Extending the identification of structural features responsible for anti-SARS-CoV activity of peptide-type compounds using QSAR modelling.

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Extending the identification of structural features responsible for anti-SARS-CoV activity of peptide-type compounds using QSAR modelling.

SAR QSAR Environ Res. 2020 Aug 27;:1-12

Authors: Masand VH, Rastija V, Patil MK, Gandhi A, Chapolikar A

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. r 2 = 0.87, Q 2 loo = 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.

PMID: 32847369 [PubMed - as supplied by publisher]

28/08/2020
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