AI Model Predicts Chemical Toxicity Across 151 Fish Species

Chemical pollution has become one of the most insidious drivers of biodiversity loss on the planet, and nowhere is the problem more difficult to quantify than in the world’s rivers, lakes, and oceans. Tens of thousands of synthetic compounds—pharmaceuticals, pesticides, industrial additives, tire-derived chemicals, and countless substances that have never been fully screened—circulate through aquatic ecosystems, accumulating in the tissues of fish and other organisms in ways that scientists can rarely measure directly. A new study published in Nature Water offers what its authors describe as a fundamental advance in how the internal exposure and toxicity of chemicals in aquatic life can be predicted, using artificial intelligence to bridge one of the widest gaps in modern ecological risk assessment: the sheer physiological diversity of the species at risk.

The research, led by Peiling Han, Jingwen Chen, Yongle Zhu, Jingyuan Yang, and Xuehua Li of Dalian University of Technology in China, together with Willie J. G. M. Peijnenburg of the Dutch National Institute for Public Health and the Environment and Leiden University, introduces the intelligent high-throughput multi-species physiologically based toxicokinetic model, abbreviated HM-PBTK. At its core, the model addresses a stubborn technical problem. Physiologically based toxicokinetic models have long been a cornerstone of toxicology because they simulate how a chemical enters an organism, distributes through its tissues, is metabolized, and is ultimately eliminated—the so-called ADME processes. But these models depend on dozens of species-specific parameters, such as blood flow rates, tissue volumes, tissue composition, and metabolic clearance rates, that are known for only a handful of laboratory species like zebrafish and rainbow trout. For the vast majority of the more than 30,000 fish species on Earth, such data simply do not exist.

The Chinese-led team’s solution was to build machine-learning models capable of predicting these physiological and biochemical parameters across species that have never been tested. Drawing on a multimodal dataset that integrates biological traits, phylogenetic information, environmental context, and chemical properties, the researchers trained AI systems to estimate the parameters that a toxicokinetic model needs, from cardiac output and oxygen consumption to in vitro intrinsic clearance rates. The resulting framework covers 151 freshwater and marine fish species, spanning a phylogenetic and ecological range that conventional modeling approaches could never approach. In effect, the team taught an algorithm to infer the internal plumbing and biochemistry of fish it has never seen, using patterns extracted from species that have been studied.

Once the AI-predicted parameters are plugged into the toxicokinetic model, the system can quantify how much of a given chemical accumulates in specific tissues—the liver, the gills, the blood, the gonads—under realistic exposure scenarios. This tissue-specific internal dose is the quantity that matters for toxicology, because the concentration of a chemical at its site of action, not merely its concentration in the surrounding water, determines whether harm occurs. The researchers validated the model against an extensive literature-derived dataset of 703 internal exposure measurements spanning multiple species and chemicals, providing an unusually rigorous test of the framework’s predictive power.

The performance results are striking. In a case study focused on oestrogenic effects—the induction of vitellogenin, an egg-yolk precursor protein that serves as a classic biomarker of endocrine disruption in fish—the model’s quantitative in vitro to in vivo extrapolation, or QIVIVE, placed 85 percent of toxicity predictions within fivefold of the corresponding experimental observations. In a field where predictions spanning orders of magnitude are common, and where animal testing for every species-chemical combination is impossible, a fivefold window across such a diverse species set represents a substantial gain in reliability. The case study is also ecologically pointed: synthetic oestrogens from wastewater treatment effluent have been shown in earlier work, including a landmark 2007 study in the Proceedings of the National Academy of Sciences, to collapse entire fish populations in experimental lakes.

The implications extend well beyond endocrine disruption. The researchers demonstrated the model’s application to chemicals that are frequently detected in the environment, simulating absorption, distribution, metabolism, and excretion in both freshwater and marine species under real exposure conditions. The framework handles both neutral and ionizable chemicals, a critical distinction because many pharmaceuticals and emerging contaminants carry electrical charges that dramatically alter how they move through biological membranes and how they partition into tissues. Earlier multispecies toxicokinetic efforts, including those by Brinkmann and colleagues and Mangold-Döring and colleagues in Environmental Science & Technology, laid important groundwork but were limited in species coverage and chemical scope; the new AI-driven approach scales the concept by orders of magnitude.

