Quantitative Structure-Activity Relationship (QSAR) is a powerful approach in computational chemistry that aims to establish a relationship between the chemical structure of molecules and their biological activities. QSAR models are built using data collected from diverse sources, such as public databases like Chembl. This mini essay outlines the key steps involved in QSAR, including data collection, data preprocessing, clustering analysis, and the development of structure-activity relationships using machine learning (ML) and deep learning (DL) algorithms.
QSAR is a data-driven approach that involves multiple stages, from data collection to developing structure-activity relationships. Chembl and other databases provide a valuable resource for collecting bioactivity data. Data preprocessing ensures the quality and compatibility of the data, while clustering analysis helps identify structural and activity similarities. ML and DL algorithms play a crucial role in modeling and predicting activities based on molecular structures. By leveraging these techniques, QSAR enables researchers to gain valuable insights, accelerate drug discovery, and optimize compound design in a cost-effective and time-efficient manner.