Informing mechanistic toxicology with computational molecular models.

Methods Mol Biol

National Exposure Research Laboratory, US Environmental Protection Agency, Research Triangle Park, NC, USA.

Published: February 2013

AI Article Synopsis

  • Computational molecular models enable toxicologists to obtain affordable and sustainable molecular-level insights that complement traditional experimental methods.
  • The text emphasizes the use of 3D molecular modeling techniques, such as molecular docking and 3D-QSAR, to help understand how chemicals interact with biological targets and inform toxicological mechanisms.
  • It outlines how integrating these modeling approaches with chemoinformatics and toxicogenomics creates a comprehensive workflow that enhances predictions of toxicological outcomes and supports risk assessment and sustainable molecular design.

Article Abstract

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.

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http://dx.doi.org/10.1007/978-1-62703-050-2_7DOI Listing

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