QSAR Study of Triazolopyridine Derivatives as PIM Inhibitors Using the Genetic Algorithm-Multiple Linear Regressions

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

POURBASHEER ESLAM; Mohajeri Avval Zhila; NEKOEI MEHDI; Hamidvand Somayeh

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Nashrieh Shimi va Mohandesi Shimi Iran Year:2018 | Volume:37 | Issue:2 Page(s): 137-148

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

516

Download:

Cites:

Information Journal Paper

Title

QSAR Study of Triazolopyridine Derivatives as PIM Inhibitors Using the Genetic Algorithm-Multiple Linear Regressions

Author(s)

POURBASHEER ESLAM | Mohajeri Avval Zhila | NEKOEI MEHDI | Hamidvand Somayeh | Issue Writer Certificate

Keywords

QSARQ1

Genetic Algorithms (GA)Q2

Multiple Linear Regressions (MLR)Q1

PIM inhibitorsQ1

Triazolopyridine derivativesQ1

Abstract

Quantitative Structure-Activity Relationship (QSAR) was developed for modeling and predicting of the PIM inhibitory activities a data set containing 39 structures of Triazolopyridine derivatives with known biological activities. Segmentation the whole dataset into a training set and test set was performed randomly. StepWise (SW) and Genetic Algorithm (GA) techniques with Multiple Linear Regression (MLR) were used to select the most important descriptors and to create the best prediction model. Comparison of the results obtained for SW-MLR and GA-MLR models was showed that GA-MLR model is superior to the SW-MLR model. The robustness and the predictive ability of the final GA-MLR model validated by internal and external statistical validations including Leave-One-Out (LOO) cross-validation, Leave-Group-Out (LGO) cross-validation, Y-randomization and external test set. High agreement between experimental and predicted activity values indicated that GA-MLR model with five variables has good quality and it could be used in design novel compounds with higher PIM inhibitor activity.

Cites

No record.

References

No record.

Cite

APA: Copy

POURBASHEER, ESLAM, Mohajeri Avval, Zhila, NEKOEI, MEHDI, & Hamidvand, Somayeh. (2018). QSAR Study of Triazolopyridine Derivatives as PIM Inhibitors Using the Genetic Algorithm-Multiple Linear Regressions. NASHRIEH SHIMI VA MOHANDESI SHIMI IRAN (PERSIAN), 37(2 ), 137-148. SID. https://sid.ir/paper/26424/en

Vancouver: Copy

POURBASHEER ESLAM, Mohajeri Avval Zhila, NEKOEI MEHDI, Hamidvand Somayeh. QSAR Study of Triazolopyridine Derivatives as PIM Inhibitors Using the Genetic Algorithm-Multiple Linear Regressions. NASHRIEH SHIMI VA MOHANDESI SHIMI IRAN (PERSIAN)[Internet]. 2018;37(2 ):137-148. Available from: https://sid.ir/paper/26424/en

IEEE: Copy

ESLAM POURBASHEER, Zhila Mohajeri Avval, MEHDI NEKOEI, and Somayeh Hamidvand, “QSAR Study of Triazolopyridine Derivatives as PIM Inhibitors Using the Genetic Algorithm-Multiple Linear Regressions,” NASHRIEH SHIMI VA MOHANDESI SHIMI IRAN (PERSIAN), vol. 37, no. 2 , pp. 137–148, 2018, [Online]. Available: https://sid.ir/paper/26424/en

Related Journal Papers