This research addresses the presence of substances of very high concern (SVHCs) confronting the drinking water sector. Responding adequately to the potential hazards by SVHCs, knowledge of emission pathways, toxicity, presence in drinking water sources, and removability during water production is crucial. As this information cannot be received for each compound individually, we employed a detailed clustering approach based on chemical properties and structures of SVHCs from lists with over 1,000 compounds. Through this process, 915 substances were divided into 51 clusters. We tested this clustering in risk assessment. To assess the risks, we developed toxicity prediction models utilizing random forests and multiple linear regression. These models were applied to make toxicity predictions for the list of compounds. This study shows that clustering is a viable approach to reducing sample size. In addition, the toxicity models provide insights into the potential human health risks. This research contributes to more informed decision-making and improved risk assessment in the drinking water sector, aiding in the protection of human health and the environment. This principle is generally applicable. If in a group a suitable representative is found, data from experiments with this compound can be used to gauge the behaviour of chemicals in this group.

  • More than 1000 mostly substance of very high concern (SVHC) substances were grouped into 51 clusters.

  • Toxicity prediction with models based on functional groups was low for the random forest analysis.

  • Linear regression models were better suited for toxicity predictions.

  • Predictability of the toxicity of substances based on structural properties is still insufficient.

  • Based on most of the models, unknown substances cannot be classified as SVHCs.

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