QSARQSPR

QSAR (quantitative structure–activity relationship) and QSPR (quantitative structure–property relationship) are computational modeling paradigms used in chemoinformatics to relate chemical structure to a measured property or biological activity. In QSAR, the target is a biological response such as receptor binding or toxicity; in QSPR, the target is a physicochemical property like logP, solubility, or boiling point. The combined term QSAR/QSPR is often used when discussing both activity and properties.

A typical workflow starts with data collection and curation, followed by encoding each molecule with descriptors that quantify structural and physicochemical characteristics. Descriptors may be constitutional, topological, geometric, electronic, or three-dimensional, and may also include fingerprint-based features or machine-learned embeddings. Model building then relates these descriptors to the target value using statistical or machine learning methods, including linear regression and partial least squares, as well as modern techniques such as random forests, support vector machines, gradient boosting, or neural networks.

Validation is essential and usually includes cross-validation, external test sets, Y-randomization tests, and an assessment of the applicability domain to determine where predictions are reliable. Good QSAR/QSPR models aim for interpretability, predictive accuracy, and robustness to data variation.

Applications span drug discovery, toxicity and environmental risk assessment, materials science, and property prediction for chemicals lacking experimental measurements. Limitations include data quality and bias, overfitting, limited applicability to novel chemotypes, and the need for careful descriptor selection and domain definition. Ongoing advances in descriptor design, machine learning, and data sharing continue to expand the utility of QSAR/QSPR.