A Strategy to Find Novel Candidate DKAs Inhibitors Using Modified QSAR Model with Favorable Druggability Properties

doi:10.1007/s40242-019-9183-5

Abstract

Abstract: The study dealed with quantitative structure-activity relationship(QSAR) to explore the important features of diketo acid(DKA) derivatives for exerting potent HIV-1 integrase inhibitors activity. A three-step screening method was proposed to choose descriptors. Then, additional descriptors were used in the CoMFA and CoMSIA. Lastly, a modified CoMSIA m7 model, constructed by adding C_sp²_03_F descriptor, showed better predictive ability. Validation parameters(Q² and R²) for the models were 0.722 and 0.925, respectively. In addition, external validation for the models using a test group revealed R_pred²=0.892. Contour maps analysis defined favored and disfavored regions of the compounds, and two new compounds with the descriptor structure were designed with better activities than Ralte-gravir(RAL), well drug-likeness and low toxicity. The research provides a base for further DKA development.

Key words: Diketo acid (DKA), Modified quantitative structure-activity relationship (QSAR), Autodock, Drug design, Descriptor screening

ZHANG Xiaoyi, NIU Wenling, TANG Tang, HOU Chengfei, GUO Yajie, KONG Ren. A Strategy to Find Novel Candidate DKAs Inhibitors Using Modified QSAR Model with Favorable Druggability Properties[J]. Chemical Research in Chinese Universities, 2019, 35(6): 1111-1118.

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