QSAR study and molecular docking of benzimidazole derivatives as potent activators of AMP-activated protein kinase

Abstract3D-QSAR and molecular docking methods were performed on a set of 74 benzimidazole derivatives previously studied as activators of the AMP-activated protein kinase (AMPK), a protein that plays a key role in the regulation of cellular energy balance. Relative enzyme activity (REA) of 74 compounds was quantitatively modelled using multiple linear regression (MLR) and neuronal networks (NN). The proposed QSAR model provided statistically significant results (rMLR=0.89; rNN=0.95 and rCV=0.90) and was validated using the leave-one-out method. The general binding mode of benzimidazole derivatives to the AMPK binding site was explored using molecular docking, with a focus on the most active molecules of our set, compounds 19 and 25

Principal Component Analysis Coupled with Artificial Neural Networks—A Combined Technique Classifying Small Molecular Structures Using a Concatenated Spectral Database

In this paper we present several expert systems that predict the class identity of the modeled compounds, based on a preprocessed spectral database. The expert systems were built using Artificial Neural Networks (ANN) and are designed to predict if an unknown compound has the toxicological activity of amphetamines (stimulant and hallucinogen), or whether it is a nonamphetamine. In attempts to circumvent the laws controlling drugs of abuse, new chemical structures are very frequently introduced on the black market. They are obtained by slightly modifying the controlled molecular structures by adding or changing substituents at various positions on the banned molecules. As a result, no substance similar to those forming a prohibited class may be used nowadays, even if it has not been specifically listed. Therefore, reliable, fast and accessible systems capable of modeling and then identifying similarities at molecular level, are highly needed for epidemiological, clinical, and forensic purposes. In order to obtain the expert systems, we have preprocessed a concatenated spectral database, representing the GC-FTIR (gas chromatography-Fourier transform infrared spectrometry) and GC-MS (gas chromatography-mass spectrometry) spectra of 103 forensic compounds. The database was used as input for a Principal Component Analysis (PCA). The scores of the forensic compounds on the main principal components (PCs) were then used as inputs for the ANN systems. We have built eight PC-ANN systems (principal component analysis coupled with artificial neural network) with a different number of input variables: 15 PCs, 16 PCs, 17 PCs, 18 PCs, 19 PCs, 20 PCs, 21 PCs and 22 PCs. The best expert system was found to be the ANN network built with 18 PCs, which accounts for an explained variance of 77%. This expert system has the best sensitivity (a rate of classification C = 100% and a rate of true positives TP = 100%), as well as a good selectivity (a rate of true negatives TN = 92.77%). A comparative analysis of the validation results of all expert systems is presented, and the input variables with the highest discrimination power are discussed

QSAR-driven screening uncovers and designs novel pyrimidine-4,6-diamine derivatives as potent JAK3 inhibitors

This study presents a robust and integrated methodology that harnesses a range of computational techniques to facilitate the design and prediction of new inhibitors targeting the JAK3/STAT pathway. This methodology encompasses several strategies, including QSAR analysis, pharmacophore modeling, ADMET prediction, covalent docking, molecular dynamics (MD) simulations, and the calculation of binding free energies (MM/GBSA). An efficacious QSAR model was meticulously crafted through the employment of multiple linear regression (MLR). The initial MLR model underwent further refinement employing an artificial neural network (ANN) methodology aimed at minimizing predictive errors. Notably, both MLR and ANN exhibited commendable performance, showcasing R2 values of 0.89 and 0.95, respectively. The model's precision was assessed via leave-one-out cross-validation (CV) yielding a Q2 value of 0.65, supplemented by rigorous Y-randomization. , The pharmacophore model effectively differentiated between active and inactive drugs, identifying potential JAK3 inhibitors, and demonstrated validity with an ROC value of 0.86. The newly discovered and designed inhibitors exhibited high inhibitory potency, ranging from 6 to 8, as accurately predicted by the QSAR models. Comparative analysis with FDA-approved Tofacitinib revealed that the new compounds exhibited promising ADMET properties and strong covalent docking (CovDock) interactions. The stability of the new discovered and designed inhibitors within the JAK3 binding site was confirmed through 500 ns MD simulations, while MM/GBSA calculations supported their binding affinity. Additionally, a retrosynthetic study was conducted to facilitate the synthesis of these potential JAK3/STAT inhibitors. The overall integrated approach demonstrates the feasibility of designing novel JAK3/STAT inhibitors with robust efficacy and excellent ADMET characteristics that surpass Tofacitinib by a significant margin

