G.AI.A: An Integrated Machine-Learning Platform for Predicting Bioaccumulation and Ecotoxicity of Pharmaceuticals

Pharmaceutical pollution in aquatic environments poses
a significant
ecological threat due to the accumulation of bioactive compounds from
human and veterinary sources. In support of the EU Green Deal’s
Chemicals Strategy for Sustainability, this study presents a computational
framework for predicting two key environmental risk indicators in
fish: bioconcentration and ecotoxicity. Bioconcentration, quantified
by the bioconcentration factor (BCF), reflects a chemical’s
tendency to accumulate in organisms, while ecotoxicity is assessed
via the median lethal concentration (LC50) over defined
exposure periods. We developed two high-performing machine learning
(ML) models, achieving ROC AUC scores of 94.60% for bioconcentration
and 96.06% for ecotoxicity, validated across both internal and external
data sets. To expand the scope of risk evaluation, we incorporated
metabolite prediction using the SyGMa tool, selected after benchmarking
multiple alternatives. This enables the assessment of both parent
compounds and their potentially toxic metabolites. Model interpretability
was enhanced through molecular fingerprint analysis, which identified
structural features associated with toxicity and accumulation, informing
the early stages of drug design. To support practical implementation,
we introduced G.AI.A (https://gaiatox.eu/), an intuitive web platform that allows users to input Simplified
Molecular Input Line Entry System (SMILES) strings for rapid prediction
of environmental risk end points. The application domain of G.AI.A
lies in predictive toxicology, enabling researchers and regulatory
bodies to assess the toxicological profiles of small organic compounds,
excluding those containing heavy metals, by analyzing their chemical
structures. The platform supports batch processing and offers interactive
visualizations, facilitating compound screening and early stage environmental
risk assessment. By integrating predictive modeling with interpretability
and usability, our framework advances green-by-design pharmaceutical
development and contributes to sustainable chemical management.