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New arylpiperazine derivatives were prepared to identify highly selective and potent ligands for the 5-hydroxytryptamine 1A (5-HT(1A)) receptor as potential pharmacological tools in studies of central nervous system (CNS) disorders. The combination of structural elements (heterocyclic nucleus, oxyalkyl chain, and arylpiperazine) known to introduce 5-HT(1A) receptor affinity and the proper selection of substituents led to compounds with higher receptor specificity and affinity. In binding studies, several molecules showed affinity in the nanomolar and subnanomolar ranges at 5-HT(1A) and moderate to no affinity for other relevant receptors (5-HT(2A), 5-HT(2C), D(1), D(2), alpha(1), and alpha(2)). The 4-[3-[4-(o-methoxyphenyl)piperazin-1-yl]propoxy]-4-aza-tricyclo[5.2.1.02,6]dec-8-ene-3,5-dione, with K(i) = 0.021 nM, was the most active and selective derivative for the 5-HT(1A) receptor with respect to other serotonin receptors, whereas the most selective derivative for dopaminergic and adrenergic receptors was a CF(3)-substituted arylpiperazine. As a general trend, compounds with a piperazinylpropoxy chain showed a preferential affinity for the 5-HT(1A) receptor, suggesting that the alkyl chain length represents a critical structural feature in determining 5-HT(1A) receptor affinity and selectivity, as confirmed by the molecular modeling invoked for explaining the differential binding affinities of the new arylpiperazines.

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Recently developed multi-targeted ligands are novel drug candidates able to interact with monoamine oxidase A and B; acetylcholinesterase and butyrylcholinesterase; or with histamine N-methyltransferase and histamine H3-receptor (H3R). These proteins are drug targets in the treatment of depression, Alzheimer's disease, obsessive disorders, and Parkinson's disease. A probabilistic method, the Parzen-Rosenblatt window approach, was used to build a "predictor" model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Molecular structures were represented based on the circular fingerprint methodology. The same approach was used to build a "predictor" model from the DrugBank dataset to determine the main pharmacological groups of the compound. The study of off-target interactions is now recognised as crucial to the understanding of both drug action and toxicology. Primary pharmaceutical targets and off-targets for the novel multi-target ligands were examined by use of the developed cheminformatic method. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. The cheminformatic targets identifications were in agreement with four 3D-QSAR (H3R/D1R/D2R/5-HT2aR) models and by in vitro assays for serotonin 5-HT1a and 5-HT2a receptor binding of the most promising ligand (71/MBA-VEG8)

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