<![CDATA[Purpose: This study aimed to explore the utilization of in silico approaches in medicinal chemistry to address the global challenge of antibiotic resistance through faster, more efficient, and innovative drug discovery methods.. Methodology: A systematic literature review was conducted using the Google Scholar, SINTA, PubMed, and ScienceDirect databases. The selected articles highlight computational techniques such as molecular docking, machine learning, and Perturbation Theory and Machine Learning (PTML) models for the identification, screening, and optimization of novel antibiotic candidates. Results: This review reveals that in silico methods significantly accelerate the discovery of potential antibiotics, facilitate the identification of compounds with novel mechanisms of action, improve ligand–target binding predictions, and support the modulation of resistance-related gene expression. The synergy between computational simulations and experimental validation enhances the reliability and efficiency of drug development pipelines. Conclusions In silico approaches offer effective, rapid, and cost-efficient solutions for the discovery of next-generation antibiotics that play a crucial role in the ongoing battle against antimicrobial resistance. Limitations: Despite their advantages, in silico methods have limitations in accurately predicting pharmacokinetics and toxicity profiles, as well as in modelling protein flexibility and complexity of biological systems. Contribution: This study contributes to medicinal chemistry, pharmaceutical science, and biotechnology by providing an integrative framework that supports innovative and resource-efficient strategies for antibiotic development and resistance mitigation.]]>
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