Artificial Intelligence–Driven Molecular Docking for Selection of Antibacterial Agents in Periodontitis Therapy: From Pathophysiology to Target Validation

Abstract

Periodontitis is a chronic inflammatory disease that leads to destruction of the supporting structures of teeth and eventually tooth loss. The disease is initiated by pathogenic bacteria and modified by host immune responses. Although mechanical cleaning remains the foundation of therapy, antibiotics are often required in moderate to severe cases. However, empirical drug selection, increasing resistance, and variable patient response limit treatment success. Artificial intelligence (AI) and molecular docking have recently emerged as powerful tools for improving precision in antimicrobial selection. These technologies allow prediction of drug–target interactions, screening of potential molecules, and optimization of therapy before clinical use. This review discusses the pathophysiology of periodontitis, the role of antibiotics such as minocycline and clindamycin, and the growing importance of AI-assisted molecular docking in periodontal drug discovery and validation.