Exploring quantitative structure-property relationship models for environmental fate assessment of petroleum hydrocarbons.

The rate and extent of biodegradation of petroleum hydrocarbons in the different aquatic environments is an important element to address. The major avenue for removing petroleum hydrocarbons from the environment is thought to be biodegradation. The present study involves the development of predictive quantitative structure-property relationship (QSPR) models for the primary biodegradation half-life of petroleum hydrocarbons that may be used to forecast the biodegradation half-life of untested petroleum hydrocarbons within the established models' applicability domain. These models use easily computable two-dimensional (2D) descriptors to investigate important structural characteristics needed for the biodegradation of petroleum hydrocarbons in freshwater (dataset 1), temperate seawater (dataset 2), and arctic seawater (dataset 3). All the developed models follow OECD guidelines. We have used double cross-validation, best subset selection, and partial least squares tools for model development. In addition, the small dataset modeler tool has been successfully used for the dataset with very few compounds (dataset 3 with 17 compounds), where dataset division was not possible. The resultant models are robust, predictive, and mechanistically interpretable based on both internal and external validation metrics (R2 range of 0.605-0.959. Q2(Loo) range of 0.509-0.904, and Q2F1 range of 0.526-0.959). The intelligent consensus predictor tool has been used for the improvement of the prediction quality for test set compounds which provided superior outcomes to those from individual partial least squares models based on several metrics (Q2F1 = 0.808 and Q2F2 = 0.805 for dataset 1 in freshwater). Molecular size and hydrophilic factor for freshwater, frequency of two carbon atoms at topological distance 4 for temperate seawater, and electronegative atom count relative to size for arctic seawater were found to be the most significant descriptors responsible for the regulation of biodegradation half-life of petroleum hydrocarbons.

Advanced Computational Methodologies Used in the Discovery of New Natural Anticancer Compounds.

Natural chemical compounds have been widely investigated for their programmed necrosis causing characteristics. One of the conventional methods for screening such compounds is the use of concentrated plant extracts without isolation of active moieties for understanding pharmacological activity. For the last two decades, modern medicine has relied mainly on the isolation and purification of one or two complicated active and isomeric compounds. The idea of multi-target drugs has advanced rapidly and impressively from an innovative model when first proposed in the early 2000s to one of the popular trends for drug development in 2021. Alternatively, fragment-based drug discovery is also explored in identifying target-based drug discovery for potent natural anticancer agents which is based on well-defined fragments opposite to use of naturally occurring mixtures. This review summarizes the current key advancements in natural anticancer compounds; computer-assisted/fragment-based structural elucidation and a multi-target approach for the exploration of natural compounds.

Identification of potential Mpro inhibitors for the treatment of COVID-19 by using systematic virtual screening approach.

The Corona virus Disease (COVID-19) is caused because of novel coronavirus (SARS-CoV-2) pathogen detected in China for the first time, and from there it spread across the globe creating a worldwide pandemic of severe respiratory complications. The virus requires structural and non-structural proteins for its multiplication that are produced from polyproteins obtained by translation of its genomic RNA. These polyproteins are converted into structural and non-structural proteins mainly by the main protease (Mpro). A systematic screening of a drug library (having drugs and diagnostic agents which are approved by FDA or other world authorities) and the Asinex BioDesign library was carried out using pharmacophore and sequential conformational precision level filters using the Schrodinger Suite. From the screening of approved drug library, three antiviral agents ritonavir, nelfinavir and saquinavir were predicted to be the most potent Mpro inhibitors. Apart from these pralmorelin, iodixanol and iotrolan were also identified from the systematic screening. As iodixanol and iotrolan carry some limitations, structural modifications in them could lead to stable and safer antiviral agents. Screenings of Asinex BioDesign library resulted in 20 molecules exhibiting promising interactions with the target protein Mpro. They can broadly be categorized into four classes based on the nature of the scaffold, viz. disubstituted pyrazoles, cyclic amides, pyrrolidine-based compounds and miscellaneous derivatives. These could be used as potential molecules or hits for further drug development to obtain clinically useful therapeutic agents for the treatment of COVID-19.

