## 5 Discussion  
Focusing on commercial availability, one immediately recognizes the abundance of options offered, especially when considering a specific area, such as SARS-CoV-2-targeted libraries and protease-inhibitor-focused or protein-protein-interaction-inhibitor-focused libraries. These libraries provide ready-to-use compounds, and, surprisingly, most commercial vendors also offer the necessary analytical support. Looking at the distribution of molecular mass in these commercial libraries, we can observe a strict focus on drug-like and not lead-like properties. All commercial libraries have the majority of compounds with a Molecular Weight of around 500 g/mol, as shown in Figures 8 and 11. The libraries also follow the misconception of \"drug-likeness\", as described by the [PERSON]'s rule of five (the rule focuses on oral bioavailability), instead of adopting approaches that would incorporate biological contexts in the form of pharmacodynamics on the focused targets and possibly even pharmacokinetic potentials of designed libraries [47]. Figure 9 displays that no library has compounds with average SlogP values above 3.71 and all libraries follow the rule-of-five guidelines in terms of the number of hydrogen bond donors and acceptors. Interestingly, however, the filters used were not exactly those described in the scientific literature, as Figure 10 depicts compound attrition ranging from 56 to 80% for the REOS, PAINS and Aggregator filters (one-break rule) and from 0.06 to 21% for the Lipinski rule-of-five filter. The attrition rates of each filter are reported in Table 2, with the REOS and Aggregator filters responsible for the highest attrition. Nevertheless, these observations \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Asinex** & **Chemdiv** & **Enamine** & **LifeChemicals** & **Otava** & **SelleckChem** \\ \hline Unfiltered & 11,439 & 212,906 & 40,640 & 36,426 & 3849 & \(188\) \\ \hline Isolated filter: & & & Number of filtered out compounds (\%) & & \\ \hline REOS & 148(1\%) & 12,546(6\%) & 970(2\%) & 3191(9\%) & 686(18\%) & 87(47\%) \\ PAINS & 18(-0\%) & 3891(2\%) & 239(1\%) & 547(2\%) & 164(4\%) & 22(12\%) \\ Aggregators & 6579(58\%) & 132,015(62\%) & 17,499(43\%) & 22,721(38\%) & 2682(70\%) & 126(67\%) \\ Lipinski Ro5 & 26(-0\%) & 5936(3\%) & 16(-0\%) & 814(89\%) & 424(11\%) & 47(25\%) \\ All filters & 8137(71\%) & 174,713(82\%) & 28,386(70\%) & 31,886(88\%) & 3581(93\%) & 177(94\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Analysis of the attrition rate of various filters for the protein–protein interaction inhibitor library. Figure 10: Compound (relative) retention after post-filtering for REOS, PAINS, Aggregators and the Lipinski rule of 5 (one rule break allowed) on protein–protein-interaction-inhibitor-targeted libraries. are only focused on the chemical space, since excessive filtering in such small libraries (2000-20,000 compounds) can cause potentially active chemicals to drop out. The main problem with the libraries discussed, in our opinion, is that vendors lack the information on library design, as well as the references to the primary literature. Commercial vendors consistently provide only general information about the screening protocols used to design the target libraries offered and are even less informative about the actual filters used. No references to actual actives are provided when using ligand-based design, with the exception of marketed libraries of known actives that can be referenced post-purchase by the client through databases such as ChEMBL. For structure-based approaches, usually only target or protein classes or a general panel of targets are provided, with reference to methods such as molecular docking. No precise docked receptors or PDB IDs of the targets are available, and no docking protocols or even references to the molecular docking software (or other HTVS software) are provided.