Microwave-Driven Hexagonal-to-Monoclinic Transition in BiPO4: An In-Depth Experimental Investigation and First-Principles Study.

Present theoretical and experimental work provides an in-depth understanding of the morphological, structural, electronic, and optical properties of hexagonal and monoclinic polymorphs of bismuth phosphate (BiPO4). Herein, we demonstrate how microwave irradiation induces the transformation of a hexagonal phase to a monoclinic phase in a short period of time and, thus, the photocatalytic performance of BiPO4. To complement and rationalize the experimental results, first-principles calculations have been performed within the framework of density functional theory. This was aimed at obtaining the geometric, energetic, and structural parameters as well as vibrational frequencies; further, the electronic properties (band structure diagram and density of states) of the bulk and corresponding surfaces of both the hexagonal and monoclinic phases of BiPO4 were also acquired. A detailed characterization of the low vibrational modes of both the hexagonal and monoclinic polymorphs is key to explaining the irreversible phase transformation from hexagonal to monoclinic. On the basis of the calculated values of the surface energies, a map of the available morphologies of both phases was obtained by using Wulff construction and compared to the observed scanning electron microscopy images. The BiPO4 crystals obtained after 16-32 min of microwave irradiation provided excellent photodegradation of Rhodamine B under visible-light irradiation. This enhancement was found to be related to the surface energy and the types of clusters formed on the exposed surfaces of the morphology. These findings provide details of the hexagonal-to-monoclinic phase transition in BiPO4 during microwave irradiation; further, the results will assist in the design of electronic devices with higher efficiency and reliability.

Experimental and theoretical study on structure-tautomerism among edaravone, isoxazolone, and their heterocycles derivatives as antioxidants.

Edaravone is a heterocyclic pyrazolone compound. It has pronounced effect against free radicals, however renal and hepatic disorders have been reported. Isoxazolones are considered bioisosteric analogues of pyrazolones and may have comparable properties. Thus, we investigated the structural and electronic influences for edaravone, isoxazolone, and their tautomers on antioxidant process. Structure and tautomerism study among edaravone, isoxazolone and their heterocycles derivatives were related to antioxidant mechanisms by using the hybrid DFT method B3LYP with the basis sets 6-31++G(2d,2p). The C-H tautomer was the most stable and energetically favored among them. Intramolecular N-H-N hydrogen bonds and polar medium were responsible for the low energy differences among all possible tautomers. N-H tautomers in both systems proved to be better antioxidant by SET (single electron transfer), while O-H tautomers were better antioxidant on HAT (homolytic hydrogen atom transfer) mechanism. Theoretical calculation showed that edaravone is more potent than phenylisoxazolone, however, both has similar antioxidant scavenging on experimental DPPH. The carbonyliminic system played a very important role in the antioxidant activity for both studied classes.

Pattern recognition techniques applied to the study of leishmanial glyceraldehyde-3-phosphate dehydrogenase inhibition.

Chemometric pattern recognition techniques were employed in order to obtain Structure-Activity Relationship (SAR) models relating the structures of a series of adenosine compounds to the affinity for glyceraldehyde 3-phosphate dehydrogenase of Leishmania mexicana (LmGAPDH). A training set of 49 compounds was used to build the models and the best ones were obtained with one geometrical and four electronic descriptors. Classification models were externally validated by predictions for a test set of 14 compounds not used in the model building process. Results of good quality were obtained, as verified by the correct classifications achieved. Moreover, the results are in good agreement with previous SAR studies on these molecules, to such an extent that we can suggest that these findings may help in further investigations on ligands of LmGAPDH capable of improving treatment of leishmaniasis.

Predicting biological activity and design of 5-HT6 antagonists through assessment of ANN-QSAR models in the context of Alzheimer's disease.

