Shaping the Future of Obesity Treatment: In Silico Multi-Modeling of IP6K1 Inhibitors for Obesity and Metabolic Dysfunction.

Recent research has uncovered a promising approach to addressing the growing global health concern of obesity and related disorders. The inhibition of inositol hexakisphosphate kinase 1 (IP6K1) has emerged as a potential therapeutic strategy. This study employs multiple ligand-based in silico modeling techniques to investigate the structural requirements for benzisoxazole derivatives as IP6K1 inhibitors. Firstly, we developed linear 2D Quantitative Structure-Activity Relationship (2D-QSAR) models to ensure both their mechanistic interpretability and predictive accuracy. Then, ligand-based pharmacophore modeling was performed to identify the essential features responsible for the compounds' high activity. To gain insights into the 3D requirements for enhanced potency against the IP6K1 enzyme, we employed multiple alignment techniques to set up 3D-QSAR models. Given the absence of an available X-ray crystal structure for IP6K1, a reliable homology model for the enzyme was developed and structurally validated in order to perform structure-based analyses on the selected dataset compounds. Finally, molecular dynamic simulations, using the docked poses of these compounds, provided further insights. Our findings consistently supported the mechanistic interpretations derived from both ligand-based and structure-based analyses. This study offers valuable guidance on the design of novel IP6K1 inhibitors. Importantly, our work exclusively relies on non-commercial software packages, ensuring accessibility for reproducing the reported models.

In Silico Modeling and Structural Analysis of Soluble Epoxide Hydrolase Inhibitors for Enhanced Therapeutic Design.

Human soluble epoxide hydrolase (sEH), a dual-functioning homodimeric enzyme with hydrolase and phosphatase activities, is known for its pivotal role in the hydrolysis of epoxyeicosatrienoic acids. Inhibitors targeting sEH have shown promising potential in the treatment of various life-threatening diseases. In this study, we employed a range of in silico modeling approaches to investigate a diverse dataset of structurally distinct sEH inhibitors. Our primary aim was to develop predictive and validated models while gaining insights into the structural requirements necessary for achieving higher inhibitory potential. To accomplish this, we initially calculated molecular descriptors using nine different descriptor-calculating tools, coupled with stochastic and non-stochastic feature selection strategies, to identify the most statistically significant linear 2D-QSAR model. The resulting model highlighted the critical roles played by topological characteristics, 2D pharmacophore features, and specific physicochemical properties in enhancing inhibitory potential. In addition to conventional 2D-QSAR modeling, we implemented the Transformer-CNN methodology to develop QSAR models, enabling us to obtain structural interpretations based on the Layer-wise Relevance Propagation (LRP) algorithm. Moreover, a comprehensive 3D-QSAR analysis provided additional insights into the structural requirements of these compounds as potent sEH inhibitors. To validate the findings from the QSAR modeling studies, we performed molecular dynamics (MD) simulations using selected compounds from the dataset. The simulation results offered crucial insights into receptor-ligand interactions, supporting the predictions obtained from the QSAR models. Collectively, our work serves as an essential guideline for the rational design of novel sEH inhibitors with enhanced therapeutic potential. Importantly, all the in silico studies were performed using open-access tools to ensure reproducibility and accessibility.

Designing multi-target drugs for the treatment of major depressive disorder.

INTRODUCTION: Major depressive disorders (MDD) pose major health burdens globally. Currently available medications have their limitations due to serious adverse effects, long latency periods as well as resistance. Considering the highly complicated pathological nature of this disorder, it has been suggested that multitarget drugs or multi-target-directed ligands (MTDLs) may provide long-term therapeutic solutions for the treatment of MDD. AREAS COVERED: In the current review, recent lead design and lead modification strategies have been covered. Important investigations reported in the last ten years (2013-2022) for the preclinical development of MTDLs (through synthetic medicinal chemistry and biological evaluation) for the treatment of MDD were discussed as case studies to focus on the recent design strategies. The discussions are categorized on the basis of pharmacological targets. Based on these important case studies, the challenges involved in different design strategies were discussed in detail. EXPERT OPINION: Even though large variations were observed in the selection of pharmacological targets, some potential biological targets (NMDA, melatonin receptors) are required to be explored extensively for the design of MTDLs. Similarly, apart from structure activity relationship (SAR), in silico techniques such as multitasking cheminformatic modeling, molecular dynamics simulation and virtual screening should be exploited to a greater extent.

Adsorption of Peptides onto Carbon Nanotubes Grafted with Poly(ethylene Oxide) Chains: A Molecular Dynamics Simulation Study.

