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Updated: Jun 30, 2026

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
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Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Published on: April 18, 2019

Mapping toxicity pathways of per- and polyfluoroalkyl substances using interpretable classification-based machine

S Sarkar1, S Pore1, K Roy1

  • 1Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India.

SAR and QSAR in Environmental Research
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

New machine learning models predict toxicity for many per- and polyfluoroalkyl substances (PFAS), addressing data gaps for these persistent environmental contaminants and aiding risk assessment.

Keywords:
PERSISTPFASPer and polyfluorinated alkyl substancesQSTRadverse outcome pathway (AOP)machine learning

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Investigating Long-Distance Transport of Perfluoroalkyl Acids in Wheat via a Split-Root Exposure Technique
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Area of Science:

  • Environmental Chemistry
  • Toxicology
  • Computational Chemistry

Background:

  • Per- and polyfluoroalkyl substances (PFAS) are widespread environmental contaminants with known adverse health effects.
  • Limited experimental toxicity data exists for most PFAS, hindering comprehensive risk assessment.
  • High-throughput screening (HTS) provides valuable toxicity data but requires robust predictive models for large chemical spaces.

Purpose of the Study:

  • To develop and validate machine learning (ML)-based Quantitative Structure Toxicity Relationship (QSTR) models for predicting PFAS toxicity.
  • To identify robust QSTR models for three key high-throughput screening endpoints: ALDH1A1 inhibition (AID-1030), Nrf2 pathway inhibition (AID-504444), and TGF-β/Smad3 signalling inhibition (AID-588855).
  • To provide mechanistic insights into PFAS toxicity through structure-activity relationship analysis and develop a user-friendly screening tool.

Main Methods:

  • Development of ML-based QSTR classification models using various data balancing techniques (ADASYN, SMOTE, etc.) to address class imbalance.
  • Training and evaluation of 14 ML classifiers across multiple balanced datasets for each endpoint, resulting in 70 models per endpoint.
  • Utilizing Sum-of-Ranking-Differences (SRD), SHAP, and substructure analyses to identify optimal models and interpret structure-toxicity relationships.

Main Results:

  • Gradient Boosting, Random Forest, and Support Vector Classifier models were identified as optimal for AID-1030, AID-504444, and AID-588855, respectively.
  • SHAP and substructure analyses revealed key PFAS structural features influencing toxicity and provided mechanistic interpretability.
  • The developed QSTR models demonstrated good performance when applied to an independent external dataset of 2,361 PFAS.

Conclusions:

  • ML-based QSTR models can effectively predict PFAS toxicity across multiple endpoints, overcoming limitations of experimental data scarcity.
  • The developed models offer valuable tools for prioritizing PFAS for further testing and informing regulatory risk assessments.
  • A Python-based screening tool, PERSIST, was created to facilitate the application of these predictive models for environmental safety assessments.