Machine learning based software tools and computational strategies for QSAR modeling

QSAR analysis is one of the prominent areas in environmental toxicology, computational drug discovery, and chemical risk assessment. QSAR has transformed drug discovery by demonstrating mathematical relationships between chemical structures and pharmacological action via quantitative analysis. QSAR analysis employs a systematic workflow to ensure accurate predictions and useful insights into chemical component activity. A variety of QSAR software has been developed to aid QSAR modeling. QSAR software uses statistical and machine learning methods to predict the activity of various chemicals. They create predictive models using available data about the molecular structure and biological activity of various compounds, thus allowing researchers to better understand the qualities required for a certain biological activity. As a result, it facilitates drug discovery by allowing researchers to swiftly identify interesting chemicals for future laboratory testing. The classification is based on criteria like accessibility, type of algorithm used for model generation, or functionality. Advanced machine learning algorithms outperformed conventional linear methods such as Multiple Linear Regression (MLR).This review provides a comprehensive overview of the most widely used QSAR software, including both open source as well as commercial tools to assist researchers in choosing the best tools for their particular scientific and regulatory requirements.