In silico investigation and potential therapeutic approaches of natural products for COVID-19: Computer-aided drug design perspective.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a substantial number of deaths around the world, making it a serious and pressing public health hazard. Phytochemicals could thus provide a rich source of potent and safer anti-SARS-CoV-2 drugs. The absence of approved treatments or vaccinations continues to be an issue, forcing the creation of new medicines. Computer-aided drug design has helped to speed up the drug research and development process by decreasing costs and time. Natural compounds like terpenoids, alkaloids, polyphenols, and flavonoid derivatives have a perfect impact against viral replication and facilitate future studies in novel drug discovery. This would be more effective if collaboration took place between governments, researchers, clinicians, and traditional medicine practitioners' safe and effective therapeutic research. Through a computational approach, this study aims to contribute to the development of effective treatment methods by examining the mechanisms relating to the binding and subsequent inhibition of SARS-CoV-2 ribonucleic acid (RNA)-dependent RNA polymerase (RdRp). The in silico method has also been employed to determine the most effective drug among the mentioned compound and their aquatic, nonaquatic, and pharmacokinetics' data have been analyzed. The highest binding energy has been reported -11.4 kcal/mol against SARS-CoV-2 main protease (7MBG) in L05. Besides, all the ligands are non-carcinogenic, excluding L04, and have good water solubility and no AMES toxicity. The discovery of preclinical drug candidate molecules and the structural elucidation of pharmacological therapeutic targets have expedited both structure-based and ligand-based drug design. This review article will assist physicians and researchers in realizing the enormous potential of computer-aided drug design in the design and discovery of therapeutic molecules, and hence in the treatment of deadly diseases.

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance.

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.

Recent advancements of nanoparticles application in cancer and neurodegenerative disorders: At a glance.

Nanoscale engineering is one of the innovative approaches to heal multitudes of ailments, such as varieties of malignancies, neurological problems, and infectious illnesses. Therapeutics for neurodegenerative diseases (NDs) may be modified in aspect because of their ability to stimulate physiological response while limiting negative consequences by interfacing and activating possible targets. Nanomaterials have been extensively studied and employed for cancerous therapeutic strategies since nanomaterials potentially play a significant role in medical transportation. When compared to conventional drug delivery, nanocarriers drug delivery offers various benefits, such as excellent reliability, bioactivity, improved penetration and retention impact, as well as precise targeting and administering. Upregulation of drug efflux transporters, dysfunctional apoptotic mechanisms, and a hypoxic atmosphere are all elements that lead to cancer treatment sensitivity in humans. It has been possible to target these pathways using nanoparticles and increase the effectiveness of multidrug resistance treatments. As innovative strategies of tumor chemoresistance are uncovered, nanomaterials are being developed to target specific pathways of tumor resilience. Scientists have recently begun investigating the function of nanoparticles in immunotherapy, a field that is becoming increasingly useful in the care of malignancies. Nanoscale therapeutics have been explored in this scientific literature and represent the most current approaches to neurodegenerative illnesses and cancer therapy. In addition, current findings and various biomedical nanomaterials' future promise for tissue regeneration, prospective medication design, and the synthesis of novel delivery approaches have been emphasized.