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Related Experiment Video

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ToxCML: A Hybrid mfCoQ-RASAR-Based Platform Integrating Consensus QSAR and Read-Across for Comprehensive Multi-End

Fauzan Syarif Nursyafi1, Muhammad Adnan Pramudito2, Yunendah Nur Fuadah3

  • 1Computational Medicine Lab, Department of Biomedical Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.

Journal of Chemical Information and Modeling
|April 13, 2026
PubMed
Summary

ToxCML is a new computational platform that accurately predicts chemical toxicity across 18 endpoints for over 54,000 chemicals. This hybrid approach enhances quantitative structure-activity relationship (QSAR) and read-across (RA) methods, reducing the need for animal testing.

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Area of Science:

  • * Computational toxicology and cheminformatics.
  • * Development of predictive models for chemical safety assessment.

Background:

  • * Traditional animal-based toxicity testing is time-consuming, expensive, and ethically problematic.
  • * Existing in silico methods like Quantitative Structure-Activity Relationship (QSAR) and Read-Across (RA) have limitations including endpoint-specific modeling and restricted applicability domains.
  • * There is a need for robust, broadly applicable computational tools to predict chemical toxicity.

Purpose of the Study:

  • * To introduce ToxCML, a hybrid multifeature consensus quantitative RA Structure-Activity Relationship (mfCoQ-RASAR) platform.
  • * To unify consensus QSAR and similarity-based consensus RA into a single, weight-optimized workflow.
  • * To predict 18 toxicity endpoints for a large chemical dataset (54,601 compounds).

Main Methods:

  • * Integration of multiple molecular representations (e.g., MACCS, Morgan fingerprints, physicochemical descriptors).
  • * Combination of machine learning-based QSAR and k-Nearest Neighbors (k-NN) RA models.
  • * Implementation of tiered applicability domain analysis and chemical space mapping for robust predictions.

Main Results:

  • * The mfCoQ-RASAR models demonstrated strong discrimination and accuracy across all 18 endpoints (AUC ~0.86-0.99, BACC ~0.73-0.98).
  • * The hybrid approach achieved high performance, outperforming or matching consensus QSAR and RA.
  • * Over 95% in-domain coverage was generally achieved across endpoints, indicating broad applicability.

Conclusions:

  • * ToxCML provides reliable, chemically contextualized, and broadly applicable multi-endpoint toxicity predictions for novel compounds.
  • * The platform supports large-scale toxicity screening and hazard prioritization.
  • * ToxCML can significantly contribute to reducing animal testing in regulatory and industrial settings.