Structure-based quantitative structure-activity relationship studies of checkpoint kinase 1 inhibitors

Juan Du, Lili Xi, Beilei Lei, Jing Lu, Jiazhong Li, Huanxiang Liu, Xiaojun Yao

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Structure-based quantitative structure-activity relationship (QSAR) studies on a series of checkpoint kinase 1 (Chk1) inhibitors were performed to find the key structural features responsible for their inhibitory activity. Molecular docking was employed to explore the binding mode of all inhibitors at the active site of Chk1 and determine the active conformation for the QSAR studies. Ligand and structure-based descriptors incorporating the ligand-receptor interaction were generated based on the docked complex. Genetic Algorithm-Multiple Linear Regression (GA-MLR) method was used to build 2D QSAR model. The 2D QSAR model gave a squared correlation coefficient R2 of 0.887, cross-validated Q2 of 0.837 and the prediction squared correlation coefficient R 2pred of 0.849, respectively. Furthermore, three-dimensional quantitative structure-activity relationship (3D QSAR) model using comparative molecular field analysis (CoMFA) with R2 of 0.983, Q2 of 0.550 and R2pred of 0.720 was also developed. The obtained results are helpful for the design of novel Chk1 inhibitors with improved activities.

Original languageEnglish
Pages (from-to)2783-2793
Number of pages11
JournalJournal of Computational Chemistry
Volume31
Issue number15
DOIs
Publication statusPublished - 30 Nov 2010
Externally publishedYes

Keywords

  • checkpoint kinase 1
  • comparative molecular field analysis
  • genetic algorithm-multiple linear regression
  • molecular docking
  • quantitative structure-activity relationship

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