Quantitative Structure-Activity Relationship (QSAR) Studies for the Inhibition of MAOs

Two dimensional-QSAR and molecular dynamics studies of a selected class of aldoxime- and hydroxy-functionalized chalcones as monoamine oxidase-B inhibitors

Candidates generated from unsaturated ketone (chalcone) demonstrated as strong, reversible and specific monoamine oxidase-B (MAO-B) inhibitory activity. For the research on MAO-B inhibition, our team has synthesized and evaluated a panel of aldoxime-chalcone ethers (ACE) and hydroxylchalcones (HC). The MAO-B inhibitory activity of several candidates is in the micro- to nanomolar range in these series. The purpose of this research was to develop predictive QSAR models and look into the relation between MAO-B inhibition by aldoxime and hydroxyl-functionalized chalcones. It was shown that the molecular descriptors ETA Shape P, MDEO-12, ETA dBetaP, SpMax1 Bhi and ETA EtaP B are significant in the inhibitory action of the MAO-B target. Using the current 2D QSAR models, potential chalcone-based MAO-B inhibitors might be created. The lead molecules were further analyzed by the detailed molecular dynamics study to establish the stability of the ligand-enzyme complex.Communicated by Ramaswamy H. Sarma.

MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug-Enzyme Interactions

Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a large dataset of drug-enzyme pairs (62,524), we recognized a unique opportunity to attempt to build a novel multi-target machine learning (MTML) quantitative structure-activity relationship (QSAR) model for probing interactions among different drugs and enzyme targets. To this end, this paper presents an MTML-QSAR model based on using the features of topological drugs together with the artificial neural network (ANN) multi-layer perceptron (MLP). Validation of the final best model found was carried out by internal cross-validation statistics and other relevant diagnostic statistical parameters. The overall accuracy of the derived model was found to be higher than 96%. Finally, to maximize the diffusion of this model, a public and accessible tool has been developed to allow users to perform their own predictions. The developed web-based tool is public accessible and can be downloaded as free open-source software.

Therapeutic application of quercetin in aging-related diseases: SIRT1 as a potential mechanism

Quercetin, a naturally non-toxic flavonoid within the safe dose range with antioxidant, anti-apoptotic and anti-inflammatory properties, plays an important role in the treatment of aging-related diseases. Sirtuin 1 (SIRT1), a member of NAD+-dependent deacetylase enzyme family, is extensively explored as a potential therapeutic target for attenuating aging-induced disorders. SIRT1 possess beneficial effects against aging-related diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), Depression, Osteoporosis, Myocardial ischemia (M/I) and reperfusion (MI/R), Atherosclerosis (AS), and Diabetes. Previous studies have reported that aging increases tissue susceptibility, whereas, SIRT1 regulates cellular senescence and multiple aging-related cellular processes, including SIRT1/Keap1/Nrf2/HO-1 and SIRTI/PI3K/Akt/GSK-3β mediated oxidative stress, SIRT1/NF-κB and SIRT1/NLRP3 regulated inflammatory response, SIRT1/PGC1α/eIF2α/ATF4/CHOP and SIRT1/PKD1/CREB controlled phosphorylation, SIRT1-PINK1-Parkin mediated mitochondrial damage, SIRT1/FoxO mediated autophagy, and SIRT1/FoxG1/CREB/BDNF/Trkβ-catenin mediated neuroprotective effects. In this review, we summarized the role of SIRT1 in the improvement of the attenuation effect of quercetin on aging-related diseases and the relationship between relevant signaling pathways regulated by SIRT1. Moreover, the functional regulation of quercetin in aging-related markers such as oxidative stress, inflammatory response, mitochondrial function, autophagy and apoptosis through SIRT1 was discussed. Finally, the prospects of an extracellular vesicles (EVs) as quercetin loading and delivery, and SIRT1-mediated EVs as signal carriers for treating aging-related diseases, as well as discussed the ferroptosis alleviation effects of quercetin to protect against aging-related disease via activating SIRT1. Generally, SIRT1 may serve as a promising therapeutic target in the treatment of aging-related diseases via inhibiting oxidative stress, reducing inflammatory responses, and restoring mitochondrial dysfunction.

Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure-activity relationship-based artificial intelligence and molecular docking of hit compounds

Dipeptidyl peptidase-4 (DPP-4) inhibitors are becoming an essential drug in the treatment of type 2 diabetes mellitus; however, some classes of these drugs exert side effects, including joint pain and pancreatitis. Studies suggest that these side effects might be related to secondary inhibition of DPP-8 and DPP-9. In this study, we identified DPP-4-inhibitor hit compounds selective against DPP-8 and DPP-9. We built a virtual screening workflow using a quantitative structure-activity relationship (QSAR) strategy based on artificial intelligence to allow faster screening of millions of molecules for the DPP-4 target relative to other screening methods. Five regression machine learning algorithms and four classification machine learning algorithms were applied to build virtual screening workflows, with the QSAR model applied using support vector regression (R²_pred 0.78) and the classification QSAR model using the random forest algorithm with 92.2% accuracy. Virtual screening results of > 10 million molecules obtained 2 716 hits compounds with a pIC₅₀ value of > 7.5. Additionally, molecular docking results of several potential hit compounds for DPP-4, DPP-8, and DPP-9 identified CH0002 as showing high inhibitory potential against DPP-4 and low inhibitory potential for DPP-8 and DPP-9 enzymes. These results demonstrated the effectiveness of this technique for identifying DPP-4-inhibitor hit compounds selective for DPP-4 and against DPP-8 and DPP-9 and suggest its potential efficacy for applications to discover hit compounds of other targets.