Informing Mechanistic Toxicology with Computational Molecular Models

  • Michael R. Goldsmith
  • Shane D. Peterson
  • Daniel T. Chang
  • Thomas R. Transue
  • Rogelio Tornero-Velez
  • Yu-Mei Tan
  • Curtis C. Dary
Part of the Methods in Molecular Biology book series (MIMB, volume 929)


Computational molecular models of chemicals interacting with biomolecular targets provides toxicologists a valuable, affordable, and sustainable source of in silico molecular level information that augments, enriches, and complements in vitro and in vivo efforts. From a molecular biophysical ansatz, we describe how 3D molecular modeling methods used to numerically evaluate the classical pair-wise potential at the chemical/biological interface can inform mechanism of action and the dose–response paradigm of modern toxicology. With an emphasis on molecular docking, 3D-QSAR and pharmacophore/toxicophore approaches, we demonstrate how these methods can be integrated with chemoinformatic and toxicogenomic efforts into a tiered computational toxicology workflow. We describe generalized protocols in which 3D computational molecular modeling is used to enhance our ability to predict and model the most relevant toxicokinetic, metabolic, and molecular toxicological endpoints, thereby accelerating the computational toxicology-driven basis of modern risk assessment while providing a starting point for rational sustainable molecular design.

Key words

Docking Molecular model Virtual ligand screening Virtual screening Enrichment Toxicity Toxicoinformatics Discovery Prediction 3D-QSAR Toxicophore Toxicant In silico Pharmacophore 



Michael-Rock Goldsmith would like to thank James Rabinowitz and Stephen Little (from the US-EPA’s National Center for Computational Toxicology) for providing mentorship and assistance during his postdoctoral research, and providing the environment to explore molecular docking in the context of toxicology while providing insight and valuable discussion in the development of the in-house in silico chemical genomics initiative at the US-EPA.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Michael R. Goldsmith
    • 1
  • Shane D. Peterson
    • 1
  • Daniel T. Chang
    • 1
  • Thomas R. Transue
    • 2
  • Rogelio Tornero-Velez
    • 3
  • Yu-Mei Tan
    • 3
  • Curtis C. Dary
    • 4
  1. 1.National Exposure Research LaboratoryUS Environmental Protection AgencyResearch Triangle ParkUSA
  2. 2.Lockheed Martin Information TechnologyResearch Triangle ParkUSA
  3. 3.National Exposure Research LaboratoryU.S. Environmental Protection AgencyResearch Triangle ParkUSA
  4. 4.National Exposure Research laboratoryUS Environmental Protection AgencyResearch Triangle ParkUSA

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