CONTEXT: Alzheimer's disease (AD) is the leading cause of dementia around the world, totaling about 55 million cases, with an estimated growth to 74.7 million cases in 2030, which makes its treatment widely desired. Several studies and strategies are being developed considering the main theories regarding its origin since it is not yet fully understood. Among these strategies, the 5-HT6 receptor antagonism emerges as an auspicious and viable symptomatic treatment approach for AD. The 5-HT6 receptor belongs to the G protein-coupled receptor (GPCR) family and is closely implicated in memory loss processes. As a serotonin receptor, it plays an important role in cognitive function. Consequently, targeting this receptor presents a compelling therapeutic opportunity. By employing antagonists to block its activity, the 5-HT6 receptor's functions can be effectively modulated, leading to potential improvements in cognition and memory. METHODS: Addressing this challenge, our research explored a promising avenue in drug discovery for AD, employing Artificial Neural Networks-Quantitative Structure-Activity Relationship (ANN-QSAR) models. These models have demonstrated great potential in predicting the biological activity of compounds based on their molecular structures. By harnessing the capabilities of machine learning and computational chemistry, we aimed to create a systematic approach for analyzing and forecasting the activity of potential drug candidates, thus streamlining the drug discovery process. We assembled a diverse set of compounds targeting this receptor and utilized density functional theory (DFT) calculations to extract essential molecular descriptors, effectively representing the structural features of the compounds. Subsequently, these molecular descriptors served as input for training the ANN-QSAR models alongside corresponding biological activity data, enabling us to predict the potential efficacy of novel compounds as 5-hydroxytryptamine receptor 6 (5-HT6) antagonists. Through extensive analysis and validation of ANN-QSAR models, we identified eight new promising compounds with therapeutic potential against AD.

Evaluation and theoretical study on the anti-inflammatory mechanism of 1-nitro-2-phenylethane.

In this study, 1-nitro-2-phenylethane was evaluated with respect to its effects in edema models of acute inflammation induced with carrageenan, dextran, and croton oil. 1-Nitro-2-phenylethane produced inhibition of rat paw edema induced by carrageenan and dextran at the doses of 25 and 50 mg/kg. The same doses caused an inhibition of croton oil-induced ear edema in mice. Our results suggest that 1-nitro-2-phenylethane has anti-inflammatory activity, probably of peripheral origin, acting in the synthesis and/or release of inflammatory mediators. A conformational study of 1-nitro-2-phenylethane was carried out using density functional theory calculations, showing three different groups of conformers corresponding to energy minimum geometries. The stereoelectronic repulsions are responsible for conformational preferences and the one most stable conformer. The prostaglandin endoperoxide synthase mechanism is related more to electrophilic than nucleophilic properties.

Identification of electronic and structural descriptors of adenosine analogues related to inhibition of leishmanial glyceraldehyde-3-phosphate dehydrogenase.

Quantitative structure-activity relationship (QSAR) studies were performed in order to identify molecular features responsible for the antileishmanial activity of 61 adenosine analogues acting as inhibitors of the enzyme glyceraldehyde 3-phosphate dehydrogenase of Leishmania mexicana (LmGAPDH). Density functional theory (DFT) was employed to calculate quantum-chemical descriptors, while several structural descriptors were generated with Dragon 5.4. Variable selection was undertaken with the ordered predictor selection (OPS) algorithm, which provided a set with the most relevant descriptors to perform PLS, PCR and MLR regressions. Reliable and predictive models were obtained, as attested by their high correlation coefficients, as well as the agreement between predicted and experimental values for an external test set. Additional validation procedures were carried out, demonstrating that robust models were developed, providing helpful tools for the optimization of the antileishmanial activity of adenosine compounds.

Understanding the molecular aspects of tetrahydrocannabinol and cannabidiol as antioxidants.

An antioxidant mechanism of tetrahydrocannabinol (THC) and cannabidiol (CBD) were compared with a simplified model of α-tocopherol, butylhydroxytoluene and hydroxytoluene in order to understand the antioxidant nature of THC and CBD molecules using DFT. The following electronic properties were evaluated: frontier orbitals nature, ionization potential, O-H bond dissociation energy (BDEOH), stabilization energy, and spin density distribution. An important factor that shows an influence in the antioxidant property of THC is the electron abstraction at the phenol position. Our data indicate that the decrease of the HOMO values and the highest ionization potential values are related to phenol, ether, and alkyl moieties. On the other hand, BDEOH in molecules with the cyclohexenyl group at ortho position of phenol are formed from lower energies than the molecules with an ether group at the meta position. In the light of our results, the properties calculated here predict that THC has a sightly higher antioxidant potential than CBD.

