# _In Silico_ Development of SARS-CoV-2 Non-Covalent Mpro Inhibitors: A Review
## 2 Mpro Inhibitors for SARS-CoV-2
### Computer-Aided Drug Design  
Computer-aided drug design (CADD) has quickly become an essential tool in the battle against SARS-CoV-2, assisting researchers in identifying and optimising inhibitors aimed at the virus's main protease [67]. By integrating structure-based (SBDD) and ligand-based (LBDD) strategies, CADD provides a rational and cost-effective framework that complements traditional discovery approaches [68]. While promising, its value lies in its ability to prioritise and optimise compounds before entering the resource-intensive experimental pipeline. In structure-based approaches, molecular docking, molecular dynamics (MD) simulations, and free energy calculations predict how candidate molecules interact with Mpro [69]. These methods are crucial for estimating the binding affinity of potential inhibitors, providing insights into the strength and stability of drug-target interactions. High-throughput virtual screening (HTVS) accelerates the drug discovery process by enabling the evaluation of large chemical libraries, significantly reducing the time and cost typically associated with experimental testing. More advanced techniques, such as quantum mechanics/molecular mechanics (QM/MM) simulations, offer more profound insights into ligand-protein interactions, thereby enhancing the precision of drug optimisation [70]. On the other hand, ligand-based approaches, including quantitative structure-activity relationship (QSAR) modelling and pharmacophore mapping, utilise known inhibitors to predict new, similarly bioactive compounds [69]. These methods have proven effective in quickly identifying lead candidates, which can be refined into more potent inhibitors. They are crucial in guiding the rational design of novel Mpro inhibitors, ensuring that the compounds exhibit the desired biological activity. The process of drug design, especially when enhancing existing compounds, follows key steps [22]. This structured process optimises the chemical properties of drug candidates, enhancing their efficacy, selectivity, and pharmacokinetic profiles [28]. It begins by identifying a lead compound--such as ML188--and organising these data in databases according to drug-like criteria [16]. Techniques such as X-ray crystallography, NMR, and cryo-EM are used to determine the three-dimensional structure of the target protein [58]. The compounds are then subjected to an ensemble docking approach, where conformers generated during molecular dynamics simulations are utilised to calculate binding free energy, which predicts the strength of drug-target interactions [71]. This process culminates in synthesis, validation, and preclinical trials, which are essential to ensure that the inhibitors are effective in real-world conditions. As drug design continues to evolve, the increasing use of computer screens has amplified the significance of CADD in identifying antiviral agents. For example, the successful discovery of antiviral compounds for diseases like HIV and HCV has spurred interest in targeting Mpro for SARS-CoV-2 treatment [72]. Leveraging advanced computational techniques, researchers have identified repene metabolites from the Red Sea coral reefs as promising Mpro inhibitors [73]. These natural compounds, including flavonoids, alkaloids, and terpenoids, exhibited strong binding affinities to Mpro, highlighting the untapped potential of marine and plant-derived metabolites in COVID-19 therapy. Notable examples include Lopinavir, Depresosterol, and Erylosides B, each offering a unique structural foundation for future drug development. Natural product scaffolds have also inspired innovative approaches to Mpro inhibition. Compounds derived from the aloe plant, such as aloesin and aloesin D, demonstrated inhibitory activity with IC\({}_{50}\) values ranging from 6.8 \(\upmu\)m to 138.0 \(\upmu\)m (Figure 9) [74]. These findings showcase the ongoing efforts to harness the therapeutic potential of natural sources.