Unlocking the Potential of Clustering and Classification Approaches: Navigating Supervised and Unsupervised Chemical Similarity.

Journal: Environmental health perspectives
PMID: 39106156

Abstract

BACKGROUND: The field of toxicology has witnessed substantial advancements in recent years, particularly with the adoption of new approach methodologies (NAMs) to understand and predict chemical toxicity. Class-based methods such as clustering and classification are key to NAMs development and application, aiding the understanding of hazard and risk concerns associated with groups of chemicals without additional laboratory work. Advances in computational chemistry, data generation and availability, and machine learning algorithms represent important opportunities for continued improvement of these techniques to optimize their utility for specific regulatory and research purposes. However, due to their intricacy, deep understanding and careful selection are imperative to align the adequate methods with their intended applications.

Authors

  • Kamel Mansouri
    National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States.
  • Kyla Taylor
    Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
  • Scott Auerbach
    Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
  • Stephen Ferguson
    National Toxicology Program Division, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA.
  • Rachel Frawley
    Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
  • Jui-Hua Hsieh
    Division of Translational Toxicology, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC, USA.
  • Gloria Jahnke
    Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
  • Nicole Kleinstreuer
    National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, NIEHS, Durham, North Carolina 27560, USA.
  • Suril Mehta
    Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
  • José T Moreira-Filho
    LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás - UFG, Goiânia, Brazil.
  • Fred Parham
    Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
  • Cynthia Rider
    Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
  • Andrew A Rooney
    Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA.
  • Amy Wang
    From the Departments of Diagnostic Imaging (M.T.S., M.J., J.L.B., G.L.B., R.A.M.), Diagnostic Imaging (A.D.Y.), and Neurosurgery (M.J., R.A.M.), Warren Alpert School of Medicine at Brown University, Rhode Island Hospital, 593 Eddy St, APC 701, Providence, RI 02903; Department of Computer Science, Brown University, Providence, RI (J.V., M.P.D., Y.H.K., S.S.S., H.J.T., A.W., H.L.C.W., C.E., U.C.); and the Norman Prince Neuroscience Institute, Rhode Island Hospital, Providence, RI (M.J., R.A.M.).
  • Vicki Sutherland
    Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.

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