Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine-learning approaches.

This work demonstrates how machine learning can enhance the sensitivity/specificity of pyrazinamide resistance prediction in genetics-based clinical microbiology workflows, highlights novel mutations for future biochemical investigation, and is a proof of concept for using this approach in other drugs.