Computational search for drug repurposing to identify potential inhibitors against SARS-COV-2 using Molecular Docking, QTAIM and IQA methods in viral Spike protein - Human ACE2 interface

Drug Repositioning: A Monetary Stratagem to Discover a New Application of Drugs

Drug repurposing, also referred to as drug repositioning or drug reprofiling, is a scientific approach to the detection of any new application for an already approved or investigational drug. It is a useful policy for the invention and development of new pharmacological or therapeutic applications of different drugs. The strategy has been known to offer numerous advantages over developing a completely novel drug for certain problems. Drug repurposing has numerous methodologies that can be categorized as target-oriented, drug-oriented, and problem-oriented. The choice of the methodology of drug repurposing relies on the accessible information about the drug molecule and like pharmacokinetic, pharmacological, physicochemical, and toxicological profile of the drug. In addition, molecular docking studies and other computer-aided methods have been known to show application in drug repurposing. The variation in dosage for original target diseases and novel diseases presents a challenge for researchers of drug repurposing in present times. The present review critically discusses the drugs repurposed for cancer, covid-19, Alzheimer's, and other diseases, strategies, and challenges of drug repurposing. Moreover, regulatory perspectives related to different countries like the United States (US), Europe, and India have been delineated in the present review.

Fluoride-bridged dinuclear dysprosium complex showing single-molecule magnetic behavior: supramolecular approach to isolate magnetic molecules

Using Na-encapsulated benzo[18]crown-6 (Na)(B18C6) as a counter cation, we successfully magnetically isolated a fluoride-bridging Dy dinuclear complex {[(PW11O39)Dy(H2O)2]2F} (Dy2POM) with lacunary Keggin ligands. (Na)(B18C6) formed two types of tetramers through C-H⋯O, π⋯π and C-H⋯π interactions, and each tetramer aligned in one dimension along the c-axis to form two types of channels. One channel was partially penetrated by a supramolecular cation from the ±a-axis direction, dividing the channel in the form of a "bamboo node". Dy2POM was spatially divided by this "bamboo node," which magnetically isolated one portion from the other. The temperature dependence of the magnetic susceptibility indicated a weak ferromagnetic interaction between the Dy ions bridged by fluoride. Dy2POM exhibited the magnetic relaxation characteristics of a single-molecule magnet, including the dependence of AC magnetic susceptibility on temperature and frequency. Magnetic relaxation can be described by the combination of thermally active Orbach and temperature-independent quantum tunneling processes. The application of a static magnetic field effectively suppressed the relaxation due to quantum tunneling.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

A survey on computational methods in discovering protein inhibitors of SARS-CoV-2

The outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [ 1– 5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to discover potential inhibitors of COVID-19 virus, so-called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). First, we briefly categorize and describe computational approaches by the basic algorithms involved in. Then we review the related biological datasets used in such predictions. Furthermore, we emphatically discuss current knowledge on SARS-CoV-2 inhibitors with the latest findings and development of computational methods in uncovering protein inhibitors against COVID-19.