How NovaMechanics combined automated machine learning, synthetic data generation, and a novel applicability domain method to build a comprehensive nanosafety evaluation workflow for Ag, TiO₂, and CuO nanoparticles. Scaling Nanosafety Assessment Beyond Traditional Experiments Traditional methods for assessing nanoparticle safety are time-consuming, expensive, and rely heavily on animal testing. With over 5,300 commercial products containing nanoparticles and growing regulatory pressure under the European Green Deal and SSbD framework, faster computational alternatives are urgently needed. Key challenges include dataset scarcity and class imbalance in nanotoxicology data, the lack of robust applicability domain methods, and the need for models that non-informatics experts can confidently use and interpret. 110 NP treatments assessed (sample-concentration combinations) 3 NP types: Ag, TiO₂, CuO Novel AD: Three-step applicability domain method Integrate experimental and computational data: Combined High Throughput Screening with High Content Imaging data from the NanoMILE project with atomistic descriptors calculated from molecular mechanics force fields, enriching the experimental feature space with structural information. Address class imbalance with synthetic data: Applied SMOTE and ADASYN oversampling techniques to generate synthetic training samples for underrepresented classes, improving model performance on minority class predictions while maintaining dataset integrity. Automate model selection and optimisation: Deployed an AutoML scheme on the Enalos Cloud Platform to systematically evaluate classification and regression algorithms, optimise hyperparameters, and select the best-performing models for each toxicity endpoint. Develop a novel applicability domain: Created a three-step AD method combining bounding-box analysis, leverage approach, and a new local similarity assessment to classify each prediction as good, moderate, or poor reliability — enhancing user confidence. Deploy as SafeNanoscope web service: Made the validated models available through the SafeNanoscope application on the Enalos Cloud Platform, with data shared through the nanoPharos database for community access and reuse. AutoML — Automated Workflow: Systematic evaluation and selection of optimal ML algorithms 3-Step — Novel AD Method: Prediction reliability classified as good, moderate, or poor SMOTE — Synthetic Data: Oversampling improves minority class representation and model balance SSbD — Regulatory Alignment: Supports Safe and Sustainable by Design framework compliance Cloud — Web Deployed: SafeNanoscope freely accessible on Enalos Cloud Platform CSBJ — Peer-Reviewed: Published in Computational and Structural Biotechnology Journal, 2024
