Quantitative structural–activity relationship (QSAR) study for fungicidal activities of thiazoline derivatives against rice blast

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• 作者：Jin Soo Song ; Taesung Moon ; Kee Dal Nam ; Jae Kyun Lee ; Hoh-Gyu Hahn ; Eui-Ju Choi ; Chang No Yoon
• 关键词：Magnaporthe grisea ; Thiazoline derivatives ; QSAR ; Multiple linear regression ; Neural networks
• 刊名：Bioorganic and Medicinal Chemistry Letters
• 出版年：2008
• 出版时间：15 March 2008
• 年：2008
• 卷：18
• 期：6
• 页码：2133-2142
• 全文大小：537 K

文摘

For the development of new fungicides against rice blast, the quantitative structural–activity relationship (QSAR) analyses for fungicidal activities of thiazoline derivatives were carried out using multiple linear regression (MLR) and neural network (NN). We have studied the substituent effects at para site of R¹ and at three sites (ortho, meta, or para) of R² aromatic rings in compounds. The results of MLR and NN analyses in the training set of Set-3 showed good correlations (r² values of 0.829 and 0.966, respectively) between the descriptors and the fungicidal activities. Five descriptors including the non-overlap steric volume bacaa460b10f80b4476e2c23205195a"" title=""Click to view the MathML source"" alt=""Click to view the MathML source"">(SV_R²C2), Connolly surface area (SA_R¹), hydrophobicity (∑π_R²), and Hammett substituent constants (σ_pR¹ and σ_mR²) were selected as important factors of fungicidal activities. Although the descriptors of optimum MLR model were used in NN, the results were improved by NN. This means that the descriptors used in MLR model include non-linear relationships.