ProQSAR: A modular and reproducible framework for small-data QSAR modeling with fit-and-use models
Abstract
Background: Quantitative structure-activity relationship (QSAR) models are central to computer-aided drug discovery and predictive toxicology, but practical adoption is often impeded by ad-hoc tooling, inconsistent validation protocols, and poor reproducibility.
Objective: We introduce ProQSAR, a modular, reproducible workbench that formalizes end-to-end QSAR development while permitting independent use of each component.
Methods: ProQSAR composes interchangeable modules for standardization, feature generation, splitting (including scaffold- and cluster-aware splits), preprocessing, outlier handling, scaling, feature selection, model training and tuning, statistical comparison, conformal calibration, and applicability-domain assessment. The pipeline can run end-to-end to produce versioned artifact bundles (serialized models) and analyst-oriented reports suitable for deployment and audit.
Results: On representative MoleculeNet benchmarks evaluated under Bemis-Murcko scaffold split, ProQSAR attains state-of-the-art descriptor-based performance: the lowest mean RMSE across the regression suite (ESOL, FreeSolv, Lipophilicity; mean RMSE 0.658±0.11 ), including a substantial improvement on FreeSolv (RMSE 0.494 vs. 0.731 for a leading graph method). On quantum mechanical benchmarks, ProQSAR demonstrated superior performance on the single-task dataset QM7 and maintained competitive results on the multi-task QM8 dataset. For classification, ProQSAR achieves the top ROC-AUC on ClinTox…
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