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In Silico Analysis of Contaminant Persistence: From QSARs to Machine Learning Models.

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Summary
This summary is machine-generated.

In silico persistence modeling has advanced from quantitative structure-activity relationships (QSARs) to machine learning (ML), enabling comprehensive environmental fate predictions for chemicals, polymers, and materials. This evolution supports robust risk assessment and decision-making for environmental contaminants.

Keywords:
artificial intelligencechemical contaminant fatedeep learningenvironmental systemslarge language models (LLMs)persistence end points

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Area of Science:

  • Environmental Chemistry
  • Computational Chemistry
  • Materials Science

Background:

  • In silico persistence modeling has evolved significantly over six decades.
  • Early quantitative structure-activity relationships (QSARs) were limited to closely related chemicals.
  • Modern machine learning (ML) models can handle heterogeneous data and broader chemical spaces for environmental fate assessments.

Purpose of the Study:

  • To review the evolution of in silico persistence modeling from QSARs to ML.
  • To define comprehensive endpoints for environmental persistence beyond single properties.
  • To outline a roadmap for advancing persistence assessment across diverse materials.

Main Methods:

  • Describing various data representations for reactants (descriptors, fingerprints, molecular graphs, images, text).
  • Detailing methods to capture environmental system features (chemical, biological, optical, spectroscopic).
  • Summarizing ML concepts, workflows, and advances in product/pathway prediction, emphasizing interpretability and uncertainty.

Main Results:

  • Persistence modeling now encompasses physicochemical properties, partitioning, degradation kinetics, transformation products, and material metrics.
  • Improved data availability through curated datasets and literature-scale curation.
  • ML models show advances in predicting transformation products and pathways with considerations for interpretability and applicability domains.

Conclusions:

  • A practical roadmap is proposed, including standardized reporting, benchmark datasets, and hybrid modeling approaches.
  • The goal is to transition persistence assessment from ad hoc studies to coordinated, decision-ready predictions.
  • This advancement is crucial for evaluating environmental persistence across small molecules, polymers, and materials.