Literature DB >> 27829350

Drug-Target Interactions: Prediction Methods and Applications.

Shanmugam Anusuya1, Manish Kesherwani2, K Vishnu Priya1, Antonydhason Vimala1, Gnanendra Shanmugam3, Devadasan Velmurugan2,4, M Michael Gromiha1.   

Abstract

Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing. The availability of heterogeneous biological data on known drug-target interactions enabled many researchers to develop various computational methods to decipher unknown drug-target interactions. This review provides an overview on these computational methods for predicting drug-target interactions along with available webservers and databases for drug-target interactions. Further, the applicability of drug-target interactions in various diseases for identifying lead compounds has been outlined. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Keywords:  Drug-target interaction; drug design; drug repurposing; feature based method; machine learning; polypharmacology; semi-supervised method; similarity based method; supervised method.

Mesh:

Year:  2018        PMID: 27829350     DOI: 10.2174/1389203718666161108091609

Source DB:  PubMed          Journal:  Curr Protein Pept Sci        ISSN: 1389-2037            Impact factor:   3.272


  5 in total

1.  Molecular docking of DS-3032B, a mouse double minute 2 enzyme antagonist with potential for oncology treatment development.

Authors:  Vítor Hugo Sales da Mota; Fabrício Freire de Melo; Breno Bittencourt de Brito; Filipe Antônio França da Silva; Kádima Nayara Teixeira
Journal:  World J Clin Oncol       Date:  2022-06-24

2.  Using BERT to identify drug-target interactions from whole PubMed.

Authors:  Jehad Aldahdooh; Markus Vähä-Koskela; Jing Tang; Ziaurrehman Tanoli
Journal:  BMC Bioinformatics       Date:  2022-06-21       Impact factor: 3.307

3.  DTiGEMS+: drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques.

Authors:  Maha A Thafar; Rawan S Olayan; Haitham Ashoor; Somayah Albaradei; Vladimir B Bajic; Xin Gao; Takashi Gojobori; Magbubah Essack
Journal:  J Cheminform       Date:  2020-06-29       Impact factor: 5.514

Review 4.  Fluorescent Biosensors for Neurotransmission and Neuromodulation: Engineering and Applications.

Authors:  Anna V Leopold; Daria M Shcherbakova; Vladislav V Verkhusha
Journal:  Front Cell Neurosci       Date:  2019-10-23       Impact factor: 5.505

5.  A Novel Deep Neural Network Technique for Drug-Target Interaction.

Authors:  Jackson G de Souza; Marcelo A C Fernandes; Raquel de Melo Barbosa
Journal:  Pharmaceutics       Date:  2022-03-11       Impact factor: 6.321

  5 in total

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