Artificial intelligence is rapidly transforming the search for new antibiotics, offering a scalable solution to the global crisis of antimicrobial resistance. Traditional broad-spectrum antibiotics often disrupt beneficial gut microbiota and accelerate resistance evolution. Researchers are now deploying machine learning to identify targeted therapeutics, predict molecular mechanisms, and design novel compounds with unprecedented precision. In 2023, a team led by microbiologist Jonathan Stokes at McMaster University in Hamilton, Canada, isolated a single novel compound, named enterololin, from a screen of ten thousand bioactive molecules. To determine its mechanism of action, the researchers bypassed traditional biochemical assays and utilized DiffDock, an artificial intelligence platform developed by computer scientist Regina Barzilay at the Massachusetts Institute of Technology. DiffDock predicts how small molecules bind to specific proteins, allowing the team to rapidly validate enterololin's targeted activity against pathogenic Escherichia coli. This collaboration highlights artificial intelligence's growing utility in accelerating pharmaceutical discovery beyond initial compound identification. Barzilay's work in this domain began in 2018 alongside MIT biomedical engineer James Collins. Together, they trained a neural network called Chemprop on thousands of molecules to correlate structural features with microbial growth inhibition. The model successfully identified halicin, a kinase inhibitor effective against tuberculosis, drug-resistant E. coli, and Acinetobacter baumannii. Barzilay emphasizes that artificial intelligence-driven drug discovery must also address the economic disincentives facing traditional pharmaceutical manufacturing by prioritizing easily synthesizable compounds. Data quality remains the foundational constraint in training these models. Molly Bartlett, a chemical informatician at Imperial College London, stresses that algorithmic predictions are only as reliable as their underlying datasets. Her work with the Fleming Initiative focuses on curating chemically diverse, physically accurate data representing both successful antimicrobials and structural failures. Bartlett also notes that generative artificial intelligence tools like Google Gemini have streamlined the coding process, making computational biology more accessible to researchers without extensive software engineering backgrounds. Expanding beyond small molecules, synthetic biologist César de la Fuente at the University of Pennsylvania is leveraging artificial intelligence to explore antimicrobial peptides. His team developed APEX, a neural network that screened over ten million peptides, including sequences derived from extinct species. The approach yielded thousands of candidates with unique mechanisms targeting bacterial cytoplasmic membranes rather than rigid cell walls, potentially circumventing established resistance pathways. Building on this, the team created ApexGO, a generative model that designs entirely synthetic peptides optimized for stability and efficacy. Early testing confirms strong antimicrobial activity in cultured cells and animal models. Despite these advancements, significant hurdles remain. Collaborators like James Collins caution that generative artificial intelligence frequently designs molecules that are chemically unstable or prohibitively expensive to synthesize. Bridging the gap between computational prediction and practical manufacturing requires integrating synthetic chemistry constraints directly into artificial intelligence frameworks. Nevertheless, the convergence of machine learning, structural biology, and targeted data curation is establishing a new paradigm in antimicrobial research, positioning artificial intelligence as an indispensable tool in sustaining global public health defenses.