Computer-Aided Discovery of Synergistic Drug–Nanoparticle Combinations for Enhanced Antimicrobial Activity

Antibiotic resistance is a critical global public health challenge driven by the limited discovery of antibiotics, the rapid evolution of resistance mechanisms, and persistent infections that compromise treatment efficacy. Combination therapies using antibiotics and nanoparticles (NPs) offer a promising solution, particularly against multidrug-resistant (MDR) bacteria. This study introduces an innovative approach to identifying synergistic drug–NP combinations with enhanced antimicrobial activity. To carry this out, we compiled two groups of data sets to predict the minimal concentration (MC) and zone of inhibition (ZOI) of various drug–NP combinations. CatBoost regression models achieved the best 10-fold cross-validation R² scores of 0.86 and 0.77, respectively. We then adopted a machine learning (ML)-reinforced genetic algorithm (GA) to identify synergistic antimicrobial NPs. The proposed approach was first validated by reproducing the previous experimental results. As a proof of concept for discovering drug–NP combinations, Au NPs were identified as highly synergistic NPs when paired with chloramphenicol, achieving a minimum bactericidal concentration (MBC) of 71.74 ng/mL against Salmonella typhimurium with a fractional inhibitory concentration index of 6.23 × 10^–3. These findings present an effective strategy for identifying synergistic drug–NP combinations, providing a promising approach to combating drug-resistant pathogens and advancing targeted antimicrobial therapies.