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首页> 外文期刊>Cogent Chemistry >Chemometric study of some α, β-unsaturated ketone as potential antifungal agents using density function theory and GFA (ATCC 10231 and NCIM 3446 cell line)
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Chemometric study of some α, β-unsaturated ketone as potential antifungal agents using density function theory and GFA (ATCC 10231 and NCIM 3446 cell line)

机译：使用密度泛函理论和GFA（ATCC 10231和NCIM 3446细胞系）对某些α，β-不饱和酮作为潜在的抗真菌剂进行化学计量学研究

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Quantitative structure–activity relationship (QSAR) is based on the hypothesis that changes in molecular structure reflect changes in the observed response or biological activity. The success of any QSAR model depends on the accuracy of the input data, selection of appropriate descriptors, statistical tools, and the validation of the developed model. A suitable set of molecular descriptors were calculated to represent the molecular structures of compounds such as constitutional, topological, geometrical, electrostatic, and quantum chemical descriptors. The important descriptors were selected with the aid of the genetic function approximation technique. The obtained model was validated using R cv 2 ?=?0.700, LOF?=?0.187, R ~(2)?=?0.8085, R adj 2 ?=?0.7625, F ?=?17.586, RMSE?=?0.1781 and SDEP?=?0.098, R pred 2 ?=?0.7956, L _(5o)?=?0.7235. Results showed that the predictive ability of the model was satisfactory and it can be used for designing similar group of antifungal compounds.
机译：定量构效关系（QSAR）基于分子结构变化反映观察到的反应或生物学活性变化的假设。任何QSAR模型的成功都取决于输入数据的准确性，适当描述符的选择，统计工具以及所开发模型的验证。计算了一组合适的分子描述符，以表示化合物的分子结构，例如组成，拓扑，几何，静电和量子化学描述符。借助遗传函数近似技术选择了重要的描述符。使用R cv 2≤0.700，LOF≤0.187，R〜（2）≤0.8085，R adj 2≤0.7625，F≤17.586，来验证所获得的模型。 RMSE≤0.1781，SDEP≤0.098，R pred 2≤0.7956，L _（5o）≤0.7235。结果表明，该模型的预测能力令人满意，可用于设计相似的抗真菌化合物组。

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