Quantitative structure activity relationship study for the prediction of complexity for 2-acetamido-2-deoxy-beta-D- glucopyranose structurally similar compounds using regression

ABSTRACT

Coronavirus disease, which is called as COVID-19 and that is one of the infectious diseases which is infected by newly found coronavirus. Maching Learning has the major role in predicting the drugs of the particular disease. Lalmuanawma et al. 2020 has given the application of machine learning and artificial intelligence in COVID'19. It is used to develop the model design, Regression is one of the supervised Machine Learning Techniques. It is used to predict the values based on the data given. In this research work, Quantitative structure activity relationship (QSAR) study has been developed for structurally similar to 2-acetamido-2-deoxy-beta-D-glucopyranose as inhibitors for COVID-19 causing targets using regression. QSAR models for complexity was created with 40 training compounds, 20 test compounds, and 21 different descriptors. The structurally 95% similar compound of 2-acetamido-2-deoxy-beta-D-glucopyranose has been collected from pubchem[13] and molinspiration.com. Using 40 compounds, the linear regression model has been developed. The predictive capability of the QSAR models was evaluated by Correlation coefficient, mean absolute error, Root mean squared error, Relative absolute error, Root relative squared error.