## 4 Examples of Commercial Targeted Libraries
### Protein-Protein-Interaction-Inhibitor-Focused Libraries
Otava offers two different PPI inhibitor libraries. The first was designed using a decision tree algorithm, which effectively separates PPI inhibitors from a group non-PPI inhibitor. The second was obtained using Bayesian modeling based on known PPI inhibitors from the TIMBAL database. The Otava database was filtered using a Bayesian similarity score cutoff and general medicinal chemistry filters (Lipinski, MW cutoff). Otava compound supplier also offers two peptidomimetic libraries, which are divided into \(\alpha\)-helix peptidomimetics and \(\beta\)-turn peptidomimetics. The \(\alpha\)-helical one was assembled using template compounds found in the scientific literature to create training sets for devising models based on different fingerprints such as FCFP6 and ECFP6. The \(\beta\)-turn library was constructed by using pharmacophore screening, where the pharmacophore is based on real \(\beta\)-turns. The second portion of this library was established by using a similarity search of known scaffolds of \(\beta\)-turn peptidomimetics. The ChemDiv library of potential PPI inhibitors contains 210,000 molecules that were collected by using in silico HTS methods that focused on PPI hot spots located on the protein surfaces, while TargetMol provides a small focused library that contains 143 PPI-related compounds with NMR and HPLC supporting analytical data. No further information is provided on how the library was assembled. For each library described, we performed an analysis of the most common molecular descriptors: Molecular Weight (MW), Total Polar Surface Area (TPSA), SlogP, number of hydrogen bond acceptors (HBA), number of hydrogen bond donors (HBD), number of rings present and the number of rotatable bonds (Table 6). Compound retention when passing through each individual filter was assessed, as well (Table 7), with the results for joined filter provided in Figure 10. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Database Name** & **No. of Compounds** & **MW** & **TPSA** & **SlogP** & **HBA** & **HBD** & **No. of Rings** & **No. of Rotatable Bonds** \\ \hline Asinex & 11,439 & 386 \(\pm\) 53 & 70 \(\pm\) 19 & 3.1 \(\pm\) 1.2 & 4.9 \(\pm\) 1.3 & 0.8 \(\pm\) 0.6 & 3.7 \(\pm\) 0.7 & 4.9 \(\pm\) 1.5 \\ Chemdiv & 212,906 & 408 \(\pm\) 61 & 72 \(\pm\) 20 & 3.5 \(\pm\) 1.3 & 5.0 \(\pm\) 1.6 & 0.8 \(\pm\) 0.7 & 3.9 \(\pm\) 0.8 & 5.1 \(\pm\) 1.9 \\ Enamine & 40,640 & 357 \(\pm\) 49 & 70 \(\pm\) 17 & 2.8 \(\pm\) 0.9 & 4.7 \(\pm\) 1.3 & 1 \(\pm\) 0.7 & 3.5 \(\pm\) 0.7 & 4.3 \(\pm\) 1.6 \\ LifeChemicals & 36,426 & 393 \(\pm\) 81 & 77 \(\pm\) 22 & 3.3 \(\pm\) 1.1 & 5.4 \(\pm\) 1.8 & 1.0 \(\pm\) 0.7 & 3.6 \(\pm\) 1.0 & 5.3 \(\pm\) 2.2 \\ Otava & 3849 & 437 \(\pm\) 71 & 86 \(\pm\) 25 & 3.8 \(\pm\) 1.6 & 5.4 \(\pm\) 1.6 & 1.4 \(\pm\) 1.0 & 3.9 \(\pm\) 1.1 & 6.4 \(\pm\) 2.4 \\ SelleckChem & 188 & 472 \(\pm\) 183 & 101 \(\pm\) 66 & 3.6 \(\pm\) 2.3 & 6.3 \(\pm\) 3.7 & 2.1 \(\pm\) 1.7 & 3.8 \(\pm\) 1.5 & 6.2 \(\pm\) 3.9 \\ Jointed PPI & 305,448 & 400 \(\pm\) 65 & 73 \(\pm\) 20 & 3.3 \(\pm\) 1.2 & 5.0 \(\pm\) 1.6 & 0.9 \(\pm\) 0.7 & 3.8 \(\pm\) 0.8 & 5.0 \(\pm\) 1.9 \\ databases & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Analysis of the most important descriptors for the protein–protein-interaction-inhibitor-targeted library.