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Summary

This study introduces a new dataset linking chemical compounds to mouse LD50 values and protein interactions, aiding the development of explainable toxicity prediction models.

Keywords:
LD50acute toxicityantitargetscomputational toxicologyinverse dockingmolecular dockingmolecular initiating event (MIE)off-targetssafety pharmacology paneltoxicodynamics

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Area of Science:

  • Computational toxicology
  • cheminformatics
  • pharmacology

Background:

  • Developing explainable predictive tools is a key trend in computational toxicology.
  • Challenges include mechanistic complexity and data scarcity.

Purpose of the Study:

  • To create a publicly available dataset for advancing transparent, mechanism-aware toxicity modeling.
  • To investigate the association between compound-protein interactions and acute toxicity.

Main Methods:

  • Compiled a dataset of 12,654 compounds with mouse intravenous LD50 values.
  • Performed docking simulations (Vina-GPU 2.0) against 44 toxicity-associated proteins.
  • Applied NIH and Brenk filters to refine the chemical space.

Main Results:

  • Identified strong associations between acute toxicity and specific proteins: hERG/KCNH2, AVPR1A, CACNA1C, KCNQ1, and EDNRA.
  • Observed significant differences in LD50 values between compounds binding to antitargets and non-binders.
  • Demonstrated the utility of inverse docking for elucidating mechanisms of action using known bioactive molecules.

Conclusions:

  • The developed dataset serves as a valuable resource for transparent and mechanism-aware toxicity modeling.
  • Openly available data facilitates further research in predictive toxicology.