In silico approaches to develop new phenyl-pyrimidines as glycogen synthase kinase 3 (GSK-3) inhibitors with halogen-bonding capabilities: 3D-QSAR CoMFA/CoMSIA, molecular docking and molecular dynamics studies.

Glycogen synthase kinase 3 (GSK-3) is involved in different diseases, such as manic-depressive illness, Alzheimer's disease and cancer. Studies have shown that insulin inhibits GSK-3 to keep glycogen synthase active. Inhibiting GSK-3 may have an indirect pro-insulin effect by favouring glycogen synthesis. Therefore, the development of GSK-3 inhibitors can be a useful alternative for the treatment of type II diabetes. Aminopyrimidine derivatives already proved to be interesting GSK-3 inhibitors. In the current study, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) have been performed on a series of 122 aminopyrimidine derivatives in order to generate a robust model for the rational design of new compounds with promising antidiabetic activity. The q2 values obtained for the best CoMFA and CoMSIA models have been 0.563 and 0.598, respectively. In addition, the r2 values have been 0.823 and 0.925 for CoMFA and CoMSIA, respectively. The models were statistically validated, and from the contour maps analysis, a proposal of 10 new compounds has been generated, with predicted pIC50 higher than 9. The final contribution of our work is that: (a) we provide an extensive structure-activity relationship for GSK-3 inhibitory pyrimidines; and (b) these models may speed up the discovery of GSK-3 inhibitors based on the aminopyrimidine scaffold. Finally, we carried out docking and molecular dynamics studies of the two best candidates, which were shown to establish halogen-bond interactions with the enzyme.Communicated by Ramaswamy H. Sarma.

Coumarin-Resveratrol-Inspired Hybrids as Monoamine Oxidase B Inhibitors: 3-Phenylcoumarin versus trans-6-Styrylcoumarin.

Monoamine oxidases (MAOs) are attractive targets in drug design. The inhibition of one of the isoforms (A or B) is responsible for modulating the levels of different neurotransmitters in the central nervous system, as well as the production of reactive oxygen species. Molecules that act selectively on one of the MAO isoforms have been studied deeply, and coumarin has been described as a promising scaffold. In the current manuscript we describe a comparative study between 3-phenylcoumarin (endo coumarin-resveratrol-inspired hybrid) and trans-6-styrylcoumarin (exo coumarin-resveratrol-inspired hybrid). Crystallographic structures of both compounds were obtained and analyzed. 3D-QSAR models, in particular CoMFA and CoMSIA, docking simulations and molecular dynamics simulations have been performed to support and better understand the interaction of these molecules with both MAO isoforms. Both molecules proved to inhibit MAO-B, with trans-6-styrylcoumarin being 107 times more active than 3-phenylcoumarin, and 267 times more active than trans-resveratrol.

3-Arylcoumarins as highly potent and selective monoamine oxidase B inhibitors: Which chemical features matter?

Monoamine oxidase B inhibitory activity is closely regulated by the interaction of the small molecules with the enzyme. It is therefore desirable to use theoretical approaches to design rational methods to develop new molecules to modulate specific interactions with the protein. Here, we report such methods, and we illustrate their successful implementation by studying six synthetized 3-arylcoumarins (71-76) based on them. Monoamine oxidase B inhibition is essential to maintain the balance of dopamine, and one of its major functions is to combat dopamine degradation, a phenomenon linked to Parkinson's disease. In this work, we study small-molecule inhibitors based on the 3-arylcoumarin scaffold and their monoamine oxidase B selective inhibition. We show that 3D-QSAR models, in particular CoMFA and CoMSIA, and molecular docking approaches, enhance the probability to find new interesting inhibitors, avoiding very costly and time-consuming synthesis and biological evaluations.

Artificial Intelligence Applied to Flavonoid Data in Food Matrices.

Increasing interest in constituents and dietary supplements has created the need for more efficient use of this information in nutrition-related fields. The present work aims to obtain optimal models to predict the total antioxidant properties of food matrices, using available information on the amount and class of flavonoids present in vegetables. A new dataset using databases that collect the flavonoid content of selected foods has been created. Structural information was obtained using a structural-topological approach called TOPological Sub-Structural Molecular (TOPSMODE). Different artificial intelligence algorithms were applied, including Machine Learning (ML) methods. The study allowed us to demonstrate the effectiveness of the models using structural-topological characteristics of dietary flavonoids. The proposed models can be considered, without overfitting, effective in predicting new values of Oxygen Radical Absorption capacity (ORAC), except in the Multi-Layer Perceptron (MLP) algorithm. The best optimal model was obtained by the Random Forest (RF) algorithm. The in silico methodology we developed allows us to confirm the effectiveness of the obtained models, by introducing the new structural-topological attributes, as well as selecting those that most influence the class variable.