Application of Inverse QSAR/QSPR Analysis for Pesticides Structures Generation
| Autor: | Faouzi Zaiz, Belgacem Souyei, Abdelkader Hadj Seyd, Abdelkrim Rebiai |
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| Rok vydání: | 2019 |
| Předmět: |
Quantitative structure–activity relationship
Variables Loo 010405 organic chemistry media_common.quotation_subject Inverse Function (mathematics) I-QSPR algorithm 01 natural sciences Atomic Signatures Pesticides 0104 chemical sciences lcsh:Chemistry Set (abstract data type) Range (mathematics) lcsh:QD1-999 Linear regression General Earth and Planetary Sciences Multiple linear regression (MLR) n-Octanol–water partition coefficients Biological system General Environmental Science media_common Mathematics |
| Zdroj: | Acta Chimica Slovenica, Vol 66, Iss 2, Pp 315-325 (2019) |
| ISSN: | 1580-3155 |
| Popis: | The present work has focused on the application of the inverse-QSAR/QSPR problem for generating new structures of pesticides; this is in view of its extremely important and widespread use in several areas, particularly the agricultural field. For this reason, we implemented a methodology containing nine detailed successive steps that include a quantitative structure-activity/property relationship (QSAR/QSPR) study performed to develop a model that relates the structures of 190 pesticides compounds to their n-octanol-water partition coefficients (logkow). We used the unique atomic signatures which represent the structures and acts as independent variables while the property (logkow) as the dependent variable. The model was constructed using 130 molecules as training set, and predictive ability tested using 60 compounds. Modeling of logkow of these compounds as a function of the signatures descriptors was established by multiple linear regression (MLR) using (LOO) cross-validation. As a result, a QSAR/QSPR equation with 14 atomic signatures was hereby obtained with a R2 =0.659273, Q2 =0.65617 and RMSEtraining= 0.930192, s=1.37297 for the training set and in leave-one-out (LOO) cross-validation experiment set value, q2 =0.605676, RMSELOO= 1.0936 respectively. In addition to all of the above, new structures have been generated for a range of pesticides that can be included as future search topics. |
| Databáze: | OpenAIRE |
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