Molecular modeling studies of novel naphthyridine and isoquinoline derivatives as CDK8 inhibitors.

Cell cycle is an important part of cellular activities. The selective inhibition of cyclin-dependent kinases (CDK) activity in tumor cells can lead to continuous cell proliferation. Thirty-nine CDK8 inhibitors were systematically investigated on the basis of a three-dimensional quantitative structure-activity relationship (3D-QSAR). Models for comparative molecular field analysis (q2=0.64, r2=0.98) and comparative molecular similarity index analysis (q2=0.609, r2=0.952) were obtained. Contour maps illustrated that bioactivity of inhibitors is most affected by steric, electrostatic, hydrogen bond donor, and receptor interactions of molecular groups. Twenty new CDK8 inhibitors (DS01-DS20) were designed based on the contour maps. The results of ADME prediction illustrated that the designed compounds had potential druggability. The binding mode between a ligand and receptor was explored through molecular docking and molecular dynamics. Results revealed that the hydrogen bond interaction with residue LYS52 remarkably affected the activity of these compounds. Further analysis indicated that the introduction of fluorine to an amino naphthyridine ring of compound 28 contributes to the improvement of molecular activities. Pharmacophore-based virtual screening and Surflex-Sim in the ZINC database of 1,30,000 molecules demonstrated that 14 compounds with an indazole ring might be antitumor inhibitors. 3D-QSAR, molecular docking, molecular dynamics and pharmacophore results are consistent. These findings can be used as a reference for the design and discovery of new CDK8 inhibitors that can reduce design errors.Communicated by Ramaswamy H. Sarma.

Molecular modeling studies of benzothiophene-containing derivatives as promising selective estrogen receptor downregulators: a combination of 3D-QSAR, molecular docking and molecular dynamics simulations.

Selective estrogen receptor downregulators (SERDs) for the treatment of positive breast cancer can act both as estrogen alpha receptor (ERα) antagonists and degraders. In this study, the optimal antagonist models (CoMFA-A, q2 = 0.660, r2 = 0.996; CoMSIA-A, q2 = 0.728, r2 = 0.992) and degrader models (CoMFA-D, q2 = 0.850, r2 = 0.996; CoMSIA-D, q2 = 0.719, r2 = 0.995) of a series of potent benzothiophene-containing SERDs were constructed to explore the three-dimensional quantitative structure-activity relationship. Internal and external validation indicated that all models exhibited good applicability, high predictive ability and robustness. Contour maps revealed the relationships between the essential structural features and antagonistic and degradation activities. Additionally, molecular docking, molecular dynamics and free energy calculation studies were further performed to investigate the detailed binding mode. Results indicated that several key residues, ARG394, GLU353, PHE404 and ILE424, were crucial for the stability of the ligand binding domain. The hydrophobic, electrostatic and Van der Waals interactions played significant effect on the binding affinity. Finally, ten novel compounds were designed based on above findings, where the predicted activity of compound D8 was equivalent to that of the compound LSZ102. 3D-QSAR, ADMET and bioavailability predictions indicated that all designed compounds with good predicted activity, good physicochemical and bioavailability could be potential candidates for SERDs. These results and combinations of computational methods provided guidance for the rational drug design of novel potential SERDs.Communicated by Ramaswamy H. Sarma.

Design of novel dopamine D2 and serotonin 5-HT2A receptors dual antagonists toward schizophrenia: An integrated study with QSAR, molecular docking, virtual screening and molecular dynamics simulations.

The extrapyramidal side effects of schizophrenia treatment can be significantly reduced by simultaneously targeting dopamine D2 and serotonin 5-HT2A receptors. In this study, three-dimensional quantitative structure-activity relationship (3D-QSAR) models of D2 receptor (CoMFA-1, q2 = 0.767, r2 = 0.969; CoMSIA-1, q2 = 0.717, r2 = 0.978) and 5-HT2A receptor antagonists (CoMFA-2, q2 = 0.703, r2 = 0.946; CoMSIA-2, q2 = 0.675, r2 = 0.916) were successfully constructed using 35 tetrahydropyridopyrimidinone derivatives. Topomer CoMFA and HQSAR models were then constructed to further validate and supplement above models. Results showed that all models had good predictive power and stability. Contour map analysis revealed that the electrostatic and hydrophobic fields played vital roles in the bioactivity of dual antagonists. Molecular docking and molecular dynamic studies also suggested that the hydrogen bonding, electrostatic and hydrophobic interactions played key roles in the formation of stable binding sites. Meanwhile, several key residues like ASP114, TRP100, PHE389 of dopamine D2 receptor and ASP134, PHE328, TRP324 of serotonin 5-HT2A receptor were identified. Based on above findings, seven compounds were obtained through bioisostere replacement and ten compounds were designed by contour map analysis, in which the predicted activity of compounds S6 and DS2 were equivalent to that of the template compound 15. 3D-QSAR and ADMET predictions indicated that all newly designed compounds had great biological activity and physicochemical properties. Moreover, based on the best pharmacophore model, four compounds (Z1, Z2, Z3 and Z4) with new backbones were obtained by virtual screening. Overall, this study could provide theoretical guidance for the structural optimization, design and synthesis of novel dopamine D2 and serotonin 5-HT2A receptors dual antagonists. Abbreviations3D-QSARThree-dimensional quantitative structure-activity relationship5-HT2ARSerotonin 5-hydroxytryptamine 5-HT2A receptor5-HT2CRSerotonin 5-hydroxytryptamine 5-HT2C receptor receptorCADDComputer-aided drug designCoMFAComparative molecular field analysisCoMSIAComparative molecular similarity index analysisD2RDopamine D(2) receptorGPCRG-protein coupled receptorPLSPartial least squares regressionHQSARHologram quantitative structure-activity relationship. Communicated by Ramaswamy H. Sarma.