A combinatorial feature selection approach to describe the QSAR of dual site inhibitors of acetylcholinesterase

A Bayesian Regularized Artificial Neural Network for Simultaneous Determination of Loratadine, Naproxen and Diclofenac in Wastewaters

Background::

Simultaneous determination of medication components in pharmaceutical samples using ordinary methods have some difficulties and therefore these determinations usually were made by expensive methods and instruments. Chemometric methods are an effective way to analyze several components simultaneously.

Objective::

In this paper, a novel approach based on Bayesian regularized artificial neural network is developed for the determination of Loratadine, Naproxen, and Diclofenac in water using UV-Vis spectroscopy.

Methods:

A dataset is collected by performing several chemical experiments and recording the UV-Vis spectra and actual constituent values. The effect of a different number of neurons in the hidden layer was analyzed based on final mean square error, and the optimum number was selected. Principle Component Analysis (PCA) was also applied to the data. Other back-propagation methods, such as Levenberg-Marquardt, scaled conjugate gradient, and resilient backpropagation, were tested.

Results::

In order to see the proposed network performance, it was performed on two crossvalidation methods, namely partitioning data into train and test parts, and leave-one-out technique. Mean square errors between expected results and predicted ones implied that the proposed method has a strong ability in predicting the expected values.

Conclusion::

he results showed that the Bayesian regularization algorithm has the best performance among other methods for simultaneous determination of Loratadine, Naproxen, and Diclofenac in water samples.

In Silico Studies in Drug Research Against Neurodegenerative Diseases

Background

Neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis, Parkinson's disease (PD), spinal cerebellar ataxias, and spinal and bulbar muscular atrophy are described by slow and selective degeneration of neurons and axons in the central nervous system (CNS) and constitute one of the major challenges of modern medicine. Computer-aided or in silico drug design methods have matured into powerful tools for reducing the number of ligands that should be screened in experimental assays.

Methods

In the present review, the authors provide a basic background about neurodegenerative diseases and in silico techniques in the drug research. Furthermore, they review the various in silico studies reported against various targets in neurodegenerative diseases, including homology modeling, molecular docking, virtual high-throughput screening, quantitative structure activity relationship (QSAR), hologram quantitative structure activity relationship (HQSAR), 3D pharmacophore mapping, proteochemometrics modeling (PCM), fingerprints, fragment-based drug discovery, Monte Carlo simulation, molecular dynamic (MD) simulation, quantum-mechanical methods for drug design, support vector machines, and machine learning approaches.

Results

Detailed analysis of the recently reported case studies revealed that the majority of them use a sequential combination of ligand and structure-based virtual screening techniques, with particular focus on pharmacophore models and the docking approach.

Conclusion

Neurodegenerative diseases have a multifactorial pathoetiological origin, so scientists have become persuaded that a multi-target therapeutic strategy aimed at the simultaneous targeting of multiple proteins (and therefore etiologies) involved in the development of a disease is recommended in future.

xMaP-An Interpretable Alignment-Free Four-Dimensional Quantitative Structure-Activity Relationship Technique Based on Molecular Surface Properties and Conformer Ensembles

A novel alignment-free molecular descriptor called xMaP (flexible MaP descriptor) is introduced. The descriptor is the advancement of the previously published translationally and rotationally invariant three-dimensional (3D) descriptor MaP (mapping property distributions onto the molecular surface) to the fourth dimension (4D). In addition to MaP, xMaP is independent of the chosen starting conformation of the encoded molecules and is therefore entirely alignment-free. This is achieved by using ensembles of conformers, which are generated by conformational searches. This step of the procedure is similar to Hopfinger's 4D quantitative structure-activity relationship (QSAR). A five-step procedure is used to compute the xMaP descriptor. First, a conformational search for each molecule is carried out. Next, for each of the conformers an approximation to the molecular surface with equally distributed surface points is computed. Third, molecular properties are projected onto this surface. Fourth, areas of identical properties are clustered to so-called patches. Fifth, the spatial distribution of the patches is converted into an alignment-free descriptor that is based on the entire conformer ensemble. The resulting descriptor can be interpreted by superimposing the most important descriptor variables and the molecules of the data set. The most important descriptor variables are identified with chemometric regression tools. The novel descriptor was applied to several benchmark data sets and was compared to other descriptors and QSAR techniques comprising a binary fingerprint, a topological pharmacophore descriptor (Cats2D), and the field-based 3D-QSAR technique GRID/PLS which is alignment-dependent. The use of conformer ensembles renders xMaP very robust. It turns out that xMaP performs very well on (almost) all data sets and that the statistical results are comparable to GRID/PLS. In addition to that, xMaP can also be used to efficiently visualize the derived quantitative structure-activity relationships.

QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer's Disease

Alzheimer's Disease (AD) is a multifactorial neurological syndrome with the combination of aging, genetic, and environmental factors triggering the pathological decline. Interestingly, the importance of the Acetylcholinesterase (AChE) enzyme has increased due to its involvement in the ß-amyloid peptide fibril formation during AD pathogenesis. In silico technique, QSAR has proven its usefulness in pharmaceutical research for the design/optimization of new chemical entities. Further, QSAR method advanced the scope of rational drug design and the search for the mechanism of drug action. It is a well-established fact that the chemical and pharmaceutical effects of a compound are closely related to its physico-chemical properties, which can be calculated by various methods from the compound structure. This chapter focuses on different Quantitative Structure-Activity Relationship (QSAR) studies carried out for a variety of cholinesterase inhibitors for the treatment of AD. These predictive models will be potentially used for further designing better and safer drugs against AD.

Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: a case study on acetylcholinesterase

Scientific disciplines such as medicinal- and environmental chemistry, pharmacology, and toxicology deal with the questions related to the effects small organic compounds exhort on biological targets and the compounds' physicochemical properties responsible for these effects. A common strategy in this endeavor is to establish structure-activity relationships (SARs). The aim of this work was to illustrate benefits of performing a statistical molecular design (SMD) and proper statistical analysis of the molecules' properties before SAR and quantitative structure-activity relationship (QSAR) analysis. Our SMD followed by synthesis yielded a set of inhibitors of the enzyme acetylcholinesterase (AChE) that had very few inherent dependencies between the substructures in the molecules. If such dependencies exist, they cause severe errors in SAR interpretation and predictions by QSAR-models, and leave a set of molecules less suitable for future decision-making. In our study, SAR- and QSAR models could show which molecular sub-structures and physicochemical features that were advantageous for the AChE inhibition. Finally, the QSAR model was used for the prediction of the inhibition of AChE by an external prediction set of molecules. The accuracy of these predictions was asserted by statistical significance tests and by comparisons to simple but relevant reference models.

2-Amino-4-arylthiazole Derivatives as Anti-giardial Agents: Synthesis, Biological Evaluation and QSAR Studies

A series of seven 2-amino-4-arylthiazoles were prepared following Hantzsch’s modified method under microwave irradiation. A set of 50 derivatives was obtained and the in vitro activity against Giardia intestinalis was evaluated. The results on the biological activity revealed that, in general, the N-(5-bromo-4-aryl-thiazol-2-yl)-acetamide scaffold showed high bioactivity. In particular, compounds 6e (IC50 = 0.39 μM) and 6b (IC50 = 0.87 μM) were found to be more potent than the positive control metronidazole. Citoxicity and acute toxicity tests performed showed low toxicity and high selectivity of the most active compounds (6e SI = 139, 6b SI = 52.3). A QSAR analysis was applied to a data set of 37 obtained 2-amino-4-arylthiazoles derivatives and the best model described a strongly correlation between the anti-giardiasic activity and molecular descriptors as E2M, RDF115m, F10, MATS6v, and Hypnotic-80, with high statistical quality. This finding indicates that N-substituted aminothiazole scaffold should be investigated for the development of highly selective anti-giardial agent.

Molecular dynamics simulation study and molecular docking descriptors in structure-based QSAR on acetylcholinesterase (AChE) inhibitors

In this study we present an approach for predicting the inhibitory activity of acetylcholinesterase (AChE) inhibitors by combining molecular dynamics (MD) simulation and docking studies in a structure-based quantitative structure-activity relationship (QSAR) model. The MD simulation was performed on AChE to obtain enzyme conformation in a water environment. The resulting conformation of the enzyme was used for docking with the most potent inhibitor (26a). Docking analysis revealed that hydrophobic interactions play important roles in the AChE-inhibitor complex. Then, all inhibitors that could bind simultaneously at the catalytic site and at the peripheral anionic site of AChE were docked into the enzyme and their interactions with AChE were used as new interpretable descriptors in a structure-based QSAR model. The least squares support vector regression was constructed using the four most relevant docking descriptors and one molecular structure descriptor. The Q(2) value of the model was found to be 0.790. Furthermore, to study the enzyme conformation stability, a second MD simulation was performed on AChE-inhibitor 26a complex. In MD simulation, the topological parameters of the inhibitor were derived from the PRODRG server, and partial atomic charges were modified using the B3LYP/6-31G level of theory. The radius of gyration for the complex showed that AChE conformation did not change in the presence of the inhibitors.