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.