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A classification study of human β 3-adrenergic receptor agonists using BCUT descriptors

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Abstract

Experimental EC50s for 202 human β 3-AR agonists are used to develop classification models as a potential screening tool for a large library of target compounds before synthesis. A variable selection approach from random forests (VS-RF) is used to extract the structural information most relevant to the human β 3-AR activation properties of the collected data set. The obtained results indicate that the VS-RF method can be used for variable selection with smallest sets of non-redundant descriptors with highly predictive accuracy (Q ex% = 96% for the external prediction set). Thus, the proposed VS-RF models should be helpful for screening of potential human β 3-AR agonists before chemical synthesis in drug development.

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Authors and Affiliations

  1. Department of Materials Science and Chemical Engineering, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Ming Hao, Yan Li & Shuwei Zhang

  2. Center of Bioinformatics, Northwest A&F University, Yangling, 71210, Shaanxi, China

    Yonghua Wang

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  1. Ming Hao
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  2. Yan Li
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  3. Yonghua Wang
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  4. Shuwei Zhang
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Correspondence to Yan Li.

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Hao, M., Li, Y., Wang, Y. et al. A classification study of human β 3-adrenergic receptor agonists using BCUT descriptors. Mol Divers 15, 877–887 (2011). https://doi.org/10.1007/s11030-011-9321-6

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