Carbon nanotubes (CNTs) display exceptional properties that predispose them to wide use in technological or biomedical applications. To remove the toxicity of CNTs and to protect them against undesired protein adsorption, coverage of the CNT sidewall with poly(ethylene oxide) (PEO) is often considered. However, controversial results on the antifouling effectiveness of PEO layers have been reported so far. In this work, the interactions of pristine CNT and CNT covered with the PEO chains at different grafting densities with polyglycine, polyserine, and polyvaline are studied using molecular dynamics simulations in vacuum, water, and saline environments. The peptides are adsorbed on CNT in all investigated systems; however, the adsorption strength is reduced in aqueous environments. Save for one case, addition of NaCl at a physiological concentration to water does not appreciably influence the adsorption and structure of the peptides or the grafted PEO layer. It turns out that the flexibility of the peptide backbone allows the peptide to adopt more asymmetric conformations which may be inserted deeper into the grafted PEO layer. Water molecules disrupt the internal hydrogen bonds in the peptides, as well as the hydrogen bonds formed between the peptides and the PEO chains.

In silico characterization of aryl benzoyl hydrazide derivatives as potential inhibitors of RdRp enzyme of H5N1 influenza virus.

RNA-dependent RNA polymerase (RdRp) is a potential therapeutic target for the discovery of novel antiviral agents for the treatment of life-threatening infections caused by newly emerged strains of the influenza virus. Being one of the most conserved enzymes among RNA viruses, RdRp and its inhibitors require further investigations to design novel antiviral agents. In this work, we systematically investigated the structural requirements for antiviral properties of some recently reported aryl benzoyl hydrazide derivatives through a range of in silico tools such as 2D-quantitative structure-activity relationship (2D-QSAR), 3D-QSAR, structure-based pharmacophore modeling, molecular docking and molecular dynamics simulations. The 2D-QSAR models developed in the current work achieved high statistical reliability and simultaneously afforded in-depth mechanistic interpretability towards structural requirements. The structure-based pharmacophore model developed with the docked conformation of one of the most potent compounds with the RdRp protein of H5N1 influenza strain was utilized for developing a 3D-QSAR model with satisfactory statistical quality validating both the docking and the pharmacophore modeling methodologies performed in this work. However, it is the atom-based alignment of the compounds that afforded the most statistically reliable 3D-QSAR model, the results of which provided mechanistic interpretations consistent with the 2D-QSAR results. Additionally, molecular dynamics simulations performed with the apoprotein as well as the docked complex of RdRp revealed the dynamic stability of the ligand at the proposed binding site of the receptor. At the same time, it also supported the mechanistic interpretations drawn from 2D-, 3D-QSAR and pharmacophore modeling. The present study, performed mostly with open-source tools and webservers, returns important guidelines for research aimed at the future design and development of novel anti-viral agents against various RNA viruses like influenza virus, human immunodeficiency virus-1, hepatitis C virus, corona virus, and so forth.

Toxicity Assessment of the Binary Mixtures of Aquatic Organisms Based on Different Hypothetical Descriptors.

Modern industrialization has led to the creation of a wide range of organic chemicals, especially in the form of multicomponent mixtures, thus making the evaluation of environmental pollution more difficult by normal methods. In this paper, we attempt to use forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNN) to establish quantitative structure-activity relationship models (QSARs) to predict the toxicity of 79 binary mixtures of aquatic organisms using different hypothetical descriptors. To search for the proper mixture descriptors, 11 mixture rules were performed and tested based on preliminary modeling results. The statistical parameters of the best derived MLR model were Ntrain = 62, R2 = 0.727, RMS = 0.494, F = 159.537, Q2LOO = 0.727, and Q2pred = 0.725 for the training set; and Ntest = 17, R2 = 0.721, RMS = 0.508, F = 38.773, and q2ext = 0.720 for the external test set. The RBFNN model gave the following statistical results: Ntrain = 62, R2 = 0.956, RMS = 0.199, F = 1279.919, Q2LOO = 0.955, and Q2pred = 0.855 for the training set; and Ntest = 17, R2 = 0.880, RMS = 0.367, F = 110.980, and q2ext = 0.853 for the external test set. The quality of the models was assessed by validating the relevant parameters, and the final results showed that the developed models are predictive and can be used for the toxicity prediction of binary mixtures within their applicability domain.

Predicting the Surface Tension of Deep Eutectic Solvents: A Step Forward in the Use of Greener Solvents.

