@article {49128,
	title = {AIDrugApp: artificial intelligence-based Web-App for virtual screening of inhibitors against SARS-COV-2},
	journal = {Journal  of Experimental \& Thereotical  Artificial  Intelligence },
	volume = {35},
	year = {2023},
	month = {APR},
	pages = {395-443},
	type = {Article},
	abstract = {<p>
	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. However, 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 {\textquoteleft}AIDrugApp{\textquoteright} 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 visualisation 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 using the {\textquoteleft}Bioactivity Prediction{\textquoteright} module of our app. Screened molecules were analysed further for validation by molecular docking and ADME analysis to identify the potential drug candidates.</p>
},
	keywords = {ADME, deep neural network, drug designing, machine learning, Molecular docking, SARS-CoV-2, virtual screening, Web application},
	issn = {0952-813X},
	doi = {10.1080/0952813X.2022.2058619},
	author = {Karade, Divya and Karade, Vikas}
}
