Recently, the world has been witnessing a global pandemic with no effective therapeutics yet, while cancer continues to be a major disease claiming many lives. The natural compound curcumin is bestowed with multiple medicinal applications in addition to demonstrating antiviral and anticancer activities. In order to elucidate the impact of curcumin on COVID-19 and cancer, the current investigation has adapted several computational techniques to unfold its possible inhibitory activity. Accordingly, curcumin and similar compounds and analogues were retrieved and assessed for their binding affinities at the binding pocket of SARS-CoV-2 main protease and DDX3. The best binding pose was escalated to molecular dynamics simulation (MDS) studies to assess the time dependent stability. Our findings have rendered one compound that has demonstrated good molecular dock score complemented by key residue interactions and have shown stable MDS results inferred by root mean square deviation (RMSD), radius of gyration (Rg), binding mode, hydrogen bond interactions, and interaction energy. Essential dynamics results have shown that the systemadapts minimum energy conformation to attain a stable state. The discovered compound (curA) could act as plausible inhibitor against SARS-CoV-2 and DDX3. Furthermore, curA could serve as a chemical scaffold for designing and developing new compounds.

Breast cancer is one of the major causes of mortality in women worldwide. The current treatments available are radiation therapy (RT), surgery, endocrine (hormone) therapy (ET), chemotherapy (CT), and targeted therapy. These treatments are associated with certain side effects that demand the use of natural compounds due to their lower to negligible side effects. One such category of natural compounds is alkaloids. Alkaloids are a group of natural compounds that have gained widespread attention due to their use as potential therapeutics. Alkaloids exert anti-inflammatory and antiviral properties along with antimicrobial activities. In the current review, 12 alkaloids are reviewed in detail for their potential in treating breast cancer. These alkaloids have been shown to induce apoptosis, decrease tumor volume, inhibit cell proliferation and migration, and induce autophagy and they can also be used as a component of combination therapy. This review provides comprehensive information on the in vitro and in vivo therapeutic abilities of alkaloids to counteract breast cancer.

Sinensetin (SIN) has been reported to exhibit anti-inflammatory and anti-cancer activity. However, the cellular and molecular mechanism by which SIN promotes hepatocellular carcinoma (HCC) cell death remains unclear. In the present study, we investigated the induction of cell death by SIN and its underlying mechanism in HepG2 cells, an HCC cell line. We found that SIN significantly induced cell death in HepG2 cells, whereas the proliferation rate of Thle2, human liver epithelial cells, was unaffected by SIN. SIN-treated HepG2 cells were not affected by apoptotic cell death; instead, autophagic cell death was induced through the p53-mediated AMPK/mTOR signaling pathway. Inhibition of p53 degradation led to both autophagy and apoptosis in HepG2 cells. p53 translocation led to SIN-induced autophagy, whereas p53 translocation inhibited SIN-induced apoptosis. However, SIN showed apoptosis in the p53-mutant Hep3B cell line. Molecular docking simulation of the p53 core domain showed effective binding with SIN, which was found significant compared with the known p53 activator, RITA. Collectively, these data suggest that SIN may be a potential anti-cancer agent targeting autophagic cell death in human liver cancer.

Acetylcholinesterase (AChE) catalyzes the hydrolysis of neurotransmitter acetylcholine to acetate and choline in a synaptic cleft. Deficits in cholinergic neurotransmitters are linked closely with the progression of Alzheimer’s disease (AD), which is a neurodegenerative disorder characterized by memory impairment, and a disordered cognitive function. Since the previously approved AChE inhibitors, donepezil (Aricept), galantamine (Reminyl), and rivastigmine (Exelon), have side effects and several studies are being carried out out to develop novel AD drugs, we have applied a three-dimensional quantitative structure−activity relationship (3D QSAR) and structure-based pharmacophore modeling methodologies to identify potential candidate inhibitors against AChE. Herein, 3D QSAR and structure-based pharmacophore models were built from known inhibitors and crystal structures of human AChE in complex with donepezil, galantamine, huperzine A, and huprine W, respectively. The generated models were used as 3D queries to screen new scaffolds from various chemical databases. The hit compounds obtained from the virtual screening were subjected to an assessment of drug-like properties, followed by molecular docking. The final hit compounds were selected based on binding modes and molecular interactions in the active site of the enzyme. Furthermore, molecular dynamics simulations for AChE in complex with the final hits were performed to evaluate that they maintained stable interactions with the active site residues. The binding free energies of the final hits were also calculated using molecular mechanics/Poisson-Boltzmann surface area method. Taken together, we proposed that these hits can be promising candidates for anti-AD drugs.

Diabetes mellitus (DM) is one of the major metabolic disorders that is currently threatening the world. DM is seen associated with obesity and diabetic retinopathy (DR). In the present paper we tried to evaluate the relationship between the three aliments at the gene level and further performed the molecular docking to identify the common drug for all the three diseases. We have adopted several software programs such as Phenopedia, VennViewer, and CDOCKER to accomplish the objective. Our results revealed six genes that commonly associated and are involved in the signalling pathway. Furthermore, evaluation of common gene association from the selected set of genes projected the presence of SIRT1 in all the three aliments. Therefore, we targeted protein 4KXQ which was produced from the gene SIRT1 and challenged it with eight phytochemicals, adopting the CDOCKER. C1 compound has displayed highest -CDOCKER energy and -CDOCKER interaction energy of 43.6905 and 43.3953, respectively. Therefore, this compound is regarded as the most potential lead molecule.

Infectious diseases have posed a global threat recently, progressing from endemic to pandemic. Early detection and finding a better cure are methods for curbing the disease and its transmission. Machine learning (ML) has demonstrated to be an ideal approach for early disease diagnosis. This review highlights the use of ML algorithms for monkeypox (MP). Various models, such as CNN, DL, NLP, Naïve Bayes, GRA-TLA, HMD, ARIMA, SEL, Regression analysis, and Twitter posts were built to extract useful information from the dataset. These findings show that detection, classification, forecasting, and sentiment analysis are primarily analyzed. Furthermore, this review will assist researchers in understanding the latest implementations of ML in MP and further progress in the field to discover potent therapeutics.