A conceptual deep learning framework for COVID-19 drug discovery

The analytical and experimental methods used for the development of drugs have some disadvantages in the aspect of the needed time for preparation of the desired parenthetical products and the efficiency of them, which not only can the risk for failure increase, particularly when pathogens are impossible to be cultivated under laboratory conditions, but these approaches can also lead to achieving arrays of antigens that are not able to provide sufficient immunity to combat the targeted disease. On the other hand, artificial intelligence (AI) and its new branches, including deep learning (DL) and machine learning (ML) techniques can be deployed for drug development purposes in order to alleviate the difficulties associated with conventional methods. Moreover, intelligent methods will provide researchers with the opportunity to use some user-friendly and efficient services to conquer such problems. In this respect, a conceptual DL framework has been studied in order to demonstrate the capability and applicability of these methods. Accordingly, a framework has been proposed to show how COVID-19 drug development can benefit from the potentials of AI and DL.