Effective Drug Candidates against Global Pandemic of Novel Corona Virus (nCoV-2019): A Probability Check through Computational Approach for Public Health Emergency.

The infection of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) started form Wuhan, Chinais a devastating and the incidence rate has increased worldwide. Due to the lack of effective treatment against SARS-CoV-2, various strategies are being tested in China and throughout the world, including drug repurposing. To identify the potent clinical antiretroviral drug candidate against pandemic nCov-19 through computational tools. In this study, we used molecular modelling tool (molecular modelling and molecular dynamics) to identify commercially available drugs that could act on protease proteins of SARS-CoV-2. The result showed that Saquinavir, an antiretroviral medication can be used as a first line agent to treat SARS-CoV-2 infection. Saquinavir showed promising binding to the protease active site compared to other possible antiviral agents such as Nelfinavir and Lopinavir. Structural flexibility is one of the important physical properties that affect protein conformation and function and taking this account we performed molecular dynamics studies. Molecular dynamics studies and free energy calculations suggest that Saquinavir binds better to the COVID-19 protease compared to other known antiretrovirals. Our studies clearly propose repurposing of known protease inhibitors for the treatment of COVID-19 infection. Previously ritonavir and lopinavir were proved an important analogues for SARS and MERS in supressing these viruses. In this study it was found that saquinavir has exhibited good G-score and E-model score compared to other analogues. So saquinavir would be prescribe to cure for nCov-2019 either single drug or maybe in combination with ritonavir.

Pharmacophore model and atom-based 3D quantitative structure activity relationship (QSAR) of human immunodeficiency virus-1 (HIV-1) capsid assembly inhibitors.

A potential anti-Human Immunodeficiency Virus (HIV) agent with novel mode of action is urgently needed to fight against drug resistance HIV. The HIV capsid protein is important for both late and early stages of the viral replication cycle and emerged as a promising target for the developing of small molecule inhibitors of HIV. We design a Pharmacophore and 3D Quantitative structure activity relationship (QSAR) model for HIV Capsid Protein inhibitors, which helps to identify overall aspects of molecular structure that govern activity and for the prediction of novel HIV Capsid inhibitors. The hypothesis was developed with a survival score of 3.6.The features, that is, two aromatic rings, one hydrophobic site and two acceptor regions were present in all the active compounds with good fitness score. Pharmacophore model was then validated by a partial least square and regression-based PHASE 3D QSAR cross-validation. The leave-n-out cross validation for test set (Q2) of the hypothesis is 0.636, the standard deviation (SD) value is 0.338, and the variance ratio (F-test) value is 74.5. Hypothesis also showed a leave-n-out cross validation for training set (R2, 0.928). Interestingly, the predicted activity of true test set compounds was found in the close vicinity of their experimental activity suggesting the methodology used and models generated can be applied to identify potential new chemical entities with better HIV-1 capsid assembly inhibition.Communicated by Ramaswamy H. Sarma.