A PLS study on the psychotropic activity for a series of cannabinoid compounds.

INTRODUCTION: The use of the Cannabis sativa plant by man has been common for centuries due to its numerous therapeutic properties resulting from the compounds present in it, called cannabinoids. However, the use of these compounds as drugs is still limited due to the psychotropic effects caused by them. The proteins that act as receptors of cannabinoid compounds were identified and characterized, being called CB1 and CB2 receptors. There is a series of 50 cannabinoid compounds that was studied through quantum and chemometric methods in order to obtain a mathematical model that could relate the structure of these compounds to their psychotropic activity. That model proved to be effective by predicting the psychoactivity of the 50 compounds from the series and elucidating relevant characteristics that imply in psychoactivity. However, most of these 50 compounds do not have experimental data of biological activity with CB1 and CB2 receptors. OBJECTIVES: This study aims to generate QSAR models in order to predict the biological activity of the 50 cannabinoid compounds and then relate the predicted biological activity values to the already known psychoactivity. METHODS: Another series of cannabinoid compounds was selected to generate and validate QSAR models, aiming to predict the biological activity of the 50 cannabinoid compounds with both CB1 and CB2 receptors. RESULTS: The PLS-CB1 and PLS-CB2 QSAR models were generated and validated in this work, proving to be highly predictive, and the biological activities (pK ) of the 50 cannabinoid compounds were predicted by them. It is important to highlight compounds Ic14, Ic18, and Ic19 (psychotropic inactive) which presented higher predicted pK values than the main cannabinoid compounds (Δ9-THC and Δ8-THC). Also, compound Ic21 stood out as the highest value of the predicted biological activities in the interaction with the CB2 receptor. CONCLUSION: The generated PLS models and the predicted pKi values of the 50 cannabinoid compounds can provide valuable information in the drug design of new cannabinoid compounds that can interact with CB1 and CB2 receptors in a therapeutic way with no psychotropic effects.

Antioxidant capacity of simplified oxygen heterocycles and proposed derivatives by theoretical calculations.

CONTEXT: Some structural properties can be involved in the antioxidant capacity of several polyphenol derivatives, among them their simplified structures. This study examines the contribution of simplified structure for the antioxidant capacity of some natural and synthetic antioxidants. The resonance structures were related to the π-type electron system of carbon-carbon double bonds between both phenyl rings. Trans-resveratrol, phenyl-benzofuran, phenyl-indenone, and benzylidene-benzofuranone are the best basic antioxidant templates among the simplified derivatives studied here. Additionally, the stilbene moiety was found on the molecules with the best antioxidant capacity. Furthermore, our investigation suggests that these compounds can be used as antioxidant scaffold for designing and developing of new promising derivatives. METHODS: To investigate the structure-antioxidant capacity for sixteen simplified natural and proposed derivatives we have employed density functional theory and used Gaussian 09. Our DFT calculations were performed using the B3LYP functional and the 6-31+G(d,p) basis set. All electron transfer mechanisms were investigated by using values of HOMO, ionization potential, energy affinity, stabilization energies, and spin density distributions.

Adsorption of sodium dodecyl sulfate on Ge substrate: the effect of a low-polarity solvent.

This paper describes the adsorption of sodium dodecyl sulfate (SDS) molecules in a low polar solvent on Ge substrate by using Fourier transform infrared-attenuated total reflection (FTIR-ATR) spectroscopy and atomic force microscopy (AFM). The maximum SDS amount adsorbed is (5.0 ± 0.3) × 10(14) molecules cm(-2) in CHCl(3), while with the use of CCl(4) as subphase the ability of SDS adsorbed is 48% lower. AFM images show that depositions are highly disordered over the interface, and it was possible to establish that the size of the SDS deposition is around 30-40 nm over the Ge surface. A complete description of the infrared spectroscopic bands for the head and tail groups in the SDS molecule is also provided.

