How Can Structure-Based Computational Methods Support Antibiotic Drug Discovery in Addressing Antimicrobial Resistance?
Antimicrobial resistance (AMR) has emerged as a major global health challenge, necessitating the development of innovative strategies to accelerate antibiotic drug discovery. Traditional drug development approaches are often time-consuming, costly, and resource-intensive, creating a growing need for computational methods that can improve research efficiency and therapeutic candidate identification. This study investigates the role of computational protein structure prediction in advancing antibiotic drug discovery through a conceptual literature review of recent research published between 2022 and 2026. The review examines the application of protein structure prediction techniques, including deep learning, machine learning, molecular simulations, and AlphaFold-based approaches, in supporting structure-based drug design. Particular attention is given to their contributions in drug target identification, binding site prediction, protein–ligand interaction analysis, virtual screening, and candidate prioritization. The findings indicate that computational approaches significantly enhance the efficiency of early-stage drug discovery by enabling accurate structural modeling, rapid screening of large compound libraries, and improved identification of promising antibacterial targets. Furthermore, the integration of artificial intelligence with molecular modeling techniques has strengthened prediction accuracy and facilitated the discovery of novel therapeutic candidates against drug-resistant pathogens. Despite these advancements, challenges related to prediction reliability, biological complexity, computational resource requirements, and dependence on high-quality datasets continue to affect the robustness of current approaches. Additionally, the reviewed studies emphasize the necessity of experimental validation to confirm computational findings and ensure clinical applicability. Overall, the study concludes that computational protein structure prediction plays a critical role in accelerating antibiotic drug discovery and offers substantial potential for addressing antimicrobial resistance through more efficient and data-driven therapeutic development strategies.