Lee, Jin Kak Song, Jin Soo Nam, Kee Dal Hahn, Hoh-Gyu Yoon, Chang No 2024-01-20T17:34:38Z 2024-01-20T17:34:38Z 2021-09-02 2011-02 0048-3575 https://pubs.kist.re.kr/handle/201004/130702 For the development of highly active fungicides against Magnaporthe grisea, we studied the substituent effects at three sites (ortho, meta, or para) of 121 and at various sites of le aromatic rings in thiazoline derivatives at a 10 ppm concentration for fungicidal activities against this target. Quantitative structural-activity relationships (QSAR) analysis to study the relationship between the substituent effects and the activities of the compounds was carried out by using multiple linear regression (MLR) and neural networks (NN). The results of the MLR and the NN showed good correlations (r(2) values of 0.849 and 0.884, respectively) between the selected descriptors and the activities in the training set. The descriptors, including a Hammett constant (sigma(1)(PR)), Connolly surface area (SA(R)(2)), and the substituent volume (SVR1 (C2,3)), play an important role for the activities of the compounds. Although the descriptors of an optimum MLR model were used in NN, the results were little improved by the NN, implying that the descriptors used in the MLR model included good linear relationships. (C) 2011 Published by Elsevier Inc. English ACADEMIC PRESS INC ELSEVIER SCIENCE NEURAL-NETWORKS QSAR PARAMETERS MODELS Substituent effects of thiazoline derivatives for fungicidal activities against Magnaporthe grisea Article 10.1016/j.pestbp.2010.10.004 1 PESTICIDE BIOCHEMISTRY AND PHYSIOLOGY, v.99, no.2, pp.125 - 130 PESTICIDE BIOCHEMISTRY AND PHYSIOLOGY 99 2 125 130 scie scopus 000286961500001 2-s2.0-78751705043 Biochemistry & Molecular Biology Entomology Physiology Biochemistry & Molecular Biology Entomology Physiology Article NEURAL-NETWORKS QSAR PARAMETERS MODELS Magnaporthe grisea QSAR Multiple linear regression Neural network