SAR Study of 1-Aryl-4-(phenylarylmethyl)piperazines as Ligands for Both Dopamine D2 and Serotonin 5-HT1A Receptors Showing Varying Degrees of (Ant)agonism. Selection of a Potential Atypical Antipsychotic

The crystal structures, Hirshfeld surface analyses and energy frameworks of 8-{1-[3-(cyclo-pent-1-en-1-yl)benz-yl]piperidin-4-yl}-2-meth-oxy-quinoline and 8-{4-[3-(cyclo-pent-1-en-1-yl)benz-yl]piperazin-1-yl}-2-meth-oxy-quinoline

The title compounds, 8-{1-[3-(cyclo-pent-1-en-1-yl)benz-yl]piperidin-4-yl}-2-meth-oxy-quinoline, C27H30N2O (I), and 8-{4-[3-(cyclo-pent-1-en-1-yl)benz-yl]piperazin-1-yl}-2-meth-oxy-quinoline, C26H29N3O (II), differ only in the nature of the central six-membered ring: piperidine in I and piperazine in II. They are isoelectronic (CH cf. N) and isotypic; they both crystallize in the triclinic space group P with very similar unit-cell parameters. Both mol-ecules have a curved shape and very similar conformations. In the biaryl group, the phenyl ring is inclined to the cyclo-pentene mean plane (r.m.s. deviations = 0.089 Å for I and 0.082 Å for II) by 15.83 (9) and 13.82 (6)° in I and II, respectively, and by 67.68 (6) and 69.47 (10)°, respectively, to the mean plane of the quinoline moiety (r.m.s. deviations = 0.034 Å for I and 0.038 Å for II). The piperazine ring in I and the piperidine ring in II have chair conformations. In the crystals of both compounds, mol-ecules are linked by C-H⋯π inter-actions, forming chains in I and ribbons in II, both propagating along the b-axis direction. The principal contributions to the overall Hirshfeld surfaces involve H⋯H contacts at 67.5 and 65.9% for I and II, respectively. The major contribution to the inter-molecular inter-actions in the crystals is from dispersion forces (E dis), reflecting the absence of classical hydrogen bonds.

Crystal structure and Hirshfeld surface analysis of the hydro-chloride salt of 8-{4-[(6-phenyl-pyridin-3-yl)meth-yl]piperazin-1-yl}-3,4-di-hydro-quinolin-2(1H)-one

The amine 8-{4-[(6-phenyl-pyridin-3-yl)meth-yl]piperazin-1-yl}-3,4-di-hydro-quinolin-2(1H)-one was crystallized as the hydro-chloride salt, 4-(2-oxo-1,2,3,4-tetra-hydro-quinolin-8-yl)-1-[(6-phenyl-pyridin-3-yl)meth-yl]piperazin-1-ium chloride, C25H27N4 +·Cl- (I·HCl). The conformation of the organic cation is half-moon in shape enclosing the chloride anion. The piperidine ring of the 3,4-di-hydro-quinolin-2(1H)-one moiety has a screw-boat conformation, while the piperazine ring has a chair conformation. In the biaryl group, the pyridine ring is inclined to the phenyl ring by 40.17 (7) and by 36.86 (8)° to the aromatic ring of the quinoline moiety. In the crystal, the cations are linked by pairwise N-H⋯O hydrogen bonds, forming inversion dimers enclosing an R 2 2(8) ring motif. The Cl- anion is linked to the cation by an N-H⋯Cl hydrogen bond. These units are linked by a series of C-H⋯O, C-H⋯N and C-H⋯Cl hydrogen bonds, forming layers lying parallel to the ab plane.

Copper-catalyzed oxidative intermolecular 1,2-alkylarylation of styrenes with ethers and indoles

A new copper-catalyzed oxidative three-component intermolecular 1,2-alkylarylation of styrenes with ethers and indoles is disclosed.

The Challenges of Multi-Target Lead Optimization

During multi-target lead optimization, medicinal chemists must address three principal challenges. First, the various desired activities must be adequately balanced and the activity profile of any active metabolites must also be considered. Second, undesired activities associated with side effects need to be removed. Third, a pharmacokinetic profile appropriate for the desired route of administration, usually oral, must be obtained. Given the extent of these challenges, the need for a high quality lead compound is paramount.

Classification of 5-HT(1A) receptor agonists and antagonists using GA-SVM method

AIM

To construct a reliable computational model for the classification of agonists and antagonists of 5-HT(1A) receptor.

METHODS

Support vector machine (SVM), a well-known machine learning method, was employed to build a prediction model, and genetic algorithm (GA) was used to select the most relevant descriptors and to optimize two important parameters, C and r of the SVM model. The overall dataset used in this study comprised 284 ligands of the 5-HT(1A) receptor with diverse structures reported in the literatures.

RESULTS

A SVM model was successfully developed that could be used to predict the probability of a ligand being an agonist or antagonist of the 5-HT(1A) receptor. The predictive accuracy for training and test sets was 0.942 and 0.865, respectively. For compounds with probability estimate higher than 0.7, the predictive accuracy of the model for training and test sets was 0.954 and 0.927, respectively. To further validate our model, the receiver operating characteristic (ROC) curve was plotted, and the Area-Under-the-ROC- Curve (AUC) value was calculated to be 0.883 for training set and 0.906 for test set.

CONCLUSION

A reliable SVM model was successfully developed that could effectively distinguish agonists and antagonists among the ligands of the 5-HT(1A) receptor. To our knowledge, this is the first effort for the classification of 5-HT(1A) receptor agonists and antagonists based on a diverse dataset. This method may be used to classify the ligands of other members of the GPCR family.

Principal Component Analysis Differentiates the Receptor Binding Profiles of Three Antipsychotic Drug Candidates from Current Antipsychotic Drugs

The receptor binding affinities of the three drug candidates 1 (SLV310), 2 (SLV313), and 3 (SLV314) were positioned against the results from nine (a)typical antipsychotic drugs. The receptor binding data from sixteen monoaminergic receptors served as the input in a principal component analysis (PCA). The PCA outcome revealed a unique binding profile of 1, 2, and 3 as compared with the reference compounds 4-8 and 10-12. The weight gain inducing antipsychotics 6-8 clustered in the PCA by scoring strongly negative for factor 1. The hyperprolactinaemia related antipsychotics 4, 5, 10, and 12 clustered by their negative scores for factor 2.