Machine Learning Model for Predicting Antibiotic Resistance Patterns from Protein Sequences

Abstract

Antibiotic resistance (AR) has emerged as a pressing global health challenge, undermining the effectiveness of conventional treatment options and threatening public health systems around the world. The rapid identification of resistance genes and their associated mechanisms is therefore critical for the development of diagnostic and therapeutic strategies. This study presents the development of an ensemble machine learning pipeline for predicting resistance gene functions using the Comprehensive Antibiotic Resistance Database (CARD). Protein sequences were extracted from CARD and processed into features using sequence-derived representations, including kmer embeddings and TF-IDF vectorization. An ensemble voting classifier was implemented, combining five base estimators: Extra Trees Classifier, Random Forest, XGBoost, Linear Discriminant Analysis, and K-Nearest Neighbors. The ensemble approach utilized both hard and soft voting strategies, with optimized weights determined through log-loss minimization. The system was evaluated on a curated subset of the CARD dataset, ensuring balanced class representation across 62 antibiotic drug classes. Results demonstrate that the ensemble approach achieves superior classification performance, with the optimized soft voting classifier achieving 89.76% accuracy, 92% precision, 90% recall, and 90% F1-score. The hard voting ensemble achieved 89.44% accuracy with comparable precision and recall metrics. These results represent significant improvements over individual base classifiers, highlighting the effectiveness of ensemble methods for antibiotic resistance prediction. The ensemble approach demonstrates superior performance while maintaining computational efficiency, making it suitable for deployment in resourceconstrained environments.