Recognizing that a powerful model is only as useful as it is accessible, the team also built a user-friendly web platform designed to make the technology available to risk assessors, regulators, and researchers who are not modeling specialists. The platform allows users to conduct comprehensive exposure-toxicity predictions for chemicals simply by setting up an exposure scenario—specifying the chemical, the environmental concentrations, and the species or conditions of interest—without writing code or manually parameterizing differential equations. This kind of operational tooling matters because regulatory ecological risk assessment, governed by frameworks such as the European Union’s chemicals legislation and the United Nations’ post-2020 global biodiversity framework, is under mounting pressure to evaluate thousands of substances for which experimental data are sparse.

The timing of the work is significant for reasons that go beyond computational novelty. Chemical pollution is now recognized alongside climate change and habitat destruction as a primary driver of global biodiversity decline. The landmark tire-rubber-derived chemical 6PPD-quinone, which was shown in 2021 in Science to kill coho salmon within hours of stormwater runoff exposure, illustrated how a single ubiquitous contaminant can devastate a wild fish population before anyone knew it was toxic. Meanwhile, the demand for animal testing is under ethical and practical strain: European statistics show millions of fish used in regulatory toxicity testing, and the scientific community has embraced replacement, reduction, and refinement principles. A validated computational framework that predicts internal exposure and toxicity without live animals directly serves those goals, offering regulators a route to screening that is faster, cheaper, and humane.

The study’s technical architecture reflects broader trends in computational toxicology, where machine learning has begun to infuse every layer of physiologically based pharmacokinetic and toxicokinetic modeling. Prior work had demonstrated multimodal deep learning for predicting drug clearance in humans and machine-learning models for tissue-to-blood partition coefficients, but the translation of these techniques to ecological species—where data are scarcer, species diversity is vastly greater, and environmental variables such as temperature and salinity complicate parameterization—required the kind of systematic data assembly and model integration this team undertook. By combining AI-predicted physiology with established toxicokinetic equations, the approach retains the mechanistic interpretability that regulators demand while gaining the coverage that pure data-driven models lack.

The researchers have made their work openly available to accelerate adoption: the source code for the HM-PBTK model is hosted on GitHub, and the underlying data have been deposited on figshare, alongside extensive supplementary information detailing the species datasets, model construction, and validation results. The study was supported by the National Key Research and Development Program of China, the National Natural Science Foundation of China, and the Programme of Introducing Talents of Discipline to Universities. Whether the framework becomes a standard tool in regulatory risk assessment will depend on further independent validation and integration into formal assessment guidelines, but the direction is clear. As chemical inventories continue to expand and monitoring budgets remain constrained, the ability to predict which chemicals will reach which tissues of which fish—and at what internal concentrations—may prove one of the most consequential applications of artificial intelligence in the service of aquatic biodiversity protection.

Subject of Research: AI-driven prediction of internal chemical exposure and toxicity in freshwater and marine fish for aquatic ecological risk assessment.

Article Title: Advancing aquatic ecological risk assessment through AI-driven prediction of chemical exposure and toxicity in freshwater and marine fish

Article References: Han, P., Chen, J., Zhu, Y., Yang, J., Peijnenburg, W. J. G. M., & Li, X. (2026). Advancing aquatic ecological risk assessment through AI-driven prediction of chemical exposure and toxicity in freshwater and marine fish. Nature Water. https://doi.org/10.1038/s44221-026-00709-7

Image Credits: AI Generated

DOI: 10.1038/s44221-026-00709-7

Keywords: aquatic toxicology, ecological risk assessment, machine learning, PBTK model, chemical pollution, fish biodiversity, internal exposure, toxicity prediction, freshwater ecosystems, marine ecosystems, endocrine disruption, ADME simulation