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Interpretable Multi-Modality Consensus QSAR Framework: Integrating Machine and Deep Learning for Enhanced Multi-Endpoint Toxicity Assessment

Authors:
FAUZAN SYARIF NURSYAFI¹, MUHAMMAD ADNAN PRAMUDITO², YUNENDAH NUR FUADAH³, and KI MOO LIM¹,⁴,⁵**

¹ Computational Medicine Lab, Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
² Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
³ Telecommunication Engineering Study Program, School of Electrical Engineering, Telkom University Main Campus, Bandung, Indonesia
⁴ Computational Medicine Lab, Department of Biomedical Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
⁵ Meta Heart Co., Ltd, Gumi, 39253, Republic of Korea

Corresponding authors: kmlim@kumoh.ac.kr

🧩 Overview

This repository contains the code for developing and evaluating QSAR (Quantitative Structure–Activity Relationship) models for multi-endpoint chemical toxicity prediction using an interpretable multi-modality consensus framework.

📌 Supplementary Materials
Comprehensive methodological details, dataset sources, descriptor definitions, hyperparameter configurations, and additional results are provided in:

  • Supplementary Online Materials.docx

This document should be consulted alongside the notebooks and manuscript to ensure full reproducibility and transparency.

🧠 Toxicity Endpoints

The framework covers 8 mechanistically distinct toxicity endpoints, comprising 30,160 unique compounds, following the original training, test, and external validation splits reported in the source datasets:

  1. Skin Sensitization
  2. Respiratory Toxicity
  3. AMES Mutagenicity
  4. Hepatotoxicity
  5. Developmental Toxicity
  6. Cardiotoxicity
  7. Drug-Induced Nephrotoxicity (DIN)
  8. Neurotoxicity

⚙️ Framework Integration

Molecular Representations

  • Fingerprints: Morgan, MACCS, Atom Pair Fingerprints (APF)
  • Physicochemical descriptors: RDKit- and CDK-derived properties

Learning Algorithms

  • Random Forest (RF)
  • XGBoost (XGB)
  • Support Vector Machine (SVM)
  • Deep Neural Network (DNN)

Model Evaluation

  • Stratified 10-fold cross-validation on training data
  • Independent test and external validation sets
  • Performance metrics: AUC, ACC, BACC, SEN, SPE, with 95% bootstrap confidence intervals

Consensus Modeling

  • Single-algorithm descriptor consensus
  • Multi-algorithm, multi-modality

🧠 Explainability & Interpretability AI (XAI) Analysis

  • SHAP-based explainable AI (XAI) for global and local feature attribution
  • Structure contribution map analysis for fingerprint-based models
  • Applicability domain (AD) assessment:
    • Tanimoto similarity-based AD for fingerprints
    • Leverage/Williams plot-based AD for physicochemical descriptors
  • UMAP-based chemical space visualization of training, test, and external compounds

📂 Repository Structure & Notebooks

1️⃣ Descriptor Computation & Data Preprocessing

Descriptor Computation_Preprosesing data.ipynb

  • Structure standardization (salts/solvents removal, charge normalization, tautomer handling)
  • Descriptor generation (MACCS, Morgan, APF, RDKit–CDK)
  • Label harmonization and export of QSAR-ready datasets

2️⃣ Machine Learning Model Training (10-fold CV)

Training_ML_10foldCrossvalidation.ipynb

  • Training RF, XGB, and SVM models for each descriptor modality
  • Stratified 10-fold cross-validation
  • Model selection based on cross-validated AUC

3️⃣ Deep Neural Network Training (10-fold CV)

Training_DNN_10foldCrossvalidation.ipynb

  • Construction of DNN architectures for each descriptor type
  • Stratified 10-fold cross-validation
  • Regularization and early stopping
  • Saving trained models for downstream consensus modeling

4️⃣ Model Evaluation & Consensus Construction

Performance_Model_Evaluation.ipynb

  • Loading trained base models
  • Construction of single- and multi-modality consensus models
  • Evaluation on independent test and external validation sets
  • Generation of final performance metrics

5️⃣ Chemical Space & Applicability Domain Analysis

Chemical Space_AD Analysis_Consensus.ipynb

  • Applicability domain assessment for individual and consensus models
  • UMAP-based visualization of chemical space coverage

6️⃣ Explainable AI (SHAP) Analysis

SHAP Analysis.ipynb

  • Global feature importance analysis
  • Descriptor- and bit-level contribution interpretation
  • Identification of key structural alerts associated with toxicity

🧮 Dependencies

Package Version
Python 3.x
RDKit 2025.3.2
CDK-pywrapper 0.1.1
scikit-learn 1.6.1
NumPy 2.1.3
Pandas 2.2.3
install-jdk 0.3.0
bounded-pool-executor 0.0.3

🧾 Notes

This repository corresponds to the manuscript:

“Interpretable Multi-Modality Consensus QSAR Framework Integrating Machine and Deep Learning for Enhanced Multi-Endpoint Toxicity Assessment.”

Additional methodological details, descriptor lists (Table S1), hyperparameter settings (Table S2), and dataset references are provided in the Supplementary Online Materials.

📚 Citation

Citation details will be updated upon publication.

🧠 Acknowledgments

This work was conducted at the Computational Medicine Lab, Kumoh National Institute of Technology, Gumi, Republic of Korea.