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

Files

PredictionofDrugInducedAutoImmunityUsingXGBoostMachineLearning.pdf (308.81 KB)

Links to Files

Permanent Link

http://hdl.handle.net/11603/41759

Collections

UMBC Staff Collection
UMBC Biological Sciences Department
UMBC Information Systems Department
UMBC Music Department
UMBC Student Collection

Author/Creator

Author/Creator ORCID

Date

2025-09-17

Type of Work

conference papers and proceedings
preprints
Text

Department

Program

Citation of Original Publication

Rights

This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.

Subjects

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 compounds[1] 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.