## 1 Introduction
In this context, computer-aided drug design (CADD) has emerged as an important strategy for accelerating the identification of bioactive natural compounds, as demonstrated by [PERSON] et al., who highlight its utility in exploring both structure- and ligand-based approaches to drug discovery [32]. The computer-assisted prediction of the biological activity of specific chemical compounds, considering their intricate chemical structures, has become a standard and invaluable technique in the field of drug discovery. This approach not only enhances the understanding of how various compounds interact with biological targets but also streamlines the overall process, significantly reducing the time and costs associated with traditional experimental methods [33; 34]. In silico drug discovery encompasses a range of advanced computer techniques, enabling researchers to identify, optimize, and design potential drug candidates effectively. By utilizing tools such as molecular modeling and CADD, scientists can simulate and predict how new compounds might behave in biological systems [35]. This computational framework is revolutionizing the drug development landscape, allowing for a more targeted approach to finding therapeutic agents that can address complex diseases [36]. The advantages of in silico methods are manifold. They facilitate high-throughput virtual screening, enabling researchers to evaluate thousands of compounds quickly against specific biological targets. Moreover, they assist in the rational design of molecules with enhanced efficacy and reduced toxicity, significantly improving the success rate of drug development [37]. With these tools at their disposal, researchers can also predict potential side effects early in the drug development process, addressing safety concerns before clinical trials begin [38]. This proactive approach not only enhances the efficiency of drug discovery but also raises ethical standards in the field by reducing the reliance on animal testing and experimental approaches that may be harmful to living subjects [39]. Among the various in silico techniques, molecular docking plays a critical role in understanding molecular interactions. This computational method is employed to estimate the strength of the interactions between proteins and ligands, ultimately determining the optimal binding poses and free energy values associated with these interactions [40; 41]. By simulating how a ligand binds to a receptor, molecular docking can elucidate the nature of non-covalent interactions, such as hydrogen bonds, hydrophobic interactions, and ionic bonds, which are crucial for ligand recognition and specificity [42]. This insight is particularly valuable, as it allows scientists to explore the intricate mechanisms of action and binding affinities of new drug candidates, paving the way for innovative therapeutic developments [43; 44]. As the field advances, the integration of machine learning and artificial intelligence into in silico drug discovery is expected to enhance predictive accuracy and model complexity further. These emerging technologies promise to aid in refining docking algorithms and pharmacokinetic predictions, ultimately leading to the discovery of more effective and safer drug candidates [45; 46]. Given the limitations of conventional anti-inflammatory drugs, such as gastrointestinal toxicity and cardiovascular risks, there is a pressing need to explore naturally occurring compounds with safer profiles. Monoterpenes, widely found in essential oils, have shown promising anti-inflammatory activity in preliminary studies, yet their interactions with enzymes in the arachidonic acid pathway remain underexplored. This study employs a blind molecular docking approach to evaluate the inhibitory potential of six monoterenes (\(\alpha\)-pinene, \(\beta\)-pinene, menthol, camphor, limonene, and linalool) against COX-1, COX-2, 5-LOX, and PLA2, aiming to assess their suitability as lead compounds for novel anti-inflammatory agents.