Optimization of Pharmacokinetic Compound Profile of Ligands of Serotonin Receptor 5-HT₇–Application of Machine Learning Methods in Ligand- and Structure-Based Approach ^†

During the search for new active compounds, at first, the focus is put mainly on the provision of compound activity towards considered targets. However, at the same time, or in the subsequent stages, the compound needs to be adequately profiled in terms of its physicochemistry and ADMET properties. Here, we present a tool for optimization of physicochemical and pharmacokinetic properties based on the application of machine learning tools. It considers several compound properties: solubility, metabolic stability, biological membrane permeability, hERG channel blocking, and mutagenicity. Separate models are constructed for each property and the predictive power of the models is verified on the ligands of serotonin receptor 5-HT₇. The models use various fingerprints for compound representation (including interaction fingerprints in the cases, where docking to the target protein can be performed). The results obtained within the study will be used for the design of new serotonin receptor ligands with optimized physicochemical and ADMET profiles.