Literature DB >> 22587765

Quantitative structure-activity relationship (QSAR) analysis to predict drug-drug interactions of ABC transporter ABCG2.

T Ishikawa1, H Hirano, H Saito, K Sano, Y Ikegami, N Yamaotsu, S Hirono.   

Abstract

Quantitative structure-activity relationship (QSAR) analysis is a practical approach by which chemical structure is quantitatively correlated with biological activity or chemical reactivity. Human ABC transporter ABCG2 exhibits broad substrate specificity toward structurally diverse compounds. To gain insight into the relationship between the molecular structures of compounds and the interaction with ABCG2, we have developed an algorithm that analyzes QSAR to evaluate ABCG2-drug interactions. In addition, to support QSAR analysis, we developed a high-speed screening method for analyzing the drug-drug interactions of ABCG2. Based on both experimental results and computational QSAR analysis data, we propose a hypothetical mechanism underlying ABC-mediated drug transport and its interaction with drugs.

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Year:  2012        PMID: 22587765     DOI: 10.2174/138955712800493825

Source DB:  PubMed          Journal:  Mini Rev Med Chem        ISSN: 1389-5575            Impact factor:   3.862


  6 in total

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Authors:  Qingcheng Mao; Jashvant D Unadkat
Journal:  AAPS J       Date:  2014-09-19       Impact factor: 4.009

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Authors:  Yi-Lung Ding; Yu-Hsuan Shih; Fu-Yuan Tsai; Max K Leong
Journal:  PLoS One       Date:  2014-03-10       Impact factor: 3.240

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4.  ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning.

Authors:  Dejun Jiang; Tailong Lei; Zhe Wang; Chao Shen; Dongsheng Cao; Tingjun Hou
Journal:  J Cheminform       Date:  2020-03-05       Impact factor: 5.514

5.  Molecular Docking and Molecular Dynamics Simulation of Fisetin, Galangin, Hesperetin, Hesperidin, Myricetin, and Naringenin against Polymerase of Dengue Virus.

Authors:  Jaka Fajar Fatriansyah; Raihan Kenji Rizqillah; Muhammad Yusup Yandi
Journal:  J Trop Med       Date:  2022-03-20

6.  Predicting substrates of the human breast cancer resistance protein using a support vector machine method.

Authors:  Eszter Hazai; Istvan Hazai; Isabelle Ragueneau-Majlessi; Sophie P Chung; Zsolt Bikadi; Qingcheng Mao
Journal:  BMC Bioinformatics       Date:  2013-04-15       Impact factor: 3.169

  6 in total

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