![]() A subselection of 32 candidates was synthesized and subsequently tested using an impedance-based assay. This model was applied to the extensive virtual compound database of Enamine, from which the top predicted 22,000 of the 600 million virtual compounds were clustered to end up with 46 chemically diverse candidates. The final model was created using a two-step approach combining several proteochemometric machine learning models through stacking. After optimizing the data set, the proteochemometric model was optimized using stepwise feature selection. Prior proteochemometric models added data from related proteins, but here we use a data-driven approach to select the optimal proteins to add to the modeled data set. ![]() The approach increases the chemical space to model for NETs using the chemical space of related proteins that were selected utilizing similarity networks. Here, a computational screening pipeline was developed to find new NET inhibitors. ![]() However, the chemical space of known ligands has a low chemical diversity, making it challenging to identify chemically novel ligands. In contrast to most other SLCs, the NET has been relatively well studied. One such SLC is the high-affinity norepinephrine transporter (NET/SLC6A2). However, proteins from the SLC family play an essential role in various diseases. Solute carriers (SLCs) are relatively underexplored compared to other prominent protein families such as kinases and G protein-coupled receptors.
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