Predictive Intelligence, Explainable by Design Our AI and machine learning workflows combine automated model building, atomistic descriptor enrichment, explainable predictions, and regulatory-compliant validation to turn complex scientific data into actionable, trustworthy decision-support tools. Our AI & ML Modelling Pipeline A structured approach from data curation to production-ready, explainable predictive models 1. Curate & Enrich Scientific Data Collect, clean, and harmonise experimental datasets from literature, databases, and project partners. Enrich with computationally derived atomistic descriptors to capture structural and physicochemical features beyond what experimental data alone provides. 2. Address Data Challenges Handle class imbalance through synthetic data generation techniques, manage dataset heterogeneity, and ensure broad chemical or material coverage. Apply rigorous quality control and meta-analysis to build robust, modelling-ready datasets. 3. Build & Optimise Models with AutoML Apply automated machine learning to systematically evaluate algorithms, optimise hyperparameters, and select the best-performing models. Use ensemble and consensus strategies from multiple modelling teams to improve accuracy and reduce bias. 4. Validate for Regulatory Acceptance Rigorously validate models according to OECD principles for QSAR development, including defined endpoints, unambiguous algorithms, defined applicability domains, and appropriate measures of goodness-of-fit and predictivity. 5. Explain, Deploy & Share Apply SHAP, permutation importance, and other explainability techniques to reveal key prediction drivers. Deploy validated models as user-friendly web services on the Enalos Cloud Platform and make datasets available through FAIR repositories.
