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...
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| Published in | Briefings in bioinformatics Vol. 22; no. 1; pp. 247 - 269 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
18.01.2021
Oxford Publishing Limited (England) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1467-5463 1477-4054 1477-4054 |
| DOI | 10.1093/bib/bbz157 |
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| Abstract | 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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| AbstractList | 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. 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.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. 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. |
| Author | Bagherian, Maryam Najarian, Kayvan Sabeti, Elyas Sartor, Maureen A Wang, Kai Nikolovska-Coleska, Zaneta |
| AuthorAffiliation | 4 Department of Pathology , University of Michigan, Ann Arbor, MI, 48109, USA 2 Michigan Institute for Data Science , University of Michigan, Ann Arbor, MI, 48109, USA 6 Department of Electrical Engineering and Computer Science , College of Engineering, University of Michigan, Ann Arbor, MI, 48109, USA 1 Department of Computational Medicine and Bioinformatics , University of Michigan, Ann Arbor, MI, 48109, USA 5 Department of Emergency Medicine , Medical School, University of Michigan, Ann Arbor, MI, 48109, USA 3 Department of Biostatistics , School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA |
| AuthorAffiliation_xml | – name: 2 Michigan Institute for Data Science , University of Michigan, Ann Arbor, MI, 48109, USA – name: 1 Department of Computational Medicine and Bioinformatics , University of Michigan, Ann Arbor, MI, 48109, USA – name: 4 Department of Pathology , University of Michigan, Ann Arbor, MI, 48109, USA – name: 5 Department of Emergency Medicine , Medical School, University of Michigan, Ann Arbor, MI, 48109, USA – name: 6 Department of Electrical Engineering and Computer Science , College of Engineering, University of Michigan, Ann Arbor, MI, 48109, USA – name: 3 Department of Biostatistics , School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA |
| Author_xml | – sequence: 1 givenname: Maryam surname: Bagherian fullname: Bagherian, Maryam email: bmaryam@umich.edu organization: Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA – sequence: 2 givenname: Elyas surname: Sabeti fullname: Sabeti, Elyas organization: Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, 48109, USA – sequence: 3 givenname: Kai surname: Wang fullname: Wang, Kai organization: Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA – sequence: 4 givenname: Maureen A surname: Sartor fullname: Sartor, Maureen A organization: Department of Pathology, University of Michigan, Ann Arbor, MI, 48109, USA – sequence: 5 givenname: Zaneta surname: Nikolovska-Coleska fullname: Nikolovska-Coleska, Zaneta organization: Department of Emergency Medicine, Medical School, University of Michigan, Ann Arbor, MI, 48109, USA – sequence: 6 givenname: Kayvan surname: Najarian fullname: Najarian, Kayvan organization: Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor, MI, 48109, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31950972$$D View this record in MEDLINE/PubMed |
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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... 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... |
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| SubjectTerms | Computational Biology - methods Databases, Factual Drug Discovery - methods Humans Learning algorithms Machine Learning Predictions Review Therapeutic targets |
| Title | Machine learning approaches and databases for prediction of drug–target interaction: a survey paper |
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