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The research aims to predict drug-induced autoimmunity using a machine learning model based on molecular descriptors.

February 6, 20260 citationsOpen Access

Prediction of Drug-Induced Autoimmunity Using X Gradient Boost Machine Learning

Key Points

The research aims to predict drug-induced autoimmunity using a machine learning model based on molecular descriptors.
Developed an XGBoost classifier using 196 RDKit-derived molecular descriptors for 477 compounds.
Addressed class imbalance with SMOTE to improve model performance.
Evaluated model performance using ROC-AUC, recall, and precision metrics.
Performed five-fold cross-validation for generalization assessment.
Achieved ROC-AUC of 0.888 on the test set with 66.7% recall and 57.1% precision for the positive class.
Five-fold cross-validation showed strong generalization with ROC-AUC of 0.974±0.067.
Identified topological complexity, aromaticity, and polarity-related descriptors as important features.

Abstract

Drug-induced autoimmunity (DIA) comprises immunemediated adverse events such as lupus, hepatitis, and uveitis that can arise after extended drug exposure, complicating prospective risk assessment. We built a gradient-boosted tree (XGBoost) classifier using 196 RDKit-derived molecular descriptors for 477 compounds1 and addressed class imbalance with SMOTE. On a held-out test set, the model achieved ROC-AUC of 0.888 with 66.7% recall and 57.1% precision for the positive class; five-fold cross-validation indicated strong generalization (ROC-AUC 0.974 ± 0.067). Gain-based feature importance highlighted topological complexity, aromaticity, and polarity-related descriptors as salient. The framework enables rapid, cost-effective screening of autoimmune risk during early discovery to prioritize compounds for deeper evaluation.

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