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Interpretable multi-modality consensus QSAR framework: integrating machine and deep learning for enhanced

Fauzan Syarif Nursyafi1, Muhammad Adnan Pramudito2, Yunendah Nur Fuadah3

  • 1Department of Biomedical Engineering, Computational Medicine Lab, Kumoh National Institute of Technology, Gumi, Republic of Korea.

Toxicology Mechanisms and Methods
|March 17, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational framework for predicting chemical toxicity across eight endpoints. The multi-modality consensus quantitative structure-activity relationship (QSAR) models offer improved accuracy and reliability for chemical safety assessments.

Keywords:
QSARSHAP XAImachine and deep learningmulti-endpoint toxicity predictionmulti-modality consensus

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Area of Science:

  • Computational toxicology
  • cheminformatics
  • predictive modeling

Background:

  • Experimental toxicity testing is costly and time-consuming.
  • Existing quantitative structure-activity relationship (QSAR) models often lack robustness due to single descriptor/algorithm use and limited datasets.
  • There is a need for more comprehensive and reliable computational methods for chemical safety assessment.

Purpose of the Study:

  • To develop an interpretable multi-modality consensus QSAR framework.
  • To predict eight diverse toxicity endpoints (skin sensitization, respiratory toxicity, AMES mutagenicity, hepatotoxicity, developmental toxicity, cardiotoxicity, drug-induced nephrotoxicity, neurotoxicity).
  • To integrate multiple molecular representations with machine learning and deep learning for enhanced prediction.

Main Methods:

  • Developed a consensus QSAR framework integrating diverse molecular representations.
  • Employed both machine learning and deep learning algorithms.
  • Optimized models using 10-fold cross-validation and weighted consensus prediction based on AUC.
  • Evaluated performance on unseen and external datasets.

Main Results:

  • Multi-modality consensus models achieved moderate to excellent performance (AUC 0.80-0.99, BACC 0.76-0.90) across all endpoints.
  • Consensus models significantly outperformed individual models for 7 of 8 endpoints (p < 0.05).
  • Applicability domain and SHAP analyses supported model reliability and biological plausibility.

Conclusions:

  • The developed multi-modality consensus framework provides a reliable and interpretable approach for broad-spectrum toxicity prediction.
  • This method enhances chemical safety assessment by offering accurate predictions across multiple toxicity endpoints.
  • The framework demonstrates broad applicability and robust performance for diverse chemical compounds.