QSPR

Quantitative structure–property relationship, or QSPR, is a field of computational modeling that predicts physicochemical properties of molecules from their chemical structure. It is a core area of chemoinformatics and a counterpart to QSAR, which typically targets biological activity. QSPR models relate descriptors to properties such as boiling point, logP, solubility, and refractive index.

Data and descriptors: QSPR requires curated datasets of compounds with measured properties. Descriptors encode structural features and include constitutional, topological, geometrical, electronic, and quantum-chemical types, as well as 3D descriptors and fingerprints. Descriptor calculation uses software like RDKit, Dragon, or quantum-chemical packages. An applicability domain helps gauge prediction reliability for a given molecule.

Modeling methods: QSPR employs regression and machine learning. Traditional approaches include multiple linear regression and partial least squares; nonlinear methods such as random forests, support vector regression, gradient boosting, and neural networks are common. Model performance is assessed by cross-validation and external tests using RMSE, MAE, and R^2 (or Q^2).

Workflow: Typical steps are data curation, descriptor calculation, dataset splitting, model development, validation, and deployment. Emphasis is placed on interpretability and on defining the applicability domain to indicate where predictions are credible.

Applications and limitations: QSPR supports drug design, materials science, environmental chemistry, and process optimization by forecasting properties before synthesis. Limitations include data quality and coverage, descriptor relevance, overfitting, and extrapolation beyond the training domain. Transparent reporting, external validation, and clear domain boundaries are important for reliable use, especially in regulated contexts.