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Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure−Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA–MLR (Genetic Algorithm–Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole–indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

N-myristoyltransferase (NMT) is an important eukaryotic monomeric enzyme which has emerged as an attractive target for developing a drug for cancer, leishmaniasis, ischemia-reperfusion injury, malaria, inflammation, etc. In the present work, statistically robust machine leaning models (QSAR (Quantitative Structure–Activity Relationship) approach) for Human NMT (Hs-NMT) inhibitory has been performed for a dataset of 309 Nitrogen heterocycles screened for NMT inhibitory activity. Hundreds of QSAR models were derived. Of these, the model 1 and 2 were chosen as they not only fulfil the recommended values for a good number of validation parameters (e.g., R2 = 0.77–0.79, Q2LMO = 0.75–0.76, CCCex = 0.86–0.87, Q2-F3 = 0.74–0.76, etc.) but also provide useful insights into the structural features that sway the Hs-NMT inhibitory activity of Nitrogen heterocycles. That is, they have an acceptable equipoise of descriptive and predictive qualities as per Organisation for Economic Co-operation and Development (OECD) guidelines. The developed QSAR models identified a good number of molecular descriptors like solvent accessible surface area of all atoms having specific partial charge, absolute surface area of Carbon atoms, etc. as important features to be considered in future optimizations. In addition, pharmacophore modeling has been performed to get additional insight into the pharmacophoric features, which provided additional results.

In the present work, QSTR modeling was conducted for microalga Pseudokirchneriella subcapitata using a data set of 271 molecules belonging to different types of chemical classes for the prediction of EC50 for 72 hr based assays. The balanced QSTR model encompasses seven easily interpretable molecular descriptors and possesses statistical robustness with high predictive ability. This Genetic Algorithm Multi-linear regression (GA-MLR) model was subjected to internal validation, Y-randomization test, applicability domain analysis, and external validation as per the recommended OECD guidelines. The newly developed model fulfilled the threshold values for more than 20 recommended validation parameters including R2 = 0.72, Q2LOO = 0.70, etc. The developed QSTR model was successful in identifying the type of hybridization or specific type of atoms of previously reported and newer structural alerts. Thus, the model could be useful for data gap filling and expanding mechanistic interpretation of toxicity for different chemicals.

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, Q2LMO ​= ​0.66–0.67). The developed QSAR models identified number of sp2 hybridized Oxygen atoms within seven bonds from aromatic Carbon atoms, the presence of Carbon and Nitrogen atoms at a topological distance of 3 and other interrelations of atom pairs as important pharmacophoric features. Hence, the present QSAR models have a good balance of Qualitative (Descriptive QSARs) and Quantitative (Predictive QSARs) approaches, therefore useful for future modifications of peptide-type compounds for anti- SARS-CoV activity., Highlights • QSAR models have been built using easily interpretable descriptors. • QSAR models have excellent external predictive ability. • Identification salient and hidden pharmacophoric features. • First ever attempt to develop QSAR models for anti-SARS-Cov activity of peptide-type compounds.

Total synthesis of two thiopeptide antibiotics, micrococcin P1 and thiocillin I, is described featuring new molybdenum catalyst., Thiopeptides are a class of potent antibiotics with promising therapeutic potential. We developed a novel Mo(vi)-oxide/picolinic acid catalyst for the cyclodehydration of cysteine peptides to form thiazoline heterocycles. With this powerful tool in hand, we completed the total syntheses of two representative thiopeptide antibiotics: micrococcin P1 and thiocillin I. These two concise syntheses (15 steps, longest linear sequence) feature a C–H activation strategy to install the trisubstituted pyridine core and thiazole groups. The synthetic material displays promising antimicrobial properties measured against a series of Gram-positive bacteria.

Aetheramides A and B are very potent anti-HIV agents. An enantioselective synthesis of a MEM-protected aetheramide A derivative is described. The synthesis was accomplished in a convergent and stereoselective manner. The key reactions involved asymmetric dihydroxylation, asymmetric allylation, asymmetric syn-aldol reactions, and asymmetric hydrogenation.

The steady growth of the pharmaceutical industry in the past halfcentury, coupled with the more recent and rapid growth in thegeneric business, has been of great benefit to human health. Todaythere are 4,500 drugs in development, including experimental ones,which comprise about 70% of the total. Nearly 44% of Americansare taking at least one prescription drug.1,2 The annual value of USsales was estimated at $330 billion for 2010, showing an annualgrowth rate over many decades of nearly 8.5%. From 1950 to 2010,the total resident population of the U.S. increased from 151 millionto 305 million people, representing an average annual growth rateof 1.2%.3-5 This expansion in population and the accompanyinggrowth of pharmaceutical production to meet customers’ needscame with a significant environmental cost. Unfortunately severalindustries are relying on outdated methods to produce active phar-maceutical ingredients (APIs), making it the most wasteful chemicalproducer as measured by the E-factor, a relation of waste producedversus desired material obtained.6-9 Yet times are changing, as theexamples in this chapter demonstrate. In their drive to harnesswaste, many pharmaceutical producers have adopted the 12 greenchemistry principles and redesigned their chemical processes toimportant medicines. Eyeballing innovative ways to reformulatetheir currentmanufacturing process has led them to trim down theirimpact on the environment. The present chapter confers about theevolution in production from initial strategy to greener approachesfor several significant pharmaceutical APIs (Fig. 2.1).