The prediction of drug-induced autoimmunity (DIA) is challenged by the high-dimensionality of molecular descriptor data and the complexity of underlying biological mechanisms. This study presents a machine learning framework to model the relationship between RDKit-derived molecular features and binary autoimmune risk labels. The methodology employs a pipeline involving mutual-information-based feature selection, multi-algorithm training, and randomized hyperparameter optimization, applied to distinct training and independent test sets. A reduced feature subset was constructed, followed by a comparative evaluation of six supervised learning algorithms: Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine, XGBoost, and LightGBM. The model identified through cross-validation yielded the highest performance metrics on external validation. Analysis indicated that key molecular features contributed to model predictions, as evidenced by feature importance rankings. This approach combines computational chemistry descriptors with machine learning to provide a systematic framework for preclinical DIA assessment.

Zhang Yucheng, Zhang Lu, Luo Shunan. Machine Learning-Based Prediction of Drug-Induced Autoimmunity Using Molecular Descriptors. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 1: 41-47. https://doi.org/10.25236/AJCIS.2026.090105.

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