Advancing pneumonia virus drug discovery with virtual screening: A cutting-edge fast and resource efficient machine learning framework for predictive analysis

Abstract

Pneumonia, a severe respiratory infection characterized by a significant morbidity and fatality rate, afflicts many individuals globally. The demand for highly effective antiviral medications has experienced a surge because of the emergence of novel pneumonia viruses, such as the COVID-19 coronavirus. Due to their inherent time and cost constraints, conventional drug development strategies sometimes need to be more manageable. Exploring alternative approaches is crucial to identifying and establishing effective therapy choices. This work introduces a computational methodology for analyzing the chemical space of medications targeting the pneumonia virus, employing Python-based data mining tools. Using computer-aided analysis in drug molecules aims to enhance the efficiency of identifying and evaluating potential new therapeutic candidates using Machine Learning (ML). The research successfully discovered two therapeutic compounds by utilizing the Bayesian Ridge approach, which is the most accurate with the least mean squared error, is less computationally expensive in terms of power, memory, and CPU, and is the fastest of the investigated approaches. It discovered the CHEMBL433378 and CHEMBL93653, with promising docking scores of −4.3 and −4.2, respectively. Additionally, both molecules demonstrated significant inhibitory activity against their respective targets, as seen by their IC50 values of 0.0018 and 0.001. Both compounds meet the criteria for the B. Mann Whitney U Test and Lipinski test.