Deep Learning Approach for Discovery of In Silico Drugs for Combating COVID-19
February 18, 2022
(e first case of COVID-19 was detected in December 2019, and from then, it has overgrown, affecting millions of people around the globe. More than 2 million cases have been confirmed, with over 0.15 million deaths globally [1, 2]. Drug repurposing is defined as discovering and identifying newer applications for existing drugs in the treatment of various diseases [3]. Recent advancements in drug discovery using deep learning have made it possible to speed up identifying and developing new pharmaceuticals [4]. Various drugs, such as Arbidol, remdesivir, and favipiravir, have been tested to cure COVID-19 patients and many others are in the testing phase [4]. Biomedical researchers are investigating drugs for treating the patients, with an attempt to develop a vaccine for preventing the virus [5]. On the other hand, computer scientists have developed early detection models for COVID-19 from CT scans and X-ray images [5]. (ese techniques are a subset of deep learning and have been applied successfully in various fields [5]. Over the past few years, a significant increase in the quantity of biomedical data has resulted in the emergence of new technologies such as parallel synthesis and HTS (highHindawi Journal of Healthcare Engineering Volume 2021, Article ID 6668985, 13 pages https://doi.org/10.1155/2021/6668985 throughput screening), to mining large-scale chemical data [6]. Since COVID-19 is transmitted from person to person, electronic devices based on artificial intelligence may play a crucial role in preventing the spread of this virus. With the expansion of the role of health epidemiologists, the pervasiveness of electronic health data has also increased
