Bioactivity classification of SARS-CoV-2 Proteinase using Machine Learning Approaches

ABSTRACT

Machine learning has seen a considerable increase in performance and interest in scientific research and industrial applications over the previous decade. The success of most current state-of-the-art methods can be linked to recent deep learning advancements. Deep learning has been demonstrated to outperform not only standard machine learning but also highly specialized tools designed by domain specialists when applied to many scientific fields involving the processing of non-tabular data, such as pictures or text. This article will cover ML-based research on SARS-Co V-2 Proteinase Biological Activity classification, with an emphasis on the most recent successes and research trends. SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) produced a global pandemic of coronavirus illness (COVID-19), which prompted a rush to find treatment options. Despite the attempts, no vaccine or medicine for therapy has been approved. In this paper, we mentioned some previous articles that have resulted in successful bioactivity prediction. The discussion of the machine having to learn technology that has been used for bioactivity prediction in general and has the potential to lead the way for successful working with complex molecules in the future is also a focus of this review. The study finishes with a brief viewpoint on contemporary machine learning research advances, including student engagement and semi-supervised learning, which offer considerable potential for increasing bioactive discovery. © 2022 IEEE.