QSAR modeling to design selective histone deacetylase 8 (HDAC8) inhibitors

Autor: Keun Woo Lee, Yongseong Kim, Sundarapandian Thangapandian, Yeung-Joon Choi, Raj Kumar, Guang Ping Cao, Minky Son, Yong Jung Kwon, Jung-Keun Suh, Hyong-Ha Kim
Rok vydání: 2016
Předmět:
Zdroj: Archives of Pharmacal Research. 39:1356-1369
ISSN: 1976-3786
0253-6269
DOI: 10.1007/s12272-015-0705-5
Popis: HDAC8 inhibitors have become an attractive treatment for cancer. This study aimed to facilitate the identification of potential chemical scaffolds for the selective inhibition of histone deacetylase 8 (HDAC8) using in silico approaches. Non-linear QSAR classification and regression models of HDAC8 inhibitors were developed with support vector machine. Mean impact value-based sequential forward feature selection and grid search strategy were used for molecular descriptor selection and parameter optimization, respectively. The generated QSAR models were validated by leave-one-out cross validation and an external test set. The best QSAR classification model yielded 84 % of accuracy on the external test prediction and Matthews correlation coefficient is 0.69. The best QSAR regression model showed low root-mean-square error (0.63) and high squared correlation coefficient (0.53) for the test set. The validated QSAR models together with various drug-like properties, molecular docking and molecular dynamics simulation were sequentially used as a multi-step query in chemical database virtual screening. Finally, two hit compounds were discovered as new structural scaffolds which can be used for further in vitro and in vivo activity analyses. The strategy used in this study could be a promising computational strategy which can be utilized for other target drug design.
Databáze: OpenAIRE
Externí odkaz: