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Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
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Towards Explainable Computational Toxicology: Linking Antitargets to Rodent Acute Toxicity
Ilia Nikitin1, Igor Morgunov1,2, Victor Safronov1,3
1Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), Bolshoy Karetny per. 19, Moscow 127051, Russia.
Pharmaceutics
|December 31, 2025
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.
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.
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