The research paper proposes a methodology to predict the extension of lockdown in order to eradicate COVID-19 from India. All the concepts related to Coronavirus, its history, prevention and cure is explained in the research paper. Concept used to predict the number of active cases, deaths and recovery is Linear Regression which is an application of machine learning. Extension of lockdown is predicted on the basis of predicted number of active cases, deaths and recovery all over India. To predict the number of active cases, deaths and recovery, date wise analysis of current data was done and necessary parameters like daily recovery, daily deaths, increase rate of covid-19 cases were included. Graphical representation of each analysis and prediction was done in order to make predicted results more understandable. The combined analysis was performed at the end which included the final result of total cases of coronavirus in India. Combined analysis included the no. of cases from start of COVID-19 to the predicted end of cases all over India.

Objective: The first case of a new strain of coronavirus (CoV), usually known as CoV disease (COVID)-19, was recognized in Wuhan city of China, in December 2019. Till today, there are no specific treatments available against COVID. During literature searching, it was observed that drugs such as remdesivir, hydroxychloroquine, and chloroquine as their therapeutic options to stop the progress of COVID-19 infections. In the present study, the molecular docking study was performed to understand the binding pattern of selected drugs. Methods: Molecular docking methods were carried out using molecular virtual Docker software using COVID-19 protease (PDB ID 6LU7), and interactions of these three drugs were visualized. Conclusion: All three drugs have shown binding interactions with an active site. We assume that these inhibitory activities helped us to identify the possible drug mechanism and further designing of new molecules or investigate the potential use of other available drugs.

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