L-Proline: A Versatile Organo-Catalyst in Organic Chemistry.

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 Brønsted acid type catalysts. It catalyzed different asymmetric syntheses, including known 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 essential catalyst for synthesizing 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, Google Scholar , PubMed, etc. Results and Conclusion: Given the importance of L-Proline based reactions, it has been observed to have tremendous applications in organic chemistry. It can also act as a 'Green catalyst'.

QSAR analysis of sodium glucose co-transporter 2 (SGLT2) inhibitors for anti-hyperglycaemic lead development.

QSAR (Quantitative Structure Activity Relationship) modelling was performed on a dataset of 90 sodium-dependent glucose cotransporter 2 (SGLT2) inhibitors. The quantitative and explicative evaluations revealed some of the subtle and distinguished structural features that are responsible for the inhibitory potency of these compounds against SGLT2, such as less possible number of ring carbons at 8 Å from the lipophilic atoms in the molecule (fringClipo8A) and more possible value for the sum of the partial charges of the lipophilic atoms present within seven bonds from the donor atoms (lipo_don_7Bc). Multivariate GA-MLR (genetic algorithm-multi linear regression) and thorough validation methodology out-turned a statistically robust QSAR model with a very high predictability shown from various statistical parameters. A QSAR model with r2 = 0.83, F = 51.54, Q2LOO = 0.79, Q2LMO = 0.79, CCCcv = 0.88, Q2Fn = 0.76-0.81, r2ext = 0.77, CCCext = 0.85, and with RMSEtr < RMSEcv was proposed. This QSAR model will assist synthetic chemists in the development of the SGLT2 inhibitors as the antidiabetic leads.

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

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.