Erratum to: Machine learning approaches and databases for prediction of drug–target interaction: a survey paper

Abstract The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

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Machine Learning for Drug-Target Interaction Prediction

Molecules ◽

10.3390/molecules23092208 ◽

2018 ◽

Vol 23 (9) ◽

pp. 2208 ◽

Cited By ~ 35

Author(s):

Ruolan Chen ◽

Xiangrong Liu ◽

Shuting Jin ◽

Jiawei Lin ◽

Juan Liu

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Drug Target ◽

High Efficiency ◽

Hierarchical Classification ◽

Computational Prediction ◽

Learning Approaches ◽

Comprehensive Overview ◽

Target Interaction

Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve as promising strategies for drug-target interaction (DTI) prediction. In this review, our goal is to focus on machine learning approaches and provide a comprehensive overview. First, we summarize a brief list of databases frequently used in drug discovery. Next, we adopt a hierarchical classification scheme and introduce several representative methods of each category, especially the recent state-of-the-art methods. In addition, we compare the advantages and limitations of methods in each category. Lastly, we discuss the remaining challenges and future outlook of machine learning in DTI prediction. This article may provide a reference and tutorial insights on machine learning-based DTI prediction for future researchers.

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Prediction of Drug-Target Interaction on Jamu Formulas using Machine Learning Approaches

2019 International Conference on Advanced Computer Science and information Systems (ICACSIS) ◽

10.1109/icacsis47736.2019.8979795 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ahmad Kamal Nasution ◽

Sony Hartono Wijaya ◽

Wisnu Ananta Kusuma

Keyword(s):

Machine Learning ◽

Drug Target ◽

Learning Approaches ◽

Target Interaction

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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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MolTrans: Molecular Interaction Transformer for drug–target interaction prediction

Bioinformatics ◽

10.1093/bioinformatics/btaa880 ◽

2020 ◽

Author(s):

Kexin Huang ◽

Cao Xiao ◽

Lucas M Glass ◽

Jimeng Sun

Keyword(s):

Molecular Interaction ◽

Drug Target ◽

Pattern Mining ◽

Representation Learning ◽

Molecular Data ◽

Supplementary Information ◽

Biomedical Data ◽

Learning Approaches ◽

Real World Data ◽

Target Interaction

Abstract Motivation Drug–target interaction (DTI) prediction is a foundational task for in-silico drug discovery, which is costly and time-consuming due to the need of experimental search over large drug compound space. Recent years have witnessed promising progress for deep learning in DTI predictions. However, the following challenges are still open: (i) existing molecular representation learning approaches ignore the sub-structural nature of DTI, thus produce results that are less accurate and difficult to explain and (ii) existing methods focus on limited labeled data while ignoring the value of massive unlabeled molecular data. Results We propose a Molecular Interaction Transformer (MolTrans) to address these limitations via: (i) knowledge inspired sub-structural pattern mining algorithm and interaction modeling module for more accurate and interpretable DTI prediction and (ii) an augmented transformer encoder to better extract and capture the semantic relations among sub-structures extracted from massive unlabeled biomedical data. We evaluate MolTrans on real-world data and show it improved DTI prediction performance compared to state-of-the-art baselines. Availability and implementation The model scripts are available at https://github.com/kexinhuang12345/moltrans. Supplementary information Supplementary data are available at Bioinformatics online.

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Plant and Animal sub cellular component localization prediction using multiple combination of various machine learning approaches

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.9.9828 ◽

2018 ◽

Vol 7 (1.9) ◽

pp. 221

Author(s):

Bipin Nair B J ◽

Ashik P.v

Keyword(s):

Machine Learning ◽

Subcellular Localization ◽

Drug Target ◽

Protein Profile ◽

Learning Approaches ◽

Cell Organelles ◽

Profile Vector ◽

Highly Correlated ◽

Living Organisms ◽

Localization Prediction

Membrane proteins are encoded in the genome and functionally important in the living organisms. Information on subcellular localization of cellular proteins has a significant role in the function of cell organelles. Discovery of drug target and system biology between localization and biological function are highly correlated. Therefore, we are predicting the localization of protein using various machine learning approaches. The prediction system based on the integration of the outcome of five sequence based sub-classifiers. The subcellular localization prediction of the final result is based on protein profile vector, which is a result of the sub-classifiers.

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A Literature Review of the Detection and Categorization of various Arecanut Diseases using Image Processing and Machine Learning Approaches

International Journal of Applied Engineering and Management Letters ◽

10.47992/ijaeml.2581.7000.0112 ◽

2021 ◽

pp. 183-204

Author(s):

Puneeth B. R. ◽

Nethravathi P. S.

