Identifying Structure–Activity Relationships for Cyanine-Derived Antibiotics Using Machine Learning and Commercial Large Language Models

Understanding the structure-activity relationship (SAR) of antibiotic scaffolds is crucial for the development of antibiotics to counter the growing crisis of antimicrobial resistant bacteria. However, an overwhelming space of structural features impairs a comprehensive understanding of the mechanism of action for potential antibiotic candidates. In this study, antibacterial data of a set of newly synthesized cyanine molecules are analyzed with both traditional machine learning (ML) and commercially available large language models (LLMs) to elucidate the SAR. Some LLMs, particularly Grok-3 Think and ChatGPT o1, outperform the traditional ML classifiers, and both approaches highlight positive charges and lipophilicity as key properties for effective cyanine antibiotics.