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QSAR models for isoindolinone-based p53-MDM2 interaction inhibitors using linear and non-linear statistical methods.
Chem Biol Drug Des. 2012 May; 79(5):691-702.CB

Abstract

The design and optimization of p53-MDM2 interaction inhibitors has attracted a great deal of interest in the development of new anticancer agents. Systematical 2D-QSAR studies on 98 isoindolinone-based p53-MDM2 interaction inhibitors were carried out using linear and the non-linear mathematical methods. At first, a forward stepwise-multiple linear regression model (FS-MLR) was proposed with reasonable statistical parameters (R(2)(train) =0.881, Q(2)(loo) =0.847, R(2)(test) =0.854). Then, enhanced replacement method-multiple linear regression (ERM-MLR) and support vector machine regression (SVMR) were applied to set up more accurate models (ERM-MLR: R(2)(train) =0.914, Q(2)(loo) =0.894 and R(2)(test) =0.903; SVMR: R(2)(train) =0.924, Q(2)(loo) =0.920 and R(test) (2) of 0.874). Furthermore, the reliability and application value of the ERM and SVMR model was also validated in virtual screening through receiver operating characteristic studies.

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Authors+Show Affiliations

Dong X
ZJU-ENS Joint Laboratory of Medicinal Chemistry, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
Yan J
No affiliation info available
Lu D
No affiliation info available
Wu P
No affiliation info available
Gao J
No affiliation info available
Liu T
No affiliation info available
Yang B
No affiliation info available
Hu Y
No affiliation info available

MeSH

Antineoplastic AgentsDrug DesignIsoindolesLinear ModelsModels, StatisticalNeoplasmsProtein Interaction MappingProto-Oncogene Proteins c-mdm2Quantitative Structure-Activity RelationshipTumor Suppressor Protein p53

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

22233482

Citation

Dong, Xiaowu, et al. "QSAR Models for Isoindolinone-based p53-MDM2 Interaction Inhibitors Using Linear and Non-linear Statistical Methods." Chemical Biology & Drug Design, vol. 79, no. 5, 2012, pp. 691-702.
Dong X, Yan J, Lu D, et al. QSAR models for isoindolinone-based p53-MDM2 interaction inhibitors using linear and non-linear statistical methods. Chem Biol Drug Des. 2012;79(5):691-702.
Dong, X., Yan, J., Lu, D., Wu, P., Gao, J., Liu, T., Yang, B., & Hu, Y. (2012). QSAR models for isoindolinone-based p53-MDM2 interaction inhibitors using linear and non-linear statistical methods. Chemical Biology & Drug Design, 79(5), 691-702. https://doi.org/10.1111/j.1747-0285.2012.01322.x
Dong X, et al. QSAR Models for Isoindolinone-based p53-MDM2 Interaction Inhibitors Using Linear and Non-linear Statistical Methods. Chem Biol Drug Des. 2012;79(5):691-702. PubMed PMID: 22233482.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - QSAR models for isoindolinone-based p53-MDM2 interaction inhibitors using linear and non-linear statistical methods. AU - Dong,Xiaowu, AU - Yan,Jingying, AU - Lu,Dong, AU - Wu,Peng, AU - Gao,Jiadi, AU - Liu,Tao, AU - Yang,Bo, AU - Hu,Yongzhou, Y1 - 2012/02/09/ PY - 2012/1/12/entrez PY - 2012/1/12/pubmed PY - 2012/7/24/medline SP - 691 EP - 702 JF - Chemical biology & drug design JO - Chem Biol Drug Des VL - 79 IS - 5 N2 - The design and optimization of p53-MDM2 interaction inhibitors has attracted a great deal of interest in the development of new anticancer agents. Systematical 2D-QSAR studies on 98 isoindolinone-based p53-MDM2 interaction inhibitors were carried out using linear and the non-linear mathematical methods. At first, a forward stepwise-multiple linear regression model (FS-MLR) was proposed with reasonable statistical parameters (R(2)(train) =0.881, Q(2)(loo) =0.847, R(2)(test) =0.854). Then, enhanced replacement method-multiple linear regression (ERM-MLR) and support vector machine regression (SVMR) were applied to set up more accurate models (ERM-MLR: R(2)(train) =0.914, Q(2)(loo) =0.894 and R(2)(test) =0.903; SVMR: R(2)(train) =0.924, Q(2)(loo) =0.920 and R(test) (2) of 0.874). Furthermore, the reliability and application value of the ERM and SVMR model was also validated in virtual screening through receiver operating characteristic studies. SN - 1747-0285 UR - https://www.unboundmedicine.com/medline/citation/22233482/QSAR_models_for_isoindolinone_based_p53_MDM2_interaction_inhibitors_using_linear_and_non_linear_statistical_methods_ L2 - https://doi.org/10.1111/j.1747-0285.2012.01322.x DB - PRIME DP - Unbound Medicine ER -