## 4 Examples of Commercial Targeted Libraries
### Protein-Protein-Interaction-Inhibitor-Focused Libraries  
Protein-protein interactions are associated with several crucial biological processes, such as cell growth, proliferation and differentiation. The finding of protein-protein inhibitors represents a complex process due to several factors, including a large interaction surface devoid of clefts and binding pockets. Both large and small molecules have been developed to facilitate this inhibition [89; 90]. A filter dubbed the rule of four has been specifically developed for discovering protein-protein inhibitors [69]. We described these targeted libraries mainly with the SARS-CoV-2 Sprot-ACE2 interaction in mind. The Selleckchem library consists of 188 protein-protein interaction (PPI) inhibitors, which target a vast array of different proteins from epigenetic targets to serine proteases. The library was constructed by filtering the literature on known PPI inhibitors. Certain compounds in the library have been FDA approved, and all of them are NMR and HPLC validated. At the time of writing the site reports 132 publications citing this library. Enamine compound supplier offers a PPI inhibitor library designed by inspecting the available structural data of 20 different protein-protein complexes involved in PPI. Ligand and structure based in silico screening was performed to obtain the 40640 compounds available. The final selection of compounds was based on specific recognition patterns (hot-spot analysis, key amino acids, secondary/ tertiary structures), lead-like properties and sp3-rich motifs, passing different rule-based filters such as PAINS and synthetic availability. Asinex provides two different libraries with PPI inhibitors, one with non-macrocyclic compounds and another, which contains small molecules and macrocyclic compounds. The non-macrocyclic library is based upon scaffolds obtained from several \(\alpha\)-helix mimetics, that were proven useful for targeting PPIs. Moreover, four pharmacophore models were created based on the available PPI inhibitor structural data and used to determine 3 key-recognition features. This was applied for molecular docking of the \(\alpha\)-helix mimetic scaffolds and for the final selection of the molecules in the library. The second library contains molecules that mimic protein structural properties such as \(\beta\)-turn and \(\alpha\)-helix, and was assembled using similarity search. LifeChemicals provide four non-overlapping libraries built with different approaches. The first library was devised using machine learning decision trees. By comparing several known PPI inhibitors to non-PPI inhibitors,  
Figure 9: Compound (relative) retention after post-filtering for REOS, PAINS, Aggregators and the Lipinski rule of 5 (one rule break allowed) on protease-inhibitor-targeted libraries. several descriptors facilitated the model to differentiate between the two. Molecules that fit the descriptors of PPI inhibitors were collected from the entire LifeChemicals HTS compound collection after which medicinal chemistry filters were applied to filter out toxic, reactive and PAINS molecules. The final library consists of 6800 molecules. The second library was constructed using similarity search on the LifeChemicals HTS-library toward a reference set of molecules from three different protein-protein interaction libraries (Timbal, 2P2I and iPPI databases). The similarity search was conducted using the Tanimoto coefficient with the threshold at 85%, the obtained molecules were checked for reactive and inactive compounds. The third library was based upon the rule of four proposed by [PERSON] [69] which states that if the molecules abide several structural rules they belong to the desired chemical space. The fourth and final library was built upon 2D similarity search against molecules from reference databases (Pubmed, ChEMBL) where PPI were confirmed with related assays. The entire LifeChemicals HTS library was filtered using a Tanimoto similarity cutoff of 80% and general medicinal chemistry filters providing the final 14,400 molecules.