Mode-of-Action-Guided, Molecular Modeling-Based Toxicity Prediction: A Novel Approach for In Silico Predictive Toxicology

  • Ping GongEmail author
  • Sundar Thangapandian
  • Yan Li
  • Gabriel Idakwo
  • Joseph Luttrell IV
  • Minjun Chen
  • Huixiao Hong
  • Chaoyang Zhang
Part of the Challenges and Advances in Computational Chemistry and Physics book series (COCH, volume 30)


Computational toxicology is a sub-discipline of toxicology concerned with the development and use of computer-based models and methodology to understand and predict chemical toxicity in a biological system (e.g., cells and organisms). Quantitative structure–activity relationship (QSAR) has been the predominant approach in computational toxicology. However, classical QSAR methodology has often suffered from low prediction accuracy, largely owing to the lack or non-integration of toxicological mechanisms. To address this lingering problem, we have developed a novel in silico toxicology approach that is based on molecular modeling and guided by mode of action (MoA). Our approach is implemented through a target-specific toxicity knowledgebase (TsTKb), consisting of a pre-categorized database of chemical MoA (ChemMoA) and a series of pre-built, category-specific classification and quantification models. ChemMoA serves as the depository of chemicals with known MoAs or molecular initiating events (i.e., known target biomacromolecules) and quantitative information for measured toxicity endpoints (if available). The models allow a user to qualitatively classify an uncharacterized chemical by MoA and quantitatively predict its toxicity potency. This approach is currently under development and will evolve to incorporate physiologically based pharmacokinetic (PBPK) modeling to address absorption, distribution, metabolism and excretion (ADME) processes in a biological system. The fully developed approach is believed to significantly advance in silico -based predictive toxicology and provide a new powerful toolbox for regulators, the chemical industry and the relevant academic communities.


Mode of action (MoA) Molecular dynamics (MD) simulation Molecular docking Deep learning Predictive toxicology Target-specific toxicity knowledgebase (TsTKb) Chemical mode of action database (ChemMoA) Qualitative classification Quantitative prediction Quantitative structure–activity relationship (QSAR) 





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Endocrine Disruptor Knowledge Base


Endocrine Disruptor Screening Program


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Safety Evaluation Ultimately Replacing Animal Testing


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Toxin and Toxin Target Database


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Toxicity Forecaster


Target-specific toxicity knowledgebase


US Department of Agriculture


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ping Gong
    • 1
    Email author
  • Sundar Thangapandian
    • 1
  • Yan Li
    • 2
  • Gabriel Idakwo
    • 3
  • Joseph Luttrell IV
    • 3
  • Minjun Chen
    • 4
  • Huixiao Hong
    • 4
  • Chaoyang Zhang
    • 3
  1. 1.Environmental LaboratoryUS Army Engineer Research and Development CenterVicksburgUSA
  2. 2.Bennett Aerospace, Inc.CaryUSA
  3. 3.School of Computing Sciences and Computer EngineeringUniversity of Southern MississippiHattiesburgUSA
  4. 4.Division of Bioinformatics and BiostatisticsNational Center for Toxicological Research, U.S. Food and Drug AdministrationJeffersonUSA

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