Extending the identification of structural features responsible for anti-SARS-CoV activity of peptide-type compounds using QSAR modelling.
SAR QSAR Environ Res
; 31(9): 643-654, 2020 Sep.
Article
in English
| MEDLINE | ID: covidwho-733459
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Antiviral Agents
/
Peptides
/
Quantitative Structure-Activity Relationship
/
Betacoronavirus
Type of study:
Prognostic study
Language:
English
Journal:
SAR QSAR Environ Res
Journal subject:
Environmental Health
Year:
2020
Document Type:
Article
Affiliation country:
1062936X.2020.1784271
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