Identification of some novel pyrazolo[1,5-a]pyrimidine derivatives as InhA inhibitors through pharmacophore-based virtual screening and molecular docking.

The InhA inhibitors play key role in mycolic acid synthesis by preventing the fatty acid biosynthesis pathway. In this present article, Pharmacophore modelling and molecular docking study followed by in silico virtual screening could be considered as effective strategy to identify newer enoyl-ACP reductase inhibitors. Pyrrolidine carboxamide derivatives were opted to generate pharmacophore models using HypoGen algorithm in Discovery studio 2.1. Further it was employed to screen Zinc and Minimaybridge databases to identify and design newer potent hit molecules. The retrieved newer hits were further evaluated for their drug likeliness and docked against enoyl acyl carrier protein reductase. Here, novel pyrazolo[1,5-a]pyrimidine analogues were designed and synthesized with good yields. Structural elucidation of synthesized final molecules was perform through IR, MASS, 1H-NMR, 13C-NMR spectroscopy and further tested for its in vitro anti-tubercular activity against H37Rv strain using Microplate Alamar blue assay (MABA) method. Most of the synthesized compounds displayed strong anti-tubercular activities. Further, these potent compounds were gauged for MDR-TB, XDR-TB and cytotoxic study.

Thiazole: A Review on Chemistry, Synthesis and Therapeutic Importance of its Derivatives.

Thiazole, a unique heterocycle containing sulphur and nitrogen atoms, occupies an important place in medicinal chemistry. It is an essential core scaffold present in many natural (Vitamin B1- Thiamine) and synthetic medicinally important compounds. The versatility of thiazole nucleus demonstrated by the fact that it is an essential part of penicillin nucleus and some of its derivatives which have shown antimicrobial (sulfazole), antiretroviral (ritonavir), antifungal (abafungin), antihistaminic and antithyroid activities. The synthetic importance of thiazole derivatives, its reduced forms and condensed derivatives have been increased much by their recent applications as anticancer (tiazofurin), anthelmintic, vulcanising accelerators (mercaptobenzothiazole) and photographic sensitizers. Thiazole chemistry has developed steadily after the pioneering work of Hofmann and Hantsch. Bogert and co-workers made significant contribution to expand this field. Mills established the importance of thiazole ring in cyanine dyes which is used as photographic sensitizer. Benzothiazole, a fused derivative of thiazole have also proved its commercial value. Present review describes chemical and biological importance of thiazole and its condensed derivatives with an emphasis on recent developments.

Discovery of novel acyl coenzyme a: cholesterol acyltransferase inhibitors: pharmacophore-based virtual screening, synthesis and pharmacology.

The present study describes ligand-based pharmacophore modeling of a series of structurally diverse acyl coenzyme A cholesterol acyltransferase inhibitors. Quantitative pharmacophore models were generated using HypoGen module of Discovery Studio 2.1, whereby the best pharmacophore model possessing two hydrophobic, one ring aromatic, and one hydrogen bond acceptor feature for inhibition of acyl coenzyme A cholesterol acyltransferase showed a very good correlation coefficient (r = 0.942) along with satisfactory cost analysis. Hypo1 was also validated by test set and cross-validation methods. Developed models were found to be predictive as indicated by low error values for test set molecules. Virtual screening against Maybridge database using Hypo1 was performed. The two most potent compounds (47 and 48; predicted IC50 = 1 nM) of the retrieved hits were synthesized and biologically evaluated. These compounds showed 86% and 88% inhibition of acyl coenzyme A cholesterol acyltransferase (at 10 µg/mL) with IC50 value of 3.6 and 2.5 nM, respectively. As evident from the close proximity of biological data to the predicted values, it can be concluded that the generated model (Hypo1) is a reliable and useful tool for lead optimization of novel acyl coenzyme A cholesterol acyltransferase inhibitors.