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

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 _sp ²_03_F descriptor, showed better predictive ability. Validation parameters(Q ² and R ²) for the models were 0.722 and 0.925, respectively. In addition, external validation for the models using a test group revealed $R_{\rm{pred}}^2=0.892$. 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 Raltegravir(RAL), well drug-likeness and low toxicity. The research provides a base for further DKA development.

Keywords

Diketo acid(DKA) / Modified quantitative structure-activity relationship(QSAR) / Autodock / Drug design / Descriptor screening

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