Bioactivity-Driven Prediction of Antibacterial Synergy Using Machine Learning Models

Abstract excerpt

<h4>Motivation</h4> Predicting antibacterial drug synergy remains difficult due to strain variability and the limited scale of experimentally tested combinations. Existing machine-learning approaches often rely on permissive cross-validation schemes that allow drug pairs to appear across folds, inflating performance. A rigorous evaluation framework and scalable feature representation are needed for robust general...

Topics

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• Adenosine Triphosphate
• Anti-Bacterial Agents
• Antibiotics Pharmacokinetics and Efficacy
• Bacterial Identification and Susceptibility Testing
• Drug Interactions
• Drug Therapy, Combination
• Gene Deletion
• Urinary Tract Infections Management

Identifiers and source

Literature Corpus work

93b0d370-a7f9-5212-9c9e-f655208b39d7

DOI

10.64898/2025.12.14.694088