# _In Silico_ Development of SARS-CoV-2 Non-Covalent Mpro Inhibitors: A Review
## 2 Mpro Inhibitors for SARS-CoV-2
### Computer-Aided Drug Design
Similarly, Table 4 reported a group of bioactive compounds from medicinal plants, such as N3, hesperidin, rutin, diosmin, and apiin, with their binding affinities exceeding the control compound Nelfinavir of \(-147\)[75]. These discoveries highlight the promise of plant-derived molecules as a valuable resource for designing effective inhibitors. However, studies reporting IC\({}_{50}\) values without correlating them with therapeutic indices or bioavailability parameters risk overestimating their drug-like potential. Despite these promising results, the transition from computational predictions to experimental validation remains critical. A recent _in silico_ study investigated two virtual screening methods using ultra-large chemical databases to identify Mpro inhibitors. One method utilised structure-based docking on a library of over 235 million compounds, while the other employed a fragment-based approach [51]. As represented in Figure 10, both methods identified distinct groups of inhibitors with promising antiviral effects, confirmed through crystallography and cellular models. This underscores the value of large-scale screening in identifying potent inhibitors quickly and effectively. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Compound** & **MolDock Score** & **HBond Score** \\ \hline
**N3 (Reference)** & \(-162.17\) & \(-8.19\) \\ \hline
**Nelfinavir** & \(-147.38\) & \(-6.87\) \\ \hline Hesperidin & \(-178.59\) & \(-20.26\) \\ \hline Rutin & \(-176.27\) & \(-21.24\) \\ \hline Diosmin & \(-174.13\) & \(-27.26\) \\ \hline Apigenin & \(-171.01\) & \(-10.19\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the docking of some phenolic compounds on the crystal structure of COVID-19 main protease, including N3, Nelfinavir, Hesperidin, Rutin, Diosmin, and Apiin [75]. Figure 9: Aloesin and Aloesin D binding modes [74]. The catalytic dyad of Cys 145 and His 41 are displayed in green and orange respectively. Fragment-based drug discovery has also yielded valuable insights. For instance, NMR-based screening identified the uracil-containing fragment Z604, which showed time-dependent binding to Mpro [76]. By expanding Z604 through fragment-growing techniques, researchers developed a compound that occupies all four active site subpockets, offering a blueprint for dual-inhibitor drug design. Yet, while this method excels at identifying minimal binding elements, it requires careful balancing of binding potency and drug-like properties during elaboration--a challenge often overlooked in computational-only studies. On the other hand, docking-based screenings have identified other promising inhibitors, such as pyrimidinedione derivatives, which were further optimised through medicinal chemistry and X-ray crystallography [77]. Docking provides crucial insights into how small molecules bind to the active site of target proteins, offering a foundation for lead identification and optimisation [78]. This process identified compounds such as C21, demonstrating strong binding potential through multiple hydrogen bonds and hydrophobic interactions. This discovery highlights the critical role of computational methods in predicting and optimising drug interactions. The discovery of nonpeptide, noncovalent SARS-CoV-2 Mpro inhibitors, such as Mei derivatives, further illustrates the power of CADD. By employing advanced docking techniques and validating these compounds through re-docking studies, researchers identified essential residues involved in binding, such as Cys145 and Glu166 [79]. This information guided the optimisation of more potent derivatives, resulting in compounds like S5-27 and S5-28 (Figure 11), which exhibited improved binding affinities and inhibitory activities with IC\({}_{50}\) values of 1.09 and 1.35 \(\upmu\)m, respectively. Figure 10: Representation of the two virtual screening approaches: ultra-large docking and fragment elaboration [51]. Licenced under the CC-BY licence.