IN SILICO STUDY FOR INVESTIGATING AND PREDICTING THE ACTIVITIES OF 1,2,4-TRIAZOLE DERIVATIES AS POTENT ANTI-TUBERCULAR AGENTS

• Autor(a) principal: ADENIJI, SHOLA ELIJAH
• Data de Publicação: 2018
• Outros Autores: Uba, Sani, Uzairu, Adamu
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: The Journal of Engineering and Exact Sciences
• DOI: 10.18540/jcecvl4iss2pp0246-0254
• Texto Completo: https://periodicos.ufv.br/jcec/article/view/2483
• topic: Tuberculosis; 1; 2; 4-Triazole; QSAR; Applicability domain; Y-Randomization.

AbstractIn silico study was carried on a dataset of 1,2,4-Triazole derivatives to investigate their activities behaviour on mycobacterium tuberculosis by utilizing Quantitative Structure-Activity Relationship (QSAR) technique. Genetic Function Algorithm (GFA) and Multiple Linear Regression Analysis (MLRA) were used to select the optimum descriptors and to generate the correlation QSAR model that relate their activities values against mycobacterium tuberculosis with the molecular structures of the inhibitors. The model was validated and was found to have squared correlation coefficient (R2) of 0.9134, adjusted squared correlation coefficient (Radj) of 0.8753 and Leave one out (LOO) cross validation coefficient (Qcv^2) value of 0.8231. The external validation set used for confirming the predictive power of the model has R2pred of 0.7482. Stability and robustness of the model obtained by the validation test indicate that the model can be used to design and synthesis other 1,2,4-Triazole derivatives with improved anti-mycobacterium tuberculosis activities