Keyword(s):

Machine Learning ◽

Literature Review ◽

Research Work ◽

Machine Learning Techniques ◽

Research Centre ◽

Learning Approaches ◽

Study Objective ◽

Survey Paper ◽

Multiple Data ◽

New Ideas

Background/Purpose: Every scholarly research project starts with a survey of the literature, which acts as a springboard for new ideas. The purpose of this literature review is to become familiar with the study domain and to assess the work's credibility. It also improves with the subject's integration and summary. This article briefly discusses the detection of disease and classification to achieve the objectives of the study. Objective: The main objective of this literature survey is to explore the different techniques applied to identify and classify the various diseases on arecanut. This paper also recommends the methodology and techniques that can be used to achieve the objectives of the study. Design/Methodology/Approach: Multiple data sources, such as journals, conference proceedings, books, and research papers published in reputable journals, were used to compile the essential literature on the chosen topic and collect information from the arecanuts research centre and many farmers in the south Canara and Udupi districts, before narrowing down the literature that is relevant to the research work. The shortlisted literature was carefully assessed by reading each paper and taking notes as appropriate. The information gathered is then examined to identify the potential gap in the study. Findings/Result: Based on the analysis of the papers reviewed, discussion with farmers and research center officers, it is observed that, not much work is carried out in the field of disease identification and classification on arecanut using machine learning techniques. This survey paper recommends techniques and the methodology that can be applied to identify and classify the diseases in arecanut and to classify them in to healthy and unhealthy. Research limitations/implications: The literature review mentioned in this paper are detection and classification of different diseases in arecanut. Originality/Value: This paper focuses on various online research journals, conference papers, technical books, and web articles. Paper Type: Literature review paper on techniques and methods used to achieve the objectives.

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A comparative chemogenic analysis for predicting Drug-Target Pair via Machine Learning Approaches

Scientific Reports ◽

10.1038/s41598-020-63842-7 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Aman Chandra Kaushik ◽

Aamir Mehmood ◽

Xiaofeng Dai ◽

Dong-Qing Wei

Keyword(s):

Machine Learning ◽

Drug Target ◽

Target Pair ◽

Learning Approaches

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Compound2DeNovoDrugPropMax –a novel programmatic tool incorporating deep learning and in silico methods for automated bio-activity discovery for any compound of interest

10.26434/chemrxiv.13052951.v2 ◽

2021 ◽

Author(s):

Ben Geoffrey A S ◽

Rafal Madaj ◽

Akhil Sanker ◽

Pavan Preetham Valluri ◽

Harshmeet Singh

Keyword(s):

Machine Learning ◽

Deep Learning ◽

In Silico ◽

Drug Target ◽

Learning Algorithms ◽

Network Data ◽

Ligand Interaction ◽

Data Set ◽

Target Interaction ◽

Pubchem Compound

Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction have been shown in the area of social networks through which highly customized suggestions are offered to social<br>network users. Similarly one can attempt the use of machine learning/deep learning algorithms on biological network data to generate predictions of scientific usefulness. In the presented work, compound-drug target interaction network data set from bindingDB has been used to train deep learning neural network and a multi class classification has been implemented to classify PubChem compound queried by the user into class labels of PBD IDs. This way target interaction prediction for PubChem compounds is carried out using deep learning. The user is required to input the PubChem Compound ID (CID) of the compound the user wishes to gain information about its predicted biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target interaction for the input CID. Further the tool also optimizes the compound of interest of the user toward drug likeness properties through a deep learning based structure optimization with a deep learning based<br>drug likeness optimization protocol. The tool also incorporates a feature to perform automated In Silico modelling for the compounds and the predicted drug targets to uncover their protein-ligand interaction profiles. The program is hosted, supported and maintained at the following GitHub repository<div><br></div>https://github.com/bengeof/Compound2DeNovoDrugPropMax<br>

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Identification of Anticancer and Anti-inflammatory Drugs from Drug-target Interaction Descriptors by Machine Learning..

Letters in Drug Design & Discovery ◽

10.2174/1570180819666220114114752 ◽

2022 ◽

Vol 19 ◽

Author(s):

Songtao Huang ◽

Yanrui Ding

Keyword(s):

Machine Learning ◽

Drug Target ◽

Hydrophobic Interactions ◽

Drug Repositioning ◽

Limited Information ◽

Target Interaction ◽

Interaction Sites ◽

Drug Classification ◽

Inflammatory Drugs ◽

Anti Inflammatory

Background: Drug repositioning is an important subject in drug-disease research. In the past, most studies simply used drug descriptors as the feature vector to classify drugs or targets, or used qualitative data about drug-target or drug-disease to predict drug-target interactions. These data provide limited information for drug repositioning. Objective: Considering both drugs and targets and constructing quantitative drug-target interaction descriptors as a method of drug characteristics are of great significance to the study of drug repositioning. Methods: Taking anticancer and anti-inflammatory drugs as research objects, the interaction sites between drugs and targets were determined by molecular docking. Sixty-seven drug-target interaction descriptors were calculated to describe the drug-target interactions, and 22 important descriptors were screened for drug classification by SVM, LightGBM and MLP. Results: The accuracy of SVM, LightGBM and MLP reached 93.29%, 92.68% and 94.51%, their Matthews correlation coefficients reached 0.852, 0.840 and 0.882, and their areas under the ROC curve reached 0.977, 0.969 and 0.968, respectively. Conclusion: Using drug-target interaction descriptors to build machine learning models can obtain better results for drug classification. Number of atom pairs, force field, hydrophobic interactions and bSASA are the four types of key features for the classification of anticancer and anti-inflammatory drugs.

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