Valorization of the Phosphate Fertilizers Catalytic Activity in 1-(Benzothiazolylamino) Methyl-2-Naphthol Derivatives Synthesis

The present work focused on developing a new protocol of the 1-(benzothiazolylamino) methyl-2-naphthol derivatives synthesis by condensation of three compounds, i.e. aromatic 2-naphthol, 2-aminobenzothiazole and aldehyde. Furthermore, this reaction was carried out in the presence of three heterogeneous phosphate catalysts: monoammonium phosphates (MAP), diammonium phosphate (DAP), and trisuperphosphate (TSP). Moreover, this method offered many advantages, such as: very high yields, shorter reaction times, and the catalysts, can be easily recovered and reused without any loss of their catalytic activities.  Copyright ©2019 BCREC Group. All rights reserve

Bidline scheduling with equity by heuristic dynamic constraint aggregation

The bidline scheduling problem with equity arises in several North American airlines. It consists of determining anonymous monthly schedules, called bidlines, that will be subsequently assigned to the crew members according to their bids and seniority. These bidlines must satisfy safety and collective agreement rules. Furthermore, to ensure an equity between the employees, each bidline should have as much as possible the same number of days off and the same number of credited (paid) hours. In this paper, we propose an approximate set partitioning type formulation for this problem and two heuristics for solving it. The first one is a standard branch-and-price heuristic that relies on a rounding procedure to derive integer solutions. The second one is obtained by combining this first heuristic with a dynamic constraint aggregation method that was recently proposed in the literature. Computational results show that, for the largest tested instances, the dynamic constraint aggregation heuristic can produce better quality solutions in a fraction of the computational time required by the standard branch-and-price heuristic.Bidline scheduling Air transportation Dynamic constraint aggregation Column generation

Preference-based and cyclic bus driver rostering problem with fixed days off

Given a set of predefined duties and groups of drivers, the duty assignment problem with group-based driver preferences (DAPGDP) aims at building rosters that cover all the duties over a predetermined cyclic horizon while respecting a set of rules (hard constraints), balancing the workload between the drivers and satisfying as much as possible the driver preferences (soft constraints). In this paper, we first model the DAPGDP as a mixed-integer linear program that minimizes the number of preference violations while maintaining the workload balance of the solutions within a certain margin relative to the optimal one. Since this model is hard to solve for large instances, we propose two new matheuristics. The first one restricts the search space by preassigning duties to rosters based on an optimal solution to the duty assignment problem with fixed days off. The second algorithm makes use of a set partitioning problem to decompose rosters consisting of a large number of positions into subrosters of smaller sizes. In a series of computational experiments conducted on real-world instances, we show that these matheuristics can be used to produce high-quality solutions for large instances of the DAPGDP (i.e., with up to 333 drivers and 1509 duties) within relatively short computational times

Column generation decomposition with the degenerate constraints in the subproblem

In this paper, we propose a new Dantzig-Wolfe decomposition for degenerate linear programs with the non degenerate constraints in the master problem and the degenerate ones in the subproblem. We propose three algorithms. The first one, where some set of variables of the original problem are added to the master problem, corresponds to the Improved Primal Simplex algorithm (IPS) presented recently by Elhallaoui et al. [7]. In the second one, some extreme points of the subproblem are added as columns in the master problem. The third algorithm is a mixed implementation that adds some original variables and some extreme points of a subproblem to the master problem. Experimental results on some degenerate instances show that the proposed algorithms yield computational times that are reduced by an average factor ranging from 3.32 to 13.16 compared to the primal simplex of CPLEX.Linear programming Primal simplex algorithm Column generation Degeneracy

Quantitative Structure-Activity Relationships of Noncompetitive Antagonists of the NMDA Receptor: A Study of a Series of MK801 Derivative Molecules Using Statistical Methods and Neural Network

From a series of 50 MK801 derivative molecules, a selected set of 44 compounds was submitted to a principal components analysis (PCA), a multiple regression analysis (MRA), and a neural network (NN). This study shows that the compounds\u27 activity correlates reasonably well with the selected descriptors encoding the chemical structures. The correlation coefficients calculated by MRA and there after by NN, r = 0.986 and r = 0.974 respectively, are fairly good to evaluate a quantitative model, and to predict activity for MK801 derivatives. To test the performance of this model, the activities of the remained set of 6 compounds are deduced from the proposed quantitative model, by NN. This study proved that the predictive power of this model is relevant