ABSTRACT
Background: Global outburst of coronavirus has challenged the whole world to discover drugs to combat the current pandemic. Repurposing drugs is a promising approach as it provides new openings to challenge the emerging COVID-19. However, in the epoch of big data, artificial intelligence (AI) technology offers to leverage computational methods for finding new candidate drugs through an In-silico approach. Aim and Objectives: The aim and objectives of our present work basically are the designing of a plant-derived compound against the COVID-19 receptors which might act as effective therapy along with predicting the outcome of the disease with a deep learning program language that is python (anaconda) 2.7 version. Methodology: Artificial Intelligence technology helps in understanding the interactions of coronavirus with receptors through the computer-aided drug designing process (CADD). The ligand-protein interactions were prepared with the Maestro (Schrödinger) program which aids to study the docking pose of artemisinin compound with SARS-CoV-2 receptors like 7CTT, a nonstructural protein (NSP) and 7MY3 Spike glycoprotein. Thus, Artificial Intelligence technology examines the drug-target interaction with Neural Networking built with a deep learning machine algorithm and predicts the outcome of the disease with python program language. Results: Artemisinin exhibited the highest antiviral activity against the SARS-CoV-2 receptors like 7CTT and 7MY3. The three-dimensional structures of the ligands and SARS-CoV-2 receptors were retrieved from the PubChem Open Chemistry Database. The ligand-protein interactions were performed with the help of the Maestro (Schrödinger) program, which revealed MM/GBSA values of 7CTT interaction with derivative ligands of antimalarial compounds such as D95 (-45.424), artemisinin (-35.222), MPD (-31,021), MRD (-21.952) and 6FGC (-34.089), whereas with 7MY3 spike glycoprotein interactions MMGBSA values for D95 (-26.304), MPD(-18.658), MRD(-28.03) and 6FGC (-13.47) binding affinities have followed Lipinski rule of 5 and further predicted the outcome with random forest decision tree with an accuracy of about 75% with python program. Conclusion: Repurposing of the drug through an In-silico approach against the SARS-CoV-2 virus revealed its antiviral actions. The docking studies approach has shown the XP score, gliding energy, and MMGBSA values which were predicted with a deep learning program built with Artificial Intelligence technology. © 2022,Jordan Journal of Biological Sciences. All Rights Reserved.
ABSTRACT
Human Corona Viruses (HCoVs) are the displaying pandemic disease globally. It is commonly regarded as the pathogens of the respiratory system coupled in the midst of broad assortment of respiratory diseases. These HCoVs also revealed to possess an enveloped positive-sensed RNA viruses and multiplies mainly in the protoplasm of the host. It is been acknowledged that the virus common route of its entry is by contacting and later coalescing with the external surface of the host. Once entered the niceties of the corona virus need to be clearly understood. Although several advancements acknowledged about its structural properties as well as emphasized physicochemical and molecular mechanism of the coronavirus yet unable to distinctively comprehend on the unusual property of trimeric S protein. Characteristically the action of the coronavirus fusion peptide (FP) and induction of consensual alteration in the viral fusion proteins necessitate for comprehension. In our present review, we have imparted a concise introduction to corona viruses and discussed on their replication, physiology and molecular mechanism of pathogenicity, further portray different types of corona viruses. Hence this review noticeably highlights the outbursts of the mutated form of (SARS-CoV) commonly named as Severe Acute Respiratory Syndrome. Corona virus now basically regarded COVID-19 all over world.