Abstract
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 (SVR2 C2), Connolly surface area (SAR1), hydrophobicity (∑ πR2), and Hammett substituent constants (σp R1 and σm R2) 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.
Original language | English |
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Pages (from-to) | 2133-2142 |
Number of pages | 10 |
Journal | Bioorganic and Medicinal Chemistry Letters |
Volume | 18 |
Issue number | 6 |
DOIs | |
Publication status | Published - 2008 Mar 15 |
Bibliographical note
Funding Information:This work was supported by National Research Laboratory program of the Ministry of Science & Technology, Korea.
Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
Keywords
- Magnaporthe grisea
- Multiple linear regression
- Neural networks
- QSAR
- Thiazoline derivatives
ASJC Scopus subject areas
- Biochemistry
- Molecular Medicine
- Molecular Biology
- Pharmaceutical Science
- Drug Discovery
- Clinical Biochemistry
- Organic Chemistry