QSARmallit

QSARmallit, or QSAR models, are computational tools that aim to predict the activity, properties, or fate of chemical compounds from their molecular structure. The approach stems from the concept of quantitative structure–activity relationships, seeking mathematical links between descriptors of chemical structure and observed data such as biological activity, toxicity, or physicochemical properties.

Models rely on a variety of descriptors, including constitutional, topological, geometric, and electronic features, or on newer fingerprints and three-dimensional fields. They can be built with linear methods such as multiple linear regression or partial least squares, and with non-linear machine learning approaches such as random forests, support vector machines, or neural networks. A typical workflow includes data curation, descriptor calculation, model training, and validation.

Validation is essential and often includes internal cross-validation and external test sets. Performance is reported with metrics such as R-squared or Q-squared, RMSE or MAE, and the model’s applicability domain determines where predictions are considered reliable. Regulatory contexts, such as OECD guidelines, emphasize transparent reporting and validation of QSAR models for safety assessment.

Applications include early-stage drug discovery, toxicology screening, environmental risk assessment, and regulatory submissions. QSARmallit can prioritize compounds, reduce experimental workload, and guide hypothesis generation, but they depend on data quality and appropriate domain knowledge. They may struggle with novel chemotypes outside the training set and can be difficult to interpret.

Software tools range from commercial platforms to open-source libraries. Common resources include descriptor calculators, modeling packages, and repositories that support reproducibility and sharing of QSAR models.