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Currently, there is no effective cure for SARS-COVID-19 diseases. The identification of novel therapeutic targets and drug-like compounds is required for the development of anti-COVID-19 drugs. Virtual screening is currently the most significant component for identifying drug-like molecules from large datasets for drug design and development. But there are no effective easily available and user-friendly applications for virtual screening of drug leads against SARS-COV-2. Therefore, we have developed a user-friendly web-app named “AIDrugApp” for the virtual screening of inhibitor molecules against SARS-CoV-2. AIDrugApp is a novel open-access, deep learning AI-based inhibitory activity prediction and data statistics visualization platform. Users can predict the inhibitory activities (Active/ Inactive) and pIC-50 values of new compounds against SARS-CoV-2 replicase polyprotein, 3CLpro and human angiotensin-converting enzymes. It is also useful for virtual screening of chemical features of molecules towards SARS-COVID-19 clinical trial bioactivities. This paper presents the development and architecture of AIDrugApp. We also present two case studies where large sets of molecules were screened by our app. Screened molecules were analyzed further by molecular docking and ADME analysis to identify the potential drug candidates. Web-App URL: https://sars-covid-app.herokuapp.com/ |
