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Machine Learning for Predicting Environmental Mobility Based on Retention Behavior.

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Identifying very persistent and very mobile (vPvM) substances is crucial for environmental protection. This study developed a cheminformatics model using chromatography data to predict chemical mobility, enabling early identification of vPvM compounds.

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Area of Science:

  • Environmental Chemistry
  • Cheminformatics
  • Toxicology

Background:

  • Very persistent and very mobile (vPvM) substances pose risks to ecosystems and human health.
  • Assessing chemical mobility is vital, but experimental data like the organic carbon-water partition coefficient (Koc) are scarce for most chemicals.
  • Thousands of new chemicals necessitate efficient prioritization tools.

Purpose of the Study:

  • To develop and validate a predictive model for chemical environmental mobility using readily available chromatography data.
  • To establish a scalable cheminformatics approach for identifying vPvM substances.

Main Methods:

  • Utilized reversed-phase liquid chromatography (RPLC) data from 146,902 chemicals to assign mobility labels.
  • Computed 881 PubChem fingerprints for each chemical to represent structural features.
  • Trained a random forest classifier to predict mobility based on RPLC retention behavior and chemical fingerprints.

Main Results:

  • The random forest model achieved high F1 scores: 0.87 (very mobile), 0.81 (mobile), and 0.96 (nonmobile) on the test set.
  • Applied to 64,492 REACH-registered chemicals, the model classified 20% as very mobile, 26% as mobile, and 53% as nonmobile.
  • Demonstrated the model's scalability for early identification of potentially harmful vPvM substances.

Conclusions:

  • A robust cheminformatics model effectively predicts chemical environmental mobility using RPLC data and structural fingerprints.
  • This approach offers a scalable solution for prioritizing chemicals, aiding in the early identification of vPvM substances.
  • The findings support proactive environmental risk assessment and management of chemical substances.