author(s):

Yijie Ding ◽

Jijun Tang ◽

Fei Guo

Keyword(s):

Machine Learning ◽

Computational Methods ◽

Drug Target ◽

Computational Models ◽

Data Sets ◽

Target Interaction ◽

Interaction Prediction ◽

The Past ◽

Benchmark Data ◽

Precision Recall Curve

:The identification of Drug-Target Interactions (DTIs) is an important process in drug discovery and medical research. However, the tradition experimental methods for DTIs identification are still time consuming, extremely expensive and challenging. In the past ten years, various computational methods have been developed to identify potential DTIs. In this paper, the identification methods of DTIs are summarized. What's more, several state-of-the-art computational methods are mainly introduced, containing network-based method and machine learning-based method. In particular, for machine learning-based methods, including the supervised and semisupervised models, have essential differences in the approach of negative samples. Although these effective computational models in identification of DTIs have achieved significant improvements, network-based and machine learning-based methods have their disadvantages, respectively. These computational methods are evaluated on four benchmark data sets via values of Area Under the Precision Recall curve (AUPR).

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

Bioinformatics ◽

10.1093/bioinformatics/btaa1005 ◽

2020 ◽

Author(s):

Kexin Huang ◽

Tianfan Fu ◽

Lucas M Glass ◽

Marinka Zitnik ◽

Cao Xiao ◽

...

Keyword(s):

Deep Learning ◽

Drug Target ◽

Prediction Models ◽

State Of The Art ◽

Supplementary Information ◽

Target Interaction ◽

Interaction Prediction ◽

Computer Scientists ◽

Benchmark Datasets ◽

Biomedical Field

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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AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders

BMC Bioinformatics ◽

10.1186/s12859-021-04127-2 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Seyedeh Zahra Sajadi ◽

Mohammad Ali Zare Chahooki ◽

Sajjad Gharaghani ◽

Karim Abbasi

Keyword(s):

Drug Discovery ◽

Unsupervised Learning ◽

Computational Methods ◽

Drug Target ◽

Experimental Results ◽

Interaction Matrix ◽

Target Interaction ◽

Interaction Prediction ◽

Lab Experiments ◽

Wet Lab

Abstract Background Drug–target interaction (DTI) plays a vital role in drug discovery. Identifying drug–target interactions related to wet-lab experiments are costly, laborious, and time-consuming. Therefore, computational methods to predict drug–target interactions are an essential task in the drug discovery process. Meanwhile, computational methods can reduce search space by proposing potential drugs already validated on wet-lab experiments. Recently, deep learning-based methods in drug-target interaction prediction have gotten more attention. Traditionally, DTI prediction methods' performance heavily depends on additional information, such as protein sequence and molecular structure of the drug, as well as deep supervised learning. Results This paper proposes a method based on deep unsupervised learning for drug-target interaction prediction called AutoDTI++. The proposed method includes three steps. The first step is to pre-process the interaction matrix. Since the interaction matrix is sparse, we solved the sparsity of the interaction matrix with drug fingerprints. Then, in the second step, the AutoDTI approach is introduced. In the third step, we post-preprocess the output of the AutoDTI model. Conclusions Experimental results have shown that we were able to improve the prediction performance. To this end, the proposed method has been compared to other algorithms using the same reference datasets. The proposed method indicates that the experimental results of running five repetitions of tenfold cross-validation on golden standard datasets (Nuclear Receptors, GPCRs, Ion channels, and Enzymes) achieve good performance with high accuracy.

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A Machine Learning Approach for Drug‐target Interaction Prediction using Wrapper Feature Selection and Class Balancing

Molecular Informatics ◽

10.1002/minf.201900062 ◽

2020 ◽

Vol 39 (5) ◽

pp. 1900062 ◽

Cited By ~ 4

Author(s):

Shweta Redkar ◽

Sukanta Mondal ◽

Alex Joseph ◽

K. S. Hareesha

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Drug Target ◽

Learning Approach ◽

Target Interaction ◽

Interaction Prediction ◽

Machine Learning Approach ◽

Wrapper Feature Selection

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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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