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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

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Interpretable QSAR modelling for immunotoxicity prediction using enhanced fingerprint and SHAP-based feature

D R Shin¹, I H Song¹, S K Lee¹

¹Department of Chemistry, Hannam University, Daejeon, Republic of Korea.

SAR and QSAR in Environmental Research

|November 5, 2025

Summary

This study developed a new interpretable quantitative structure-activity relationship (QSAR) model to predict chemical immunosuppressive toxicity using immune cell data. The framework aids in early identification of harmful chemicals for safer drug development and chemical design.

Keywords:

QSAR XGBoost human immune cell lines immunosuppressive immunotoxicity

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Area of Science:

Toxicology
Computational Chemistry
Immunology

Background:

Accurate prediction of chemical immunotoxicity is crucial for safety evaluation and drug development.
Current methods face limitations due to scarce in vitro data and complex immune responses.

Purpose of the Study:

To introduce an interpretable quantitative structure-activity relationship (QSAR)-based modeling framework for assessing immunosuppressive toxicity.
To identify key molecular determinants of immunosuppression using machine learning and feature selection.

Main Methods:

Utilized IC50 data from Jurkat, peripheral blood mononuclear cells (PBMC), and THP-1 immune cell lines.
Employed three tree-based machine learning algorithms with feature selection.
Applied SHapley Additive exPlanations (SHAP) for model interpretability and structural alert extraction.

Main Results:

Identified critical molecular determinants linked to immunosuppressive effects.
Enhanced model interpretability provided mechanistic insights into immunotoxicity.
Demonstrated improved reliability of immunotoxicity predictions through integrated cell-specific data and interpretable modeling.

Conclusions:

The developed framework offers a scientifically grounded approach for early identification of immunotoxic chemicals.
This research supports safer chemical design and informed decision-making in toxicology and drug development.
Integration of cell-specific data and interpretable models enhances prediction accuracy for immunotoxicity.