Literature DB >> 27334201

Molecular Docking for Identification of Potential Targets for Drug Repurposing.

Heng Luo, William Mattes, Donna L Mendrick, Huixiao Hong1.   

Abstract

Using existing drugs for new indications (drug repurposing) is an effective method not only to reduce drug development time and costs but also to develop treatments for new disease including those that are rare. In order to discover novel indications, potential target identification is a necessary step. One widely used method to identify potential targets is through molecule docking. It requires no prior information except structure inputs from both the drug and the target, and can identify potential targets for a given drug, or identify potential drugs for a specific target. Though molecular docking is popular for drug development and repurposing, challenges remain for the method. In order to improve the prediction accuracy, optimizing the target conformation, considering the solvents and adding cobinders to the system are possible solutions.

Mesh:

Year:  2016        PMID: 27334201     DOI: 10.2174/1568026616666160530181149

Source DB:  PubMed          Journal:  Curr Top Med Chem        ISSN: 1568-0266            Impact factor:   3.295


  7 in total

1.  Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy.

Authors:  Jiansong Fang; Zengrui Wu; Chuipu Cai; Qi Wang; Yun Tang; Feixiong Cheng
Journal:  J Chem Inf Model       Date:  2017-10-13       Impact factor: 4.956

Review 2.  New Insights Into Drug Repurposing for COVID-19 Using Deep Learning.

Authors:  Chun Yen Lee; Yi-Ping Phoebe Chen
Journal:  IEEE Trans Neural Netw Learn Syst       Date:  2021-10-27       Impact factor: 10.451

3.  Machine-Learning Prediction of Oral Drug-Induced Liver Injury (DILI) via Multiple Features and Endpoints.

Authors:  Xiaobin Liu; Danhua Zheng; Yi Zhong; Zhaofan Xia; Heng Luo; Zuquan Weng
Journal:  Biomed Res Int       Date:  2020-05-19       Impact factor: 3.411

4.  Trader as a new optimization algorithm predicts drug-target interactions efficiently.

Authors:  Yosef Masoudi-Sobhanzadeh; Yadollah Omidi; Massoud Amanlou; Ali Masoudi-Nejad
Journal:  Sci Rep       Date:  2019-06-27       Impact factor: 4.379

5.  DDIT: An Online Predictor for Multiple Clinical Phenotypic Drug-Disease Associations.

Authors:  Lu Lu; Jiale Qin; Jiandong Chen; Hao Wu; Qiang Zhao; Satoru Miyano; Yaozhong Zhang; Hua Yu; Chen Li
Journal:  Front Pharmacol       Date:  2022-01-19       Impact factor: 5.810

6.  An integrated strategy for identifying new targets and inferring the mechanism of action: taking rhein as an example.

Authors:  Hao Sun; Yiting Shen; Guangwen Luo; Yuepiao Cai; Zheng Xiang
Journal:  BMC Bioinformatics       Date:  2018-09-06       Impact factor: 3.169

7.  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

  7 in total

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