Density functional theory (DFT) study of edaravone derivatives as antioxidants.

Quantum chemical calculations at the B3LYP/6-31G* level of theory were employed for the structure-activity relationship and prediction of the antioxidant activity of edaravone and structurally related derivatives using energy (E), ionization potential (IP), bond dissociation energy (BDE), and stabilization energies (ΔE(iso)). Spin density calculations were also performed for the proposed antioxidant activity mechanism. The electron abstraction is related to electron-donating groups (EDG) at position 3, decreasing the IP when compared to substitution at position 4. The hydrogen abstraction is related to electron-withdrawing groups (EDG) at position 4, decreasing the BDE(CH) when compared to other substitutions, resulting in a better antioxidant activity. The unpaired electron formed by the hydrogen abstraction from the C-H group of the pyrazole ring is localized at 2, 4, and 6 positions. The highest scavenging activity prediction is related to the lowest contribution at the carbon atom. The likely mechanism is related to hydrogen transfer. It was found that antioxidant activity depends on the presence of EDG at the C(2) and C(4) positions and there is a correlation between IP and BDE. Our results identified three different classes of new derivatives more potent than edaravone.

Examining the Effect of Charged Lipids on Mitochondrial Outer Membrane Dynamics Using Atomistic Simulations.

The outer mitochondrial membrane (OMM) is involved in multiple cellular functions such as apoptosis, inflammation and signaling via its membrane-associated and -embedded proteins. Despite the central role of the OMM in these vital phenomena, the structure and dynamics of the membrane have regularly been investigated in silico using simple two-component models. Accordingly, the aim was to generate the realistic multi-component model of the OMM and inspect its properties using atomistic molecular dynamics (MD) simulations. All major lipid components, phosphatidylinositol (PI), phosphatidylcholine (PC), phosphatidylethanolamine (PE), and phosphatidylserine (PS), were included in the probed OMM models. Because increased levels of anionic PS lipids have potential effects on schizophrenia and, more specifically, on monoamine oxidase B enzyme activity, the effect of varying the PS concentration was explored. The MD simulations indicate that the complex membrane lipid composition (MLC) behavior is notably different from the two-component PC-PE model. The MLC changes caused relatively minor effects on the membrane structural properties such as membrane thickness or area per lipid; however, notable effects could be seen with the dynamical parameters at the water-membrane interface. Increase of PS levels appears to slow down lateral diffusion of all lipids and, in general, the presence of anionic lipids reduced hydration and slowed down the PE headgroup rotation. In addition, sodium ions could neutralize the membrane surface, when PI was the main anionic component; however, a similar effect was not seen for high PS levels. Based on these results, it is advisable for future studies on the OMM and its protein or ligand partners, especially when wanting to replicate the correct properties on the water-membrane interface, to use models that are sufficiently complex, containing anionic lipid types, PI in particular.

Towards a relationship between photoluminescence emissions and photocatalytic activity of Ag2SeO4: combining experimental data and theoretical insights.

A systematic theoretical and experimental study was carried out to find a relationship between photoluminescence emissions and photocatalytic activity of Ag2SeO4 obtained by different synthesis methods (sonochemistry, ultrasonic probe, coprecipitation and microwave assisted hydrothermal synthesis). Experimental characterization techniques (XRD with Rietveld refinement, Raman, FTIR, UV-vis, XPS and photoluminescence spectroscopy) were used to elucidate its structural order at short, medium, and long ranges. Morphological analysis performed by FE-SEM showed distinct morphologies due to the different methods of synthesis. Based on density functional theory (DFT) calculations, it was possible to study in detail the Ag2SeO4 surface properties, including its surface energy, geometry, and electronic structure for the (100), (010), (001), (101), (011), (110), (111), (021), (012) and (121) surfaces. The equilibrium morphology of Ag2SeO4 was predicted as a truncated octahedron with exposed (111), (001), (010) and (011) surfaces. Photoluminescence emissions showed a band covering the visible spectrum, and the Ag2SeO4 obtained by the coprecipitation method presented the most intense band with a maximum in the red region. Photocatalytic results confirmed that Ag2SeO4 synthesized by the sonochemistry method is the best photocatalyst for rhodamine B degradation under UV light irradiation.

