AI Steps Up as a Powerful Ally in Pollutant Assessment and Environmental Toxicology

Environmental toxicology is drowning in chemicals it cannot keep up with. Industrial chemistry now produces an expanding universe of synthetic compounds, nanomaterials, transformation products, and complex mixtures, while the traditional toolkit of animal testing remains too slow, too expensive, and ethically fraught to evaluate thousands of substances at once. A new narrative review published in Discover Artificial Intelligence argues that predictive artificial intelligence has matured into a genuine complement to experimental toxicology, capable of integrating chemical structures, high-throughput screening data, omics profiles, and environmental sensor measurements to prioritize pollutants and support mechanism-informed risk assessment. But the authors, led by Ehsan Ghassemi Barghi and Nasrin Ghassemi Barghi, are equally emphatic about the caveats: dataset bias, domain shift, weak external validation, and limited transferability across chemical classes and ecosystems mean that AI should be treated as a partner to laboratory evidence, never a standalone replacement.

The review, which surveyed peer-reviewed literature published primarily between January 2015 and May 2026 across PubMed, Scopus, Web of Science, and Google Scholar, organizes the field around the principal stages of AI-assisted toxicology. Machine-learning algorithms such as random forests and support vector machines remain workhorses for classification and regression tasks built on molecular descriptors, while k-nearest neighbors and naive Bayes classifiers offer more interpretable hazard categorization. Deep-learning architectures go further, learning patterns directly from raw molecular structures, biological sequences, images, and sensor time series. Graph neural networks, for instance, represent molecules as networks of atoms and bonds, using message-passing operations to learn structural features without relying exclusively on hand-crafted descriptors, making them particularly suited to molecular toxicity prediction and structure–activity analysis. Transformer models, which use attention mechanisms to capture long-range dependencies, have been applied to chemical-structure representations for predicting acute and chronic toxicity in aquatic organisms.

Perhaps the most forward-looking architectures blend data with mechanism. Physics-informed neural networks incorporate established equations and toxicokinetic–toxicodynamic relationships directly into model training, potentially improving biological plausibility when empirical data are scarce, though the authors caution that incorrect mechanistic assumptions can themselves introduce bias. Foundation models pretrained on large molecular and biological datasets promise to reuse broadly learned representations in data-poor tasks, yet their application in environmental toxicology remains exploratory, constrained by pretraining bias, uncertain data provenance, and limited endpoint-specific validation. The review also proposes a four-stage maturity framework for responsible adoption: data readiness, model development, scientific validation, and regulatory implementation, with progression to each stage permitted only when the requirements of the previous one are met.

On the hazard-prediction front, AI-based ecotoxicity models use molecular descriptors, structural fingerprints, physicochemical properties, and high-throughput bioactivity data to estimate toxicological endpoints for industrial chemicals, with their principal practical value lying in screening large chemical inventories and flagging compounds that need targeted experimental investigation. Pesticides and herbicides have attracted particular attention, with machine learning and graph-based neural networks modeling interactions between chemical structures and biological endpoints, including curated datasets on honey bee toxicity and terrestrial and aquatic organism responses. Quantitative benchmarks matter here: the DeepTox model achieved an overall test-set area under the receiver operating characteristic curve of 0.846 in the Tox21 Challenge, and regression tasks such as predicting median lethal concentration are typically evaluated using the coefficient of determination, root mean square error, and mean absolute error, all of which are endpoint- and dataset-specific.

High-throughput AI screening is emerging as the field’s answer to contaminants of emerging concern, including per- and polyfluoroalkyl substances, microplastics, and novel pharmaceuticals, for which empirical toxicity data are often scarce or entirely absent. Deep-learning classifiers, structural alerts, and read-across frameworks can estimate potential ecological hazards from known chemical analogues, though confidence drops sharply when emerging contaminants lack sufficiently similar compounds or relevant biological endpoints in the training data. Mixture toxicity presents an even harder problem, since real-world organisms face continuous cocktail effects from combinations whose synergistic, additive, or antagonistic interactions are nearly impossible to test exhaustively. Deep-learning ensembles can represent multidimensional relationships among chemical structures and biological targets, but the review stresses that their ability to predict mixture toxicity remains limited by sparse interaction data, combinatorial complexity, and insufficient external validation across mixture compositions and exposure ratios.

Beyond hazard prediction, AI is reshaping environmental monitoring itself. Machine-learning models applied to sensor networks, satellite imagery, Internet of Things devices, and laboratory measurements now support particulate-matter forecasting, spatial mapping of heavy-metal contamination, anomaly detection, and estimation of pollutant loads in surface water and groundwater, expanding spatial and temporal coverage and enabling earlier identification of unusual pollution patterns. Exposure assessment benefits as well: algorithms such as random forests, support vector machines, and deep neural networks can integrate chemical concentrations, human biomonitoring results, land-use information, and demographic data to estimate cumulative exposures across inhalation, ingestion, and dermal contact routes, identifying highly exposed populations that traditional methods may overlook. At the ecosystem level, AI models can trace contaminant movement through food chains, predict species vulnerabilities, and link chemical exposure to population-level effects within ecological risk assessment.

