Since the onset of the COVID-19 pandemic, the scientific community has focused on studying the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that causes the disease and on developing therapies and vaccines, several of which have been developed in record time. In the pharmacological field, no drugs have yet been definitively approved to inhibit the replication of SARS-CoV-2 and stop the development of COVID-19. Several targets are being studied, including the main protease (M-pro), which plays an essential role in virus replication [1]. This protease and the papain-like protease cleave the pp1a and pp1ab polyproteins to produce several nonstructural proteins, including M-pro itself, required for virus replication and transcription [1]. The high conservation of M-pro among related viruses, the importance of M-pro in the replication of the virus and the fact that M-pro only exists in coronaviruses and not in humans makes it an attractive target for the development of antiviral drugs [2,3,4]. SARS-CoV-2 M-pro has 306 amino acids that form three domains (I, II and III) [4]. The M-pro binding site is located between domains I and II, and domain III is involved in dimerization, which is essential for M-pro activity [4]. Similar to the M-pro from SARS-CoV-1 and other coronaviruses, SARS-CoV-2 M-pro has two catalytic amino acids, His41 and Cys145 (Figure 1). A catalytic water molecule is also important and makes a strong hydrogen bond with His41 [5]. Although some allosteric binding sites have been identified for the SARS-CoV-2 M-pro [6,7,8,9], most of the inhibitors crystallized within the M-pro bind to the active site [10]. One strategy used to find SARS-CoV-2 M-pro inhibitors, especially at the beginning of the pandemic, was drug repositioning [2,11,12,13]. This strategy is based on looking for drugs approved for one disease (therefore, its safety and possible adverse effects are known) that can be used to treat another—in this case, COVID-19. One of the most widely used computational tools for repositioning drugs, or looking for compounds with new activities, is what is known as protein-ligand docking. This tool predicts whether a particular molecule can bind (and, if it can, how) to a particular target (for example, the SARS-CoV-2 M-pro [14]). However, protein-ligand docking has several limitations, such as the consideration of the protein as a rigid body and the lack of confidence in the ability of scoring functions to give accurate binding energies [15,16]. In addition, the flexibility of the SARS-CoV-2 M-pro makes it a challenging target for small-molecule inhibitor design [17]. Using two different SARS-CoV-2 M-pro structures and five protein-ligand docking methods, we have recently shown that docking scores or the Gibbs free energy (∆G) calculated with an MM-GBSA method [18] do not correlate with bioactivity [19], probably because of the inability of common docking programs to correctly reproduce the binding modes of SARS-CoV-2 M-pro inhibitors [20]. This reinforces the idea that it is essential to validate the results obtained by protein-ligand docking or any other computational tool, especially when analyzing SARS-CoV-2 M-pro inhibitors [19,21,22,23]. The results of protein-ligand docking can be computationally validated by re-docking, cross-docking and applying the same protocol to a set of known active compounds and a set of decoy or inactive compounds [19]. Protein-ligand docking is expected to discriminate decoys from active compounds. If docking scores are used to rank the potency of a set of compounds, it must first be demonstrated that there is a correlation between docking scores and activity or potency, for example, expressed as IC₅₀ values [19]. However, the best way to validate the predictions of protein-ligand docking is to experimentally test the predicted bioactivity of selected hits.

Table 1 shows the origin of the non-redundant set of 1765 SARS-CoV-2 M-pro inhibitors collected between January 2020 and August 2021 (see supplementary file S1). This set of inhibitors includes only those compounds whose inhibitory capacity, mainly expressed as the IC₅₀ value, against M-pro from SARS-CoV-2 has been determined. A total of 758 compounds were extracted from peer-reviewed articles published between January 2020 and August 2021. When multiple IC₅₀ values were found for the same compound, the mean value was calculated. From a set of 1037 M-pro inhibitors with an IC₅₀ value downloaded from the COVID Moonshot project [39,40] on 1st October 2021, the compounds that had already been collected from the bibliographic search were discarded. In the end, 999 compounds were collected from COVID Moonshot. The IC₅₀ values of these compounds were estimated as the mean value of the IC₅₀ values from two biochemical assays: a fluorescence-based assay and a RapidFire Mass Spectrometry assay. Finally, 8 compounds were collected from the ChEMBL database [41], which contained more SARS-CoV-2 M-pro inhibitors, but most of them had already been collected from the bibliography. The SMILES of the 1765 SARS-CoV-2 M-pro inhibitors were standardized with the Standardizer 21.15.0 program from ChemAxon (http://www.chemaxon.com, accessed on 4 September 2021). The pIC₅₀ values of the SARS-CoV-2 M-pro inhibitors collected range from 2.5 to 9.0 (Table 1). Putative covalent inhibitors were identified by the presence of typical covalent warheads (Table 2). When one of these warheads is in the appropriate position within the M-pro binding site, it can form a covalent bond, usually with the catalytic Cys145 [25]. There are twice as many non-covalent inhibitors as putative covalent inhibitors (Table 1), although pIC₅₀ values are highest in some putative covalent inhibitors (Table 1 and Figure 2). However, conventional IC₅₀ measurements are of limited value for characterizing the potency of irreversible covalent inhibitors, because incubation for different periods of time would provide different IC₅₀ values [42]. Other parameters, such as molecular weight, LogP, number of hydrogen bond donors and hydrogen bond acceptors were similar between the covalent and non-covalent sets.