398 kcal/mol), 1XU7 (- 117.341 kcal/mol), 3K35 (- 114.267 kcal/mol). Erythrin was found to fare better than the three clinically used antidiabetic compounds, metformin, repaglinide and sitagliptin. Further, the molecular interactions between erythrin and the diabetes related target proteins was established by analysing the interactions with associated amino acids. In silico pharmacokinetics and toxicity profile of erythrin using admetSAR software predicted erythrin as non-carcinogenic and non-mutagenic. The drug-likeliness was calculated using molsoft software respecting Lipinski's rule of five. The compound was found to comply with Lipinksi rules violating only one filter criterion. The study suggested that erythrin could be a potential anti-diabetic agent.Glaucoma, the most perilous disease leading to blindness is a result of optical neuropathy. Accumulation of aqueous humor in the posterior chamber due to a large difference in the rate of formation and its drainage in the anterior chamber causes an increase in intraocular pressure (IOP) leading to damage of nerve cells. A literature survey has revealed that inhibition of the Rho guanosine triphosphatases (rho GTPase) pathway by specific inhibitors leads to the relaxation of contractile cells involved in the aqueous outflow pathway. Relaxation of the strained contractile cells results in increased outflow thereby releasing IOP. In the present study molecular docking has been used to screen twenty seven bioactive (17 natural compounds and 10 conventional drugs) compounds that may play a significant role in relaxing contractile cells by inhibiting rho-GTPase protein. Docking results showed that among all-natural bioactive compounds Cyanidin and Delphinidine have a good binding affinity (- 8.4 kcal/mol) than the omising lead compound as a future drug for Glaucoma.Traditionally, Tinospora cordifolia is commonly used in the treatment of diabetes and obesity; has been evaluated for their anti-diabetic and anti-obese potency in experimental animal models. However, the binding affinity of multiple bioactives with various proteins involved in the pathogenesis of diabetes and obesity has not been reported yet. Hence, the present study aimed to assess the binding affinity of multiple bioactives from T. cordifolia with various targets involved in the pathogenesis of diabetes and obesity. The ligands and targets were retrieved from the PubChem and Protein Data Bank respectively and docked using autodock4.0. Druglikeness and absorption, distribution, metabolism, excretion, and toxicity profile were predicted using Molsoft and admetSAR1 respectively. The multiple bioactives from T. cordifolia were identified to interact with multiple proteins involved in the pathogenesis of diabetes/obesity, i.e., isocolumbin (- 9 kcal/mol) with adiponectin (PDB 4DOU), β-sitosterol (- 10.9 kcal/mst diabetes and obesity via in-vitro and in-vivo approaches.For any antimicrobial assay, a standard drug is used to compare the bactericidal efficiency of the bioactive compound under screening. The standard drugs have different targets that may be intracellular or membrane located. The location of the target is believed to be determining the bioactivity of the drug depending on the drug's access to its target. Therefore, different drugs must have a different magnitude in exhibiting the biological effect. However, in most of the published literature about the screening of bioactive compounds on antimicrobial activity, generally, the standard drug is randomly chosen while comparing against the bioactive compound of interest. Further, the antimicrobial activity is inferred by comparing the randomly chosen standard drugs without knowing the physicochemical parameters of the standard drug and the test molecule. It is just like an unfair comparison of the impact of a bullet with the impact of an explosive in a combat scene. Computer-based strategies for structure-based drug discovery presents a valuable alternative to the costly and time-consuming process of random screening. The docking studies provide better insights into the binding mechanism of substrate and inhibitor at the molecular level. The evaluation of such a comparison of bioactive compounds against randomly selected standard drugs through a customized virtual screening pipeline showed 57% false positives, 18% true positive, 17% true negative, 8% false-negative results. read more This study directs for mandatory cheminformatics-based assessment of the bioactive compounds before choosing the standard drug to compare with. The online version contains supplementary material available at 10.1007/s40203-020-00064-9.The online version contains supplementary material available at 10.1007/s40203-020-00064-9.Data visualizations are a valuable tool used during both statistical analysis and the interpretation of results as they graphically reveal useful information about the structure, properties and relationships between variables, which may otherwise be concealed in tabulated data. In disciplines like medicine and the social sciences, where collected data include sensitive information about study participants, the sharing and publication of individual-level records is controlled by data protection laws and ethico-legal norms. Thus, as data visualizations - such as graphs and plots - may be linked to other released information and used to identify study participants and their personal attributes, their creation is often prohibited by the terms of data use. These restrictions are enforced to reduce the risk of breaching data subject confidentiality, however they limit analysts from displaying useful descriptive plots for their research features and findings. Here we propose the use of anonymization techniques to generate privacy-preserving visualizations that retain the statistical properties of the underlying data while still adhering to strict data disclosure rules. We demonstrate the use of (i) the well-known k-anonymization process which preserves privacy by reducing the granularity of the data using suppression and generalization, (ii) a novel deterministic approach that replaces individual-level observations with the centroids of each k nearest neighbours, and (iii) a probabilistic procedure that perturbs individual attributes with the addition of random stochastic noise. We apply the proposed methods to generate privacy-preserving data visualizations for exploratory data analysis and inferential regression plot diagnostics, and we discuss their strengths and limitations.