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

2020 ◽  
Vol 11 (9) ◽  
pp. 2531-2557 ◽  
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.

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

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.

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

2019 ◽  
Vol 20 (6) ◽  
pp. 492-494 ◽  
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.

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

2019 ◽  
Vol 20 (3) ◽  
pp. 194-202 ◽  
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.

scholarly journals DEEPScreen: High Performance Drug-Target Interaction Prediction with Convolutional Neural Networks Using 2-D Structural Compound Representations

2018 ◽  
Author(s):  
Ahmet Sureyya Rifaioglu ◽  
Volkan Atalay ◽  
Maria Jesus Martin ◽  
Rengul Cetin-Atalay ◽  
Tunca Dogan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Drug Discovery ◽  
Drug Target ◽  
Large Scale ◽  
Experimental Studies ◽  
Drug Candidate ◽  
Support Vector ◽  
Target Proteins ◽  
Interaction Prediction

The identification of physical interactions between drug candidate chemical substances and target biomolecules is an important step in the process of drug discovery, where the standard procedure is the systematic screening of chemical compounds against pre-selected target proteins. However, experimental screening procedures are expensive and time consuming, therefore, it is not possible to carry out comprehensive tests. Within the last decade, computational approaches have been developed with the objective of aiding experimental studies by predicting novel drug-target interactions (DTI), via the construction and application of statistical models. In this study, we propose a large-scale DTI interaction prediction system, DEEPScreen, for early stage drug discovery, using convolutional deep neural networks. One of the main advantages of DEEPScreen is employing readily available simple 2-D images of compounds at the input level instead of engineered complex feature vectors that displayed limited performance in DTI prediction tasks previously. DEEPScreen learns complex features inherently from the 2-D molecular representations, thus producing highly accurate predictions. DEEPScreen system was trained for 704 target proteins (using ChEMBL curated bioactivity data) and finalized with rigorous hyper-parameter optimization tests. We compared the performance of DEEPScreen against shallow classifiers such as the random forest, logistic regression and support vector machines, to indicate the effectiveness of the proposed deep learning approach. Additionally, we compared DEEPScreen with other deep learning based state-of-the-art DTI predictors on widely used benchmark datasets and showed that DEEPScreen produces better or comparable results to the top performers. The method proposed here can be employed to computationally scan a large portion of the recorded drug candidate compound and protein spaces to aid the experimentalists working in the field of drug discovery and repurposing by providing a preselection of interesting novel DTIs.

scholarly journals Application of Machine Learning for Drug–Target Interaction Prediction

2021 ◽  
Vol 12 ◽  
Author(s):  
Lei Xu ◽  
Xiaoqing Ru ◽  
Rong Song
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
High Performance ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Learning Techniques ◽  
Present Generation ◽  
Applications Of Machine Learning

Exploring drug–target interactions by biomedical experiments requires a lot of human, financial, and material resources. To save time and cost to meet the needs of the present generation, machine learning methods have been introduced into the prediction of drug–target interactions. The large amount of available drug and target data in existing databases, the evolving and innovative computer technologies, and the inherent characteristics of various types of machine learning have made machine learning techniques the mainstream method for drug–target interaction prediction research. In this review, details of the specific applications of machine learning in drug–target interaction prediction are summarized, the characteristics of each algorithm are analyzed, and the issues that need to be further addressed and explored for future research are discussed. The aim of this review is to provide a sound basis for the construction of high-performance models.

Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization

2017 ◽  
Vol 14 (3) ◽  
pp. 646-656 ◽  
Author(s):  
Ali Ezzat ◽  
Peilin Zhao ◽  
Min Wu ◽  
Xiao-Li Li ◽  
Chee-Keong Kwoh
Keyword(s):  
Matrix Factorization ◽  
Drug Target ◽  
Target Interaction ◽  
Interaction Prediction
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