# _In Silico_ Development of SARS-CoV-2 Non-Covalent Mpro Inhibitors: A Review
## 2 Mpro Inhibitors for SARS-CoV-2
### Computer-Aided Drug Design
(**a**) The crystal structure of Mpro bound to the inhibitor X77 (PDB ID: 6W63) was used for an ultralarge-scale docking-ingen. (**b**) The crystal structure of Mpro in complex with a fragment molecule (compound 4, PDB ID: 5 RF7) was used for docking a focused library designed for fragment elaboration. (**c,d**) Representative compounds identified from molecular docking of a ZINC15 library containing 235 million lead-like molecules. (**e,f**) Representative compounds identified from docking focused libraries derived from fragment elaboration. (**g–j**) Selected inhibitors were further validated through biophysical and biochemical assays, X-ray crystallography, and testing in virus-infected cell models. In contrast to the promise of _in silico_ tools, their overuse without a proper experimental follow-up remains a critical weakness. For example, compounds like C21, S5-27, and pyrimidinedione derivatives exhibited high binding affinities and favourable interaction profiles in docking simulations; only a handful were synthesised and biologically tested. While computational methods like docking and molecular dynamics simulations provide invaluable insights, experimental validation remains crucial. Recent studies have integrated both approaches, validating computational predictions of binding affinity and molecular interactions through synthesis and testing. For example, [PERSON] et al. used molecular docking to predict the binding of a compound to the 3 CLpro protease, confirming their predictions through co-crystallisation experiments and revealing a novel binding pocket [80]. Similarly, [PERSON] et al. validated their computational predictions by synthesising and testing CCF0058981, which demonstrated potent antiviral activity in cell-based assays [16]. Virtual screening has also proven effective in identifying preclinical compounds like MG-132, which exhibited sub-micromolar antiviral activity in Vero cells [81]. Integrating virtual screening with experimental assays offers a powerful strategy for rapidly identifying potential antiviral agents. In parallel, the screening of FDA-approved drugs has yielded promising compounds, such as dipyridamole and esbelen, which demonstrated antiviral activity against Mpro in vitro [60; 82]. The development of triazole-based inhibitors like ensitrelvir exemplifies the successful integration of computational and experimental methods. After virtual screening and X-ray validation, ensitrelvir entered clinical trials and became the first non-covalent Mpro inhibitor approved [77]. Although compounds like GC376 show potent inhibition of SARS-CoV-2 Mpro in vitro, the in vivo efficacy remains a challenge [83; 84]. While these successes are promising, challenges persist in computational drug discovery. False positives in virtual screening, inaccuracies in docking score predictions, and the need for extensive experimental validation remain significant hurdles [77]. However, by merging computational predictions with rigorous experimental testing, this integrated approach enhances the likelihood of developing effective therapies that can successfully transition from in vitro hits to in vivo candidates. Thus, traditional drug discovery approaches are often slow and resource-intensive, while _in silico_ docking analysis offers a quicker, more cost-effective alternative for large-scale molecular docking screening supported by molecular dynamics simulations. This method has been particularly effective in identifying potential inhibitors for SARS-CoV-2 Mpro by focusing on inhibitors previously reported for SARS-Mpro [85].