One of the most striking practical applications involves real-time water quality early warning. Long Short-Term Memory networks, a deep-learning architecture designed for sequence and time-series forecasting, are increasingly integrated with IoT sensor networks to capture long-term dependencies and nonlinear dynamics in high-frequency data. Rather than merely forecasting physicochemical parameters such as chemical oxygen demand or dissolved oxygen, these models can translate environmental fluctuations into predicted biological effects, coupling with Biological Early Warning Systems to classify behavioral or physiological stress responses of sentinel aquatic species, such as altered swimming trajectories in Daphnia magna or abnormal ventilatory rates in fish. Deep-learning architectures are also used to predict the onset of harmful algal blooms by analyzing multi-variable interactions among water temperature, nutrient runoff, and historical patterns, shifting monitoring from passive environmental chemistry toward proactive ecological risk assessment.

Omics integration represents another frontier. Genomics, transcriptomics, proteomics, and metabolomics characterize molecular responses to contaminants across levels of biological organization, revealing biomarkers of oxidative stress, endocrine disruption, and xenobiotic metabolism. Machine-learning and deep-learning methods can combine gene-expression, protein, and metabolite profiles to identify coordinated molecular responses, pathway perturbations, and candidate biomarkers, while network-based approaches such as graph neural networks and Bayesian networks represent relationships among molecular events and phenotypic outcomes. High-throughput screening platforms such as ToxCast and Tox21 generate bioactivity profiles across large chemical libraries, and machine-learning models can organize these assay patterns to prioritize chemicals for targeted evaluation. The authors caution, however, that such integration does not automatically improve predictive accuracy or establish causal toxicological mechanisms.

Regulatory adoption is already underway in structured forms. Within the US Environmental Protection Agency, the CompTox Chemicals Dashboard integrates chemical, exposure, and bioactivity information, while the CERAPP and CoMPARA projects delivered consensus predictions of estrogen- and androgen-receptor activity to support prioritization. In European and OECD-related contexts, the OECD QSAR Toolbox supports chemical grouping, analogue identification, and read-across justification, and VEGA supports in silico prediction with applicability-domain assessment. Crucially, computational predictions enter regulatory workflows through screening, prioritization, grouping, and data-gap filling, and are then combined with in vitro, in vivo, exposure, and mechanistic evidence within Integrated Approaches to Testing and Assessment or Weight-of-Evidence frameworks. The final outcome is never an automatic AI-based hazard classification; regulators decide whether the combined evidence suffices for the intended decision.

The review’s most sobering section concerns limitations. Available datasets concentrate on well-studied pharmaceuticals, pesticides, and industrial chemicals, leaving emerging contaminants, transformation products, mixtures, and less-tested species underrepresented, which produces availability and selection bias. Domain shift, the change in statistical relationships between training and application data, can erode accuracy when models move across laboratories, chemical classes, regions, sensor platforms, or species. High internal-validation scores may simply reflect memorization of structurally similar chemicals when random data splitting places analogues in both training and test sets, so the authors call for scaffold-based, temporal, laboratory-independent, or geographically independent splits, transparent reporting of preprocessing and hyperparameters, and explicitly defined applicability domains marking out-of-domain predictions as extrapolations. Explainable AI methods such as SHAP and LIME can identify variables associated with individual predictions but should not be read as proof of causality. Hybrid models combining machine learning with adverse outcome pathways and mass-balance constraints may improve plausibility, yet they are no automatic fix. The conclusion is measured: with standardized datasets, transparent reporting, independent validation, and uncertainty characterization, AI can strengthen pollutant assessment and reduce unnecessary animal testing, but only as a complementary component within established weight-of-evidence frameworks.

Subject of Research: Applications and limitations of artificial intelligence in pollutant assessment and environmental toxicology

Article Title: Artificial intelligence approaches for pollutant assessment and environmental toxicology

Article References: Barghi, E. G., Motafeghi, F., Gharab, J. G., & Barghi, N. G. (2026). Artificial intelligence approaches for pollutant assessment and environmental toxicology. Discover Artificial Intelligence, 6(1), Article 983. https://doi.org/10.1007/s44163-026-02126-x

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02126-x

Keywords: artificial intelligence, environmental toxicology, machine learning, pollutant assessment, deep learning, ecotoxicity prediction, high-throughput screening, omics integration, environmental monitoring, regulatory toxicology, PFAS, mixture toxicity