Drug design of new 5-HT6R antagonists aided by artificial neural networks.

Alzheimer's Disease (AD) is the most frequent illness and cause of death amongst the age related-neurodegenerative disorders. The Alzheimer's Disease International (ADI) reported in 2019 that over 50 million people were living with dementia in the world and this number could potentially be around 152 million by 2050.5-hydroxtryptamine subtype 6 receptor (5-HT6R) has been identified as a potential anti-amnesic drug target and therefore, the administration of 5-HT6R antagonists can likely mitigate the memory loss and intellectual deterioration associated with AD. Herein, computational tools were applied to design new 5-HT6 antagonists and their biological activity values were predicted by our QSAR model obtained from Artificial Neural Networks (ANN). The proposed compounds here from the QSAR-ANN model presented significant biological activity values and some of them have achieved pKi above 9.00. Furthermore, our results suggest that the presence of halogen atoms (especially bromine) linked to the aromatic ring at para-position (HYD) contribute considerably to the increase of the biological activity values while bulky groups in the PI position do not culminate with the increase antagonist activity of compounds here analyzed. Finally, the ADME/Tox profile as well as the synthetic accessibility of new proposed compounds qualify them to go on further with experimental procedures and thenceforward their antagonist effects can be confirmed.

A partial least squares and artificial neural network study for a series of arylpiperazines as antidepressant agents.

Depression affects more than 300 million people around the world and can lead to suicide. About 30% of patients on treatment for depression drop out of therapy due to side effects or to latency time associated to therapeutic effects. 5-HT receptor, known as serotonin, is considered the key in depression treatment. Arylpiperazine compounds are responsible for several pharmacological effects and are considered as ligands in serotonin receptors, such as the subtype 5-HT2a. Here, in silico studies were developed using partial least squares (PLSs) and artificial neural networks (ANNs) to design new arylpiperazine compounds that could interact with the 5-HT2a receptor. First, molecular and electronic descriptors were calculated and posteriorly selected from correlation matrixes and genetic algorithm (GA). Then, the selected descriptors were used to construct PLS and ANN models that showed to be robust and predictive. Lastly, new arylpiperazine compounds were designed and their biological activity values were predicted by both PLS and ANN models. It is worth to highlight compounds G5 and G7 (predicted by the PLS model) and G3 and G15 (predicted by the ANN model), whose predicted pIC50 values were as high as the three highest values from the arylpiperazine original set studied here. Therefore, it can be asserted that the two models (PLS and ANN) proposed in this work are promising for the prediction of the biological activity of new arylpiperazine compounds and may significantly contribute to the design of new drugs for the treatment of depression.

Advances and Perspectives in Applying Deep Learning for Drug Design and Discovery.

Discovering (or planning) a new drug candidate involves many parameters, which makes this process slow, costly, and leading to failures at the end in some cases. In the last decades, we have witnessed a revolution in the computational area (hardware, software, large-scale computing, etc.), as well as an explosion in data generation (big data), which raises the need for more sophisticated algorithms to analyze this myriad of data. In this scenario, we can highlight the potentialities of artificial intelligence (AI) or computational intelligence (CI) as a powerful tool to analyze medicinal chemistry data. According to IEEE, computational intelligence involves the theory, the design, the application, and the development of biologically and linguistically motivated computational paradigms. In addition, CI encompasses three main methodologies: neural networks (NN), fuzzy systems, and evolutionary computation. In particular, artificial neural networks have been successfully applied in medicinal chemistry studies. A branch of the NN area that has attracted a lot of attention refers to deep learning (DL) due to its generalization power and ability to extract features from data. Therefore, in this mini-review we will briefly outline the present scope, advances, and challenges related to the use of DL in drug design and discovery, describing successful studies involving quantitative structure-activity relationships (QSAR) and virtual screening (VS) of databases containing thousands of compounds.

A Proposal for the Mechanism of the CH + CO2 Reaction.

