Due to its large scale applications in the real field, the study of bi-metallic nano-alloy clusters is an active field of research. Though a number of experimental reports are available in this domain, a deep theoretical insight is yet to receive. Among several nano-clusters, the compound formed between Cu-Ag has gained a large importance due to its remarkable optical property. Density Functional Theory (DFT) is one of the most popular approaches of quantum mechanics to study the electronic properties of materials. Conceptually, DFT based descriptors have turned to be indispensable tools for analyzing and correlating the experimental properties of compounds. In this venture, we have analyzed the experimental properties of the (Cu-Ag) n = 1 − 7 nano-alloy clusters invoking DFT methodology. A nice correlation has been found between optical properties of the aforesaid nano-clusters with our evaluated theoretical descriptors. The similar agreement between experimental bond length and computed data is also reflected in this analysis. Beside these, the effect of even-odd alternation behavior of nano compounds on the HOMO-LUMO gap is very important in our computation. It is probably the first attempt to establish such type of correlation.

QSAR models are widely and successfully used in many research areas. The success of such models highly depends on molecular descriptors typically classified as 1D, 2D, 3D, or 4D. While 3D information is likely important, e. g., for modeling ligand-protein binding, previous comparisons between the performances of 2D and 3D descriptors were inconclusive. Yet in such comparisons the modeled ligands were not necessarily represented by their bioactive conformations. With this in mind, we mined the PDB for sets of protein-ligand complexes sharing the same protein for which uniform activity data were reported. The results, totaling 461 structures spread across six series were compiled into a carefully curated, first of its kind dataset in which each ligand is represented by its bioactive conformation. Next, each set was characterized by 2D, 3D and 2D + 3D descriptors and modeled using three machine learning algorithms, namely, k-Nearest Neighbors, Random Forest and Lasso Regression. Models' performances were evaluated on external test sets derived from the parent datasets either randomly or in a rational manner. We found that many more significant models were obtained when combining 2D and 3D descriptors. We attribute these improvements to the ability of 2D and 3D descriptors to code for different, yet complementary molecular properties.

Recently Maria S. Gualdesi et al., have reported about bioactive lamivudine and its carbonate derivatives with proved activity against human immunodeficiency disease (anti-HIV) and hepatitis B viruses (anti-HBV) respectively in simulated gastric and intestinal fluids samples. This is one of the simplest, sensitive, accurate and precise assays for determining all the compounds which are readily adaptable for routine use in clinical laboratories. Employing simple chromatographic technique, they have lucidly explained the kinetic and stability features of lamivudine and its carbonate derivatives. But a theoretical quantum mechanical study of aforesaid compounds is yet to explore. In this report, the authors have made a correlation between experimental properties of lamivudine derivatives with their theoretical counterparts. They have invoked DFT based global and local descriptors to explore the effect of substituents on activity and mechanistic pathway of instant compounds respectively. Their computed data reveals a hand to hand relationship between experimental and theoretical parameters. Finally, a QSAR model has been proposed in terms of DFT based global descriptors.

Pollution of Sea by various harmful hydrocarbons due to accidental leakages of oils during ship operations making it one of serious environmental issues all over the world. Biosurfactants act as one of the promising candidates for overcoming such pollutions. They are surface active molecules synthesized by microorganisms. They replace their chemically synthesized counterparts due to their eco-friendly nature on environment. In present study, we isolated some of the bacterial strains showing biosurfactant activity and further we characterized particular bacterial strain showing highest activity isolated from oil-spilled area of Arabian Sea. Various biosurfactant activity assay tests were performed for isolating the potent bacterial strain. Marine biosurfactants produced by some marine microorganisms have been paid more attention, particularly for the bioremediation of the sea polluted by crude oil. Among all of the isolated strains, strain 2 identified as Bacillus sp. showed the highest biosurfactant activity. The isolated culture filtrate was found to be highly effective in microbial enhanced oil recovery (MEOR). Keywords

No abstract