Quantitative structural-activity relationship (QSAR) study for fungicidal activities of thiazoline derivatives against rice blast.
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 R(1) and at three sites (ortho, meta, or para) of R(2) aromatic rings in compounds. The results of MLR and NN analyses in the training set of Set-3 showed good correlations (r(2) values of 0.829 and 0.966, respectively) between the descriptors and the fungicidal activities. Five descriptors including the non-overlap steric volume SV(R2C2)), Connolly surface area SA(R1), hydrophobicity Sigma pi(R2), and Hammett substituent constants (sigma(pR1) and sigma(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.
Song
Jin Soo
JS
Bioanalysis and Biotransformation Research Center, Korea Institute of Science and Technology, PO Box 131, Cheongryang, Seoul 130-650, Republic of Korea.
Moon
Taesung
T
Nam
Kee Dal
KD
Lee
Jae Kyun
JK
Hahn
Hoh-Gyu
HG
Choi
Eui-Ju
EJ
Yoon
Chang No
CN
D000935:Antifungal Agents / Q000138:chemical synthesis / Q000494:pharmacology*; D018360:Crystallography, X-Ray; D020082:Magnaporthe / Q000187:drug effects* / Q000472:pathogenicity; D013058:Mass Spectrometry; D008956:Models, Chemical; D015394:Molecular Structure; D016571:Neural Networks, Computer; D012275:Oryza / Q000187:drug effects* / Q000382:microbiology; D010935:Plant Diseases / Q000382:microbiology*; D021281:Quantitative Structure-Activity Relationship
D000935:Antifungal Agents