Deep eutectic solvents (DES) are an important class of green solvents that have been developed as an alternative to toxic solvents. However, the large-scale industrial application of DESs requires fine-tuning their physicochemical properties. Among others, surface tension is one of such properties that have to be considered while designing novel DESs. In this work, we present the results of a detailed evaluation of Quantitative Structure-Property Relationships (QSPR) modeling efforts designed to predict the surface tension of DESs, following the Organization for Economic Co-operation and Development (OECD) guidelines. The data set used comprises a large number of structurally diverse binary DESs and the models were built systematically through rigorous validation methods, including 'mixtures-out'- and 'compounds-out'-based data splitting. The most predictive individual QSPR model found is shown to be statistically robust, besides providing valuable information about the structural and physicochemical features responsible for the surface tension of DESs. Furthermore, the intelligent consensus prediction strategy applied to multiple predictive models led to consensus models with similar statistical robustness to the individual QSPR model. The benefits of the present work stand out also from its reproducibility since it relies on fully specified computational procedures and on publicly available tools. Finally, our results not only guide the future design and screening of novel DESs with a desirable surface tension but also lays out strategies for efficiently setting up silico-based models for binary mixtures.

Computational Modelling and Sustainable Synthesis of a Highly Selective Electrochemical MIP-Based Sensor for Citalopram Detection.

A novel molecularly imprinted polymer (MIP) has been developed based on a simple and sustainable strategy for the selective determination of citalopram (CTL) using screen-printed carbon electrodes (SPCEs). The MIP layer was prepared by electrochemical in situ polymerization of the 3-amino-4 hydroxybenzoic acid (AHBA) functional monomer and CTL as a template molecule. To simulate the polymerization mixture and predict the most suitable ratio between the template and functional monomer, computational studies, namely molecular dynamics (MD) simulations, were carried out. During the experimental preparation process, essential parameters controlling the performance of the MIP sensor, including CTL:AHBA concentration, number of polymerization cycles, and square wave voltammetry (SWV) frequency were investigated and optimized. The electrochemical characteristics of the prepared MIP sensor were evaluated by both cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) techniques. Based on the optimal conditions, a linear electrochemical response of the sensor was obtained by SWV measurements from 0.1 to 1.25 µmol L-1 with a limit of detection (LOD) of 0.162 µmol L-1 (S/N = 3). Moreover, the MIP sensor revealed excellent CTL selectivity against very close analogues, as well as high imprinting factor of 22. Its applicability in spiked river water samples demonstrated its potential for adequate monitoring of CTL. This sensor offers a facile strategy to achieve portability while expressing a willingness to care for the environment.

Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next?

Conventional in silico modeling is often viewed as 'one-target' or 'single-task' computer-aided modeling since it mainly relies on forecasting an endpoint of interest from similar input data. Multitasking or multitarget in silico modeling, in contrast, embraces a set of computational techniques that efficiently integrate multiple types of input data for setting up unique in silico models able to predict the outcome(s) relating to various experimental and/or theoretical conditions. The latter, specifically, based upon the Box-Jenkins moving average approach, has been applied in the last decade to several research fields including drug and materials design, environmental sciences, and nanotechnology. The present review discusses the current status of multitasking computer-aided modeling efforts, meanwhile describing both the existing challenges and future opportunities of its underlying techniques. Some important applications are also discussed to exemplify the ability of multitasking modeling in deriving holistic and reliable in silico classification-based models as well as in designing new chemical entities, either through fragment-based design or virtual screening. Focus will also be given to some software recently developed to automate and accelerate such types of modeling. Overall, this review may serve as a guideline for researchers to grasp the scope of multitasking computer-aided modeling as a promising in silico tool.

N2O Hydrogenation on Silver Doped Gold Catalysts, a DFT Study.

In this study, the full reaction mechanism for N2O hydrogenation on silver doped Au(210) surfaces was investigated in order to clarify the experimental observations. Density functional theory (DFT) calculations were used to state the most favorable reaction paths for individual steps involved in the N2O hydrogenation. From the DFT results, the activation energy barriers, rate constants and reaction energies for the individual steps were determined, which made it possible to elucidate the most favorable reaction mechanism for the global catalytic process. It was found that the N2O dissociation occurs in surface regions where silver atoms are present, while hydrogen dissociation occurs in pure gold regions of the catalyst or in regions with a low silver content. Likewise, N2O dissociation is the rate determining step of the global process, while water formation from O adatoms double hydrogenation and N2 and H2O desorptions are reaction steps limited by low activation energy barriers, and therefore, the latter are easily carried out. Moreover, water formation occurs in the edges between the regions where hydrogen and N2O are dissociated. Interestingly, a good dispersion of the silver atoms in the surface is necessary to avoid catalyst poison by