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ABSTRACT
A quantitative structure–activity relationship (QSAR) model was built from a dataset of 54 peptide-type compounds as SARS-CoV inhibitors. The analysis was executed to identify prominent and hidden structural features that govern anti-SARS-CoV activity. The QSAR model was derived from the genetic algorithm–multi-linear regression (GA-MLR) methodology. This resulted in the generation of a statistically robust and highly predictive model. In addition, it satisfied the OECD principles for QSAR validation. The model was validated thoroughly and fulfilled the threshold values of a battery of statistical parameters (e.g. r2 = 0.87, Q2loo = 0.82). The derived model is successful in identifying many atom-pairs as important structural features that govern the anti-SARS-CoV activity of peptide-type compounds. The newly developed model has a good balance of descriptive and statistical approaches. Consequently, the present work is useful for future modifications of peptide-type compounds for SARS-CoV and SARS-CoV-2 activity.
Acknowledgements
The authors are thankful to Dr. Paola Gramatica and her team for providing QSARINS-2.2.2 and developers of TINKER, ChemSketch 12 Freeware (ACD labs), and PaDEL for providing the free versions of their software.
Disclosure statement
No potential conflict of interest is reported by the authors.
Supplementary material
Supplemental data for this article can be accessed at https://doi.org/10.1080/1062936X.2020.1784271