CoMFA and CoMSIA analysis of 2,4-thiazolidinediones derivatives as aldose reductase inhibitors

Structural modification of 4, 5-dihydro-[1, 2, 4] triazolo [4, 3-f] pteridine derivatives as BRD4 inhibitors using 2D/3D-QSAR and molecular docking analysis

Cancer treatment continues to be one of the most serious public health issues in the world. The overexpression of BRD4 protein has led to a series of malignant tumors, hence the development of small molecule BRD4 protease inhibitors has always been a hot spot in the field of medical research. In this study, a series of 4,5-dihydro-[1, 2, 4] triazolo [4, 3-f] pteridine derivatives were used to establish 3D/2D-QSAR models and to discuss the relationship between inhibitor structure and activity. Four ideal models were established, including the comparative molecular field analysis (CoMFA: [Formula: see text] = 0.574, [Formula: see text] = 0.947) model, comparative molecular similarity index analysis (CoMSIA: [Formula: see text]= 0.622, [Formula: see text] = 0.916) model, topomer CoMFA ([Formula: see text] = 0.691, [Formula: see text]= 0.912) model and hologram quantitative structure-activity relationship (HQSAR: [Formula: see text]= 0.759, [Formula: see text] = 0.963) model. They show quite good external predictive power for the test set, with [Formula: see text] values of 0.602, 0.624, 0.671 and 0.750, respectively. In addition, the contour and color code map given by the 2D/3D-QSAR model with the results of molecular docking analyzed to chalk up modification methods for improving inhibitory activity, which was verified by designing novel compounds. The analysis results are helpful to promote the modification of the inhibitor framework and to provide a reference for the construction of new and promising BRD4 inhibitor compounds.

3D-QSAR and docking studies on adenosine A2A receptor antagonists by the CoMFA method

Parkinson's disease affects millions of people around the world. Recently, adenosine A2A receptor antagonists have been identified as a drug target for the treatment of Parkinson's disease. Consequently, there is an immediate need to develop new classes of A2A receptor antagonists. In the present analysis, three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed on a series of pyrimidines, using comparative molecular field analysis (CoMFA). The best prediction was obtained with a CoMFA standard model (q(2) = 0.475, r(2) = 0.977) and a CoMFA region focusing model (q(2) = 0.637, r(2) = 0.976) combined with steric and electrostatic fields. The structural insights derived from the contour maps helped to better interpret the structure-activity relationships. Also, to understand the structure-activity correlation of A2A receptor antagonists, we have carried out molecular docking analysis. Based on the results obtained from the present 3D-QSAR and docking studies, we have identified some key features for increasing the activity of compounds, which have been used to design new A2A receptor antagonists. The newly designed molecules showed high activity with the obtained models.

Combined 3D-QSAR modeling and molecular docking studies on pyrrole-indolin-2-ones as Aurora A kinase inhibitors

Aurora kinases have emerged as attractive targets for the design of anticancer drugs. 3D-QSAR (comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA)) and Surflex-docking studies were performed on a series of pyrrole-indoline-2-ones as Aurora A inhibitors. The CoMFA and CoMSIA models using 25 inhibitors in the training set gave r(2) (cv) values of 0.726 and 0.566, and r(2) values of 0.972 and 0.984, respectively. The adapted alignment method with the suitable parameters resulted in reliable models. The contour maps produced by the CoMFA and CoMSIA models were employed to rationalize the key structural requirements responsible for the activity. Surflex-docking studies revealed that the sulfo group, secondary amine group on indolin-2-one, and carbonyl of 6,7-dihydro-1H-indol-4(5H)-one groups were significant for binding to the receptor, and some essential features were also identified. Based on the 3D-QSAR and docking results, a set of new molecules with high predicted activities were designed.

Molecular modeling studies on 11H-dibenz[b,e]azepine and dibenz[b,f][1,4]oxazepine derivatives as potent agonists of the human TRPA1 receptor

A computational strategy based on comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) was performed on a series of the 11H-dibenz[b,e]azepine and dibenz[b,f][1,4]oxazepine derivatives as potent agonists of the human TRPA1 receptor. The CoMFA and CoMSIA models resulting from a 21 molecule training set gave r²(cv) values of 0.631 and 0.542 and r² values of 0.986 and 0.981, respectively. The statistically significant models were validated by a test set of five compounds with predictive r²(pred). values of 0.967 and 0.981 for CoMFA and CoMSIA, respectively. A systemic external validation was also performed on the established models. The information obtained from 3D counter maps could facilitate the design of more potent human TRPA1 receptor agonists.

3D-QSAR (CoMFA and CoMSIA) and pharmacophore (GALAHAD) studies on the differential inhibition of aldose reductase by flavonoid compounds

Inhibitory activities of flavonoid derivatives against aldose reductase (AR) enzyme were modelled by using CoMFA, CoMSIA and GALAHAD methods. CoMFA and CoMSIA methods were used for deriving quantitative structure-activity relationship (QSAR) models. All QSAR models were trained with 55 compounds, after which they were evaluated for predictive ability with additional 14 compounds. The best CoMFA model included both steric and electrostatic fields, meanwhile, the best CoMSIA model included steric, hydrophobic and H-bond acceptor fields. These models had a good predictive quality according to both internal and external validation criteria. On the other hand, GALAHAD was used for deriving a 3D pharmacophore model. Twelve active compounds were used for deriving this model. The obtained model included hydrophobe, hydrogen bond acceptor and hydrogen bond donor features; it was able to identify the active AR inhibitors from the remaining compounds. These in silico tools might be useful in the rational design of new AR inhibitors.