基于启发式和基因表达式编程法预测磺胺类药物的pKa值
Quantitative structure activity relationship models based on heuristic method and gene expression programming for the prediction of the pKa values of sulfa drugs
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摘要:
应用启发式算法（HM）和基因表达式编程方法（GEP）建立了31种磺胺类药物pKa值的定量构效关系模型。用ChemOffice2004软件进行化合物的结构输入，利用半经验方法进行分子结构优化，在CODDESA软件中计算出组成、拓扑、几何、电子和量子化学参数，并用启发式方法筛选出4个相关参数，在此基础上运用多元线性回归和基因表达式编程方法建立QSPR模型。两种方法均得到了较好的结果，HM和GEP的的相关系数分别为0.90和0.95。两种QSPR模型在新药研究中可以预测化合物的pKa值，将为新药研究提供理论指导。
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Abstract:Quantitative structure-property relationships (QSPR) were developed to predict the pKa values of sulfa drugs via heuristic method (HM) and gene expression programming (GEP). The descriptors of 31 sulfa drugs were calculated by the software CODESSA, which can calculate constitutional, topological, geometrical, electrostatic, and quantum chemical descriptors. HM was also used for the preselection of 4 appropriate molecular descriptors. Linear and nonlinear QSPR models were developed based on the HM and GEP separately and two prediction models lead to a good correlation coefficient (R) of 0.90 and 0.95. The two QSPR models are useful in predicting pKa during the discovery of new drugs and providing theory information for studying the new drugs.
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