Few experimental studies on the CH + CO2 global reaction propose H, CO, and HCO as major products. However, the reaction mechanisms behind this process have not yet been elucidated. Moreover, some intriguing kinetic particularities were noticed in these previous investigations. The advanced theoretical study performed here shows that a CH insertion mechanism is capable of explaining all the experimental data available. Hence, the strong deviations from a traditional Arrhenius behavior ascribed to the rate-determining elementary reaction (the CH insertion step) account for the kinetic particularities observed experimentally. A change in the preferred product channel as temperatures increase (from HCO + CO to H + 2CO) is also predicted to occur due to the HCO decomposition, although the CH depletion rates in typical conditions are not affected by this additional step.

A chemometric study of the 5-HT(1A) receptor affinities presented by arylpiperazine compounds.

Arylpiperazine compounds are promising 5-HT(1A) receptor ligands that can contribute for accelerating the onset of therapeutic effect of selective serotonin reuptake inhibitors. In the present work, the chemometric methods HCA, PCA, KNN, SIMCA and PLS were employed in order to obtain SAR and QSAR models relating the structures of arylpiperazine compounds to their 5-HT(1A) receptor affinities. A training set of 52 compounds was used to construct the models and the best ones were obtained with nine topological descriptors. The classification and regression models were externally validated by means of predictions for a test set of 14 compounds and have presented good quality, as verified by the correctness of classifications, in the case of pattern recognition studies, and by the high correlation coefficients (q(2)=0.76, r(2)=0.83) and small prediction errors for the PLS regression. Since the results are in good agreement with previous SAR studies, we can suggest that these findings can help in the search for 5-HT(1A) receptor ligands that are able to improve antidepressant treatment.

Two-dimensional QSAR studies on arylpiperazines as high-affinity 5-HT(1A) receptor ligands.

5-HT(1A) receptor plays an important role in the delayed onset of antidepressant action of a class of selective serotonin reuptake inhibitors. Moreover, 5-HT(1A) receptor levels have been shown to be altered in patients suffering from major depression. In this work, hologram quantitative structure-activity relationship (HQSAR) studies were performed on a series of arylpiperazine compounds presenting affinity to the 5-HT(1A) receptor. The models were constructed with a training set of 70 compounds. The most significant HQSAR model (q(2) = 0.81, r(2) = 0.96) was generated using atoms, bonds, connections, chirality, and donor and acceptor as fragment distinction, with fragment size of 6-9. Predictions for an external test set containing 20 compounds are in good agreement with experimental results showing the robustness of the model. Additionally, useful information can be obtained from the 2D contribution maps.

New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists.

The treatment of neuropathic pain is very complex and there are few drugs approved for this purpose. Among the studied compounds in the literature, sigma-1 receptor antagonists have shown to be promising. In order to develop QSAR studies applied to the compounds of 1-arylpyrazole derivatives, multivariate analyses have been performed in this work using partial least square (PLS) and artificial neural network (ANN) methods. A PLS model has been obtained and validated with 45 compounds in the training set and 13 compounds in the test set (r2training = 0.761, q2 = 0.656, r2test = 0.746, MSEtest = 0.132 and MAEtest = 0.258). Additionally, multi-layer perceptron ANNs (MLP-ANNs) were employed in order to propose non-linear models trained by gradient descent with momentum backpropagation function. Based on MSEtest values, the best MLP-ANN models were combined in a MLP-ANN consensus model (MLP-ANN-CM; r2test = 0.824, MSEtest = 0.088 and MAEtest = 0.197). In the end, a general consensus model (GCM) has been obtained using PLS and MLP-ANN-CM models (r2test = 0.811, MSEtest = 0.100 and MAEtest = 0.218). Besides, the selected descriptors (GGI6, Mor23m, SRW06, H7m, MLOGP, and µ) revealed important features that should be considered when one is planning new compounds of the 1-arylpyrazole class. The multivariate models proposed in this work are definitely a powerful tool for the rational drug design of new compounds for neuropathic pain treatment. Graphical abstract Main scaffold of the 1-arylpyrazole derivatives and the selected descriptors.