Potentials of Machine Learning in Predicting Key Features of Synthetic Antimicrobial Polymers.

As the global rise in antimicrobial resistance calls for new therapeutic strategies, synthetic antimicrobial polymers (SAMPs) have emerged as promising alternatives to host-defense peptides, offering tunable structures and reduced limitations. In this work, we employed machine learning (ML) approaches to elucidate the structure-activity relationships of a library of polyacrylamides systematically varied in (1) amine side-chain chemistry, (2) chain length, (3) cationic amine ratio, and (4) polymer architecture. The library consisted of 23 different polymer designs, 3 of which exhibited low minimum inhibitory concentrations (MIC) against different bacterial strains, and 5 of which caused low red blood cells agglutination. Among the evaluated ML algorithms, regression random forest and gradient boosting regression consistently reproduced feature importance and maintained stable decision-tree structures, with gradient boosting outperforming random forest in predictive power. Gradient boosting achieved RSME values of 20, 6, 13 and 12 μg/ml, respectively, for each modelled MIC of 4 bacterial strains: