Literature DB >> 20031966

DCDB: drug combination database.

Yanbin Liu1, Bin Hu, Chengxin Fu, Xin Chen.   

Abstract

SUMMARY: Rapid advances in pharmaceutical sciences have brought ever-increasing interests in combined therapies for better clinical efficacy and safety, especially in cases of complicated and refractory diseases. Innovative experimental technologies and theoretical frameworks are being actively developed for multicomponent drug research. In this work, we present the Drug Combination Database, with aims to facilitate analyses of known drug combinations, to summarize patterns of beneficial drug interactions, and to provide a basis for theoretical modeling and simulation of such drug interactions. Its current version (1.0) collected 499 approved or investigational drug combinations, including 40 unsuccessful drug combinations, involving 485 individual drugs, from >6000 references.

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Year:  2009        PMID: 20031966     DOI: 10.1093/bioinformatics/btp697

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  40 in total

1.  Large-scale elucidation of drug response pathways in humans.

Authors:  Yael Silberberg; Assaf Gottlieb; Martin Kupiec; Eytan Ruppin; Roded Sharan
Journal:  J Comput Biol       Date:  2012-02       Impact factor: 1.479

Review 2.  A survey of current trends in computational drug repositioning.

Authors:  Jiao Li; Si Zheng; Bin Chen; Atul J Butte; S Joshua Swamidass; Zhiyong Lu
Journal:  Brief Bioinform       Date:  2015-03-31       Impact factor: 11.622

3.  Computational Drug Repositioning Using Continuous Self-Controlled Case Series.

Authors:  Zhaobin Kuang; James Thomson; Michael Caldwell; Peggy Peissig; Ron Stewart; David Page
Journal:  KDD       Date:  2016-08

Review 4.  Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.

Authors:  Peter Csermely; Tamás Korcsmáros; Huba J M Kiss; Gábor London; Ruth Nussinov
Journal:  Pharmacol Ther       Date:  2013-02-04       Impact factor: 12.310

5.  Combinatorial therapy discovery using mixed integer linear programming.

Authors:  Kaifang Pang; Ying-Wooi Wan; William T Choi; Lawrence A Donehower; Jingchun Sun; Dhruv Pant; Zhandong Liu
Journal:  Bioinformatics       Date:  2014-01-24       Impact factor: 6.937

Review 6.  Data Centric Molecular Analysis and Evaluation of Hepatocellular Carcinoma Therapeutics Using Machine Intelligence-Based Tools.

Authors:  Rengul Cetin-Atalay; Deniz Cansen Kahraman; Esra Nalbat; Ahmet Sureyya Rifaioglu; Ahmet Atakan; Ataberk Donmez; Heval Atas; M Volkan Atalay; Aybar C Acar; Tunca Doğan
Journal:  J Gastrointest Cancer       Date:  2021-12-15

Review 7.  Machine learning approaches for drug combination therapies.

Authors:  Betül Güvenç Paltun; Samuel Kaski; Hiroshi Mamitsuka
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 11.622

8.  Machine learning methods, databases and tools for drug combination prediction.

Authors:  Lianlian Wu; Yuqi Wen; Dongjin Leng; Qinglong Zhang; Chong Dai; Zhongming Wang; Ziqi Liu; Bowei Yan; Yixin Zhang; Jing Wang; Song He; Xiaochen Bo
Journal:  Brief Bioinform       Date:  2022-01-17       Impact factor: 11.622

9.  INDI: a computational framework for inferring drug interactions and their associated recommendations.

Authors:  Assaf Gottlieb; Gideon Y Stein; Yoram Oron; Eytan Ruppin; Roded Sharan
Journal:  Mol Syst Biol       Date:  2012-07-17       Impact factor: 11.429

10.  Prediction of effective drug combinations by chemical interaction, protein interaction and target enrichment of KEGG pathways.

Authors:  Lei Chen; Bi-Qing Li; Ming-Yue Zheng; Jian Zhang; Kai-Yan Feng; Yu-Dong Cai
Journal:  Biomed Res Int       Date:  2013-09-05       Impact factor: 3.411

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