Prediction of the Octanol–Water Partition Coefficients of N-Aryl-Substituted Anthranilic Acid Derivatives

Andryukov, K. V.; Korkodinova, L. M.

doi:10.1007/s11094-014-1027-5

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…В работе [1] исследовались модели для выявления перспективных лекарственных веществ в ряду производных N-арилзамещенных антраниловых кислот. Основным фактором в этих моделях являлись константы липофильности.…”

Section: Introductionunclassified

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Prediction of Antifungal Activity of 1,2,4-Triazole Derivatives Using Qsar Models

Osipov

2023

SJLSA

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Purpose. Development of QSAR models and investigation of their effectiveness for predicting the antifungal activity of 1,2,4-triazole derivatives against Aspergillus flavus, Aspergillus fumigatus and Trichophyton mentagrophytes. Materials and methods. Experimental data on the antifungal activity of 1,2,4-triazole derivatives against Aspergillus flavus, Aspergillus fumigatus and Trichophyton mentagrophytes were used for scientific research. The data were analyzed using QSAR models based on molecular descriptors automatically generated from structural formulas of 1,2,4-triazole derivatives. Results. New QSAR models for predicting antifungal activity against Aspergillus flavus, Aspergillus fumigatus and Trichophyton mentagrophytes based on six physico-chemical parameters of chemicals (eeig11r, eeig09x, r6m+, belm2, eeig12r) are presented. During the comparative analysis of the developed QSAR models, a model was identified that has the best statistical parameters and does not have multicollinearity: MAE=0.136; MAPE=12.55; forecast accuracy=87.45%; MSE=0.028; RMSE=0.167. Among the six factors, the most significant ones were identified. Conclusion. As a result of the conducted studies, QSAR models for predicting the antifungal activity of 1,2,4-triazole derivatives against Aspergillus flavus, Aspergillus fumigatus and Trichophyton mentagrophytes were identified and analyzed. From one to six molecular descriptors were taken as factors in the models. The factors that make the greatest contribution to the prediction of antifungal activity were evaluated. The best models are selected based on the calculated statistical parameters.

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Section: Introductionunclassified

“…В работах [3][4] разработаны QSAR модели молекулярного дизайна для производных антраниловой кислоты, учитывающих противомикробную, анальгетическую и противовоспалительную активность.…”

Section: Introductionunclassified

Prediction of Antifungal Activity of 1,2,4-Triazole Derivatives Using Qsar Models

Osipov

2023

SJLSA

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Molecular-Docking Study of the Interaction of Anti-Inflammatory N-Aroyl-Substituted Halo(H)Anthranilic Acid Amides and Hydrazides with Cyclooxygenase 1

Andryukov

Korkodinova

2018

Pharm Chem J

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Structure—Activity (Analgesic) Relationship of a Series of N-Substituted 3,5-Dibromo(H)Anthranilic Acid Amides and Hydrazides Studied Using an Experimental Model in Mice and Molecular Docking of Cyclooxygenases 1 and 2

Andryukov

Korkodinova²

2022

Pharm Chem J

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Prediction of the Octanol–Water Partition Coefficients of N-Aryl-Substituted Anthranilic Acid Derivatives

Cited by 3 publications

References 2 publications

Prediction of Antifungal Activity of 1,2,4-Triazole Derivatives Using Qsar Models

Prediction of Antifungal Activity of 1,2,4-Triazole Derivatives Using Qsar Models

Molecular-Docking Study of the Interaction of Anti-Inflammatory N-Aroyl-Substituted Halo(H)Anthranilic Acid Amides and Hydrazides with Cyclooxygenase 1

Structure—Activity (Analgesic) Relationship of a Series of N-Substituted 3,5-Dibromo(H)Anthranilic Acid Amides and Hydrazides Studied Using an Experimental Model in Mice and Molecular Docking of Cyclooxygenases 1 and 2

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