ProQSAR: A modular and reproducible framework for small-data QSAR modeling with fit-and-use models - Journal of Cheminformatics

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 $$0.658\pm 0.11$$ 0.658±0.11), including a substantial improvement on FreeSolv (RMSE $$0.494$$ 0.494 vs. $$0.731$$ 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 (91.4%) while remaining competitive across other benchmark (overall classification average $$70.4\pm 11.6$$ 70.4±11.6). Crucially, all predictions are accompanied by cross-conformal prediction and explicit applicability-domain flags that identify out-of-distribution entries, enabling calibrated and decision support. Availability ProQSAR is released on PyPI, Conda, and Docker Hub; all releases embed full provenance (parameters, package versions, checksums) to ensure reproducibility. Scientific contribution ProQSAR (i) enforces best-practice, group-aware validation together with formal statistical comparisons across models, (ii) integrates calibrated uncertainty quantification (cross-conformal prediction) and applicability-domain diagnostics for interpretable, risk-aware predictions, and (iii) exposes both a composable developer API and a one-click pipeline that generates deployment-ready artifacts and human-readable reports, demonstrated on representative benchmarks.

ANNalog: generation of MedChem-similar molecules - Journal of Cheminformatics

Generative deep learning models have demonstrated significant potential in designing drug-like molecules. However, medicinal chemistry typically requires generating analogues that combine structural similarity with scaffold hopping, which is the replacement of molecular scaffolds while retaining biological relevance. To address this, we introduce ANNalog, a transformer-based sequence-to-sequence generative model trained on pairs of molecules extracted from the same bioactivity assay in a paper as recorded in ChEMBL33. The dataset was constructed based on the idea that molecules tested within the same assay can be considered analogues in medicinal chemistry space. Paired molecules were encoded as Simplified Molecular Input Line Entry System strings, and Levenshtein distance-guided alignment was applied to maximise intrapair string similarity; this preprocessing step was found to markedly enhance model performance. ANNalog has the ability to produce structurally similar analogues involving minor modifications, such as substituent replacements, as well as the ability to perform scaffold hopping, generating structurally distinct yet chemically relevant analogues. Scaffold-hopping capability was validated using manually curated molecule pairs and further confirmed through a case study involving orexin-2 receptor antagonists from patent literature. When the generation process was constrained using ANNalog’s prefix control feature, approximately 25% of the known scaffolds from the patent set were successfully recovered by the model, illustrating enhanced performance under user-guided conditions. Scientific Contribution: This study introduces ANNalog, a generative model trained using pairs of molecules synthesised and tested together within the same medicinal chemistry project. Unlike previous models trained on pairs of molecules selected according to similarity measures, ANNalog successfully generates not only structurally similar molecules but also diverse scaffold-hopping transformations that have precedent in the medicinal chemistry literature.

Research from the 13th International Conference on Chemical Structures

The 13th International Conference on Chemical Structures (ICCS 2025) will take place on June 1-5, 2025, in Noordwijkerhout, The Netherlands, where it has been ...

Posters – ICCS