AI-guided precise design of antimicrobial polymers through high-throughput screening technology on an automated platform

Antimicrobial peptides (AMPs)-mimicking antimicrobial polymers show great potential as therapeutic alternatives to antibiotics in the looming "post-antibiotic era". However, the discovery of new AMP-mimicking antimicrobial polymers is challenging due to the vast chemical space of side-chain combinations. The advancement of AI-guided high-throughput screening enables more efficient, precise, and intelligent material design. Herein, we integrate combinatorial chemistry, machine learning, and automated high-throughput synthesis and characterization platforms to establish a new paradigm for the design of antimicrobial polymers with excellent biocompatibility. Starting with a library of 13,728 combinations, a seed dataset of 400 structures is generated, followed by four Design-Build-Test-Learn iterations using a new machine learning model. 7 top-performing candidates are screened with a minimum inhibitory concentration (MIC) ≤ 8 μg/mL and an inhibitory concentration causing 20 % cell death (IC₂₀) ≥ 64 μg/mL. The highest-performing polymer (MIC 2 μg/mL, IC₂₀ 256 μg/mL) shows similar in vivo therapeutic efficacy with ceftazidime. Overall, the integration of AI-guided high-throughput screening and combinatorial chemistry accelerates the discovery of new antimicrobial polymers, which provides a scalable strategy for developing novel antimicrobial agents.