Using Grey System GM(1,1) Model to Predict the Drug-GPCRs Couples


Article Preview

It is an important topic to find and identify molecular drug targets for modern drug research. Naturally, to find new or potential drug-GPCRs couples is very useful for determining the new effective medicine target, because GPCRs are among the most frequent targets of therapeutic drugs and proven evolutionary pharmacology value. To realize this, this paper incorporates chemical functional groups of drugs and grey model GM(1,1) based on digital coding of amino acids to formulate the drug-GPCRs couples for statistical prediction. The overall success rate using the fuzzy K-Nearest Neighbor algorithm by the jackknife test is about 81%. This novel approach will further stimulate the development of scanning target via a computational approach.



Edited by:

Mohamed Othman




X. Xiao et al., "Using Grey System GM(1,1) Model to Predict the Drug-GPCRs Couples", Applied Mechanics and Materials, Vols. 229-231, pp. 2634-2637, 2012

Online since:

November 2012




[1] AL Hopkins and CR Groom: The druggable genome, Nature Reviews Drug Discovery (2002).

[2] Filmore, David: It's a GPCR world, Modern Drug Discovery, American Chemical Society (2004).

[3] John P. Overington, Bissan Al-Lazikani & Andrew L. Hopkins: How many drug targets are there, Nature Reviews Drug Discovery (2006).


[4] Yi Lin, Sifeng Liu: A historical introduction to grey systems theory, Systems, Man and Cybernetics (2004).


[5] Patani GA, LaVoie EJ: Bioisosterism: A Rational Approach in Drug Design, Chemical Reviews (1996).

[6] Paul Finn, Stephen Muggleton, David Page & Ashwin Srinivasan: Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL, Machine Learning (1998).


[7] Zhisong He, Jian Zhang, Xiao-He Shi, Le-Le Hu, Xiangyin Kong, Yu-Dong Cai & Kuo-Chen Chou: Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features, PLoS ONE (2010).


[8] Xuan Xiao, Pu Wang & Kuo-Chen Chou: Predicting protein structural classes with pseudo amino acid composition: An approach using geometric moments of cellular automaton image, Elsevier Ltd (2008).


[9] Yamanishi Y, Araki M, Gutteridge A, Honda W & Kanehisa M: Prediction of drug-target interaction networks from the integration of chemical and genomic spaces, Bioinformatics (2008).


[10] Keller JM, Gray MR, Givens JAJ: A frezzy K-nearest neighbor algorithm. IEEE transactions on systems, man, and cybernetics 15: 580–585 (1985).