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.

In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R2 = 0.80-0.82, Q2loo = 0.74-0.77). The developed models identified interrelations of atom pairs as important molecular descriptors. Therefore, the present QSAR models have a good balance of Qualitative and Quantitative approaches, thereby, useful for future modifications of peptide-type compounds for anti-SARS-CoV activity. File list (3) download file view on ChemRxiv COvid-19 Manuscript.docx (669.91 KiB) download file view on ChemRxiv Supplementary material-Ki-Manuscript.docx (376.96 KiB) download file view on ChemRxiv Descriptors for submission-Ki.csv (6.95 KiB) Structure features of peptide-type SARS-CoV main protease inhibitors: Quantitative Structure Activity Relationship study

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Background: L-proline is a natural amino acid having secondary amine functionality and acts as a bifunctional catalyst (organo-catalyst). The amino-functional group acts as Lewis base type while carboxylic acids act as Bronsted acid type catalysts. It catalyzed different asymmetric syntheses including some known name reactions such as Aldol condensation, Mannich reaction, Michael Addition, Knoevenagel condensation, Hantzsch synthesis, OXA-Michael Henry tandem, Ullmann reactions, Wieland-Miescher ketone synthesis, Robinson annulation, Biginelli reaction, α-amination. It is also an important catalyst for the synthesis of different heterocyclic skeletons such as coumarin, spiro-oxindoles, imidazoles, benzimidazoles, quinoxalines, podophyllotoxin, benzothiazoles, isoxazolidines, phenothiazines, aziridine, indole, 1,5-benzodiazepines, pyridine, and quinazolines. Objective: In this review, we had the objective to critically summarize the use of proline and proline derivatives as catalysts of multicomponent reactions performed in various media and leading to synthetically and biologically relevant heterocycles, a very important class of compounds that constitutes over 60% of drugs and agrochemicals. Methods: All scholarly articles for L-Proline catalyzed reactions were retrieved from ScienceDirect, GoogleScholar, PubMed, etc. Results and Conclusion: Given the importance of L-Proline based reactions, it has been observed that it has tremendous applications in organic chemistry. It can also act as a 'Green catalyst'.

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