Abstract. Antibiotic resistance poses a serious threat to global health, fueled by the widespread and often uncontrolled use of antibiotics in both medicine and agriculture. Factors such as inappropriate prescribing, poor compliance with sanitary standards in healthcare settings, and environmental pollution contribute to the emergence and spread of resistant bacterial strains.
Developing new antibiotics is increasingly challenging due to the rapid evolution of resistance and limited discovery of novel compounds. Traditional approaches have largely been exhausted, and although modern technologies such as systems biology and discovery platforms offer potential, they have yet to produce significant breakthroughs.
Artificial intelligence (AI) is emerging as a transformative tool in the development of antibacterial agents. Deep learning algorithms can analyze chemical structures and predict biological activity, as seen in the discovery of halicin, a compound with a novel mechanism that prevents resistance development. AI also facilitates the optimization of treatment regimens through personalized therapy, minimizing side effects.
Additionally, virtual screening expedites drug discovery, reduces costs, and enhances efficiency. Future directions involve improving the transparency and interpretability of AI models to ensure safer clinical applications.
Keywords: primary health care, Center for Best Practices, patronage, universal and progressive model, integrated disease management, patient-centered care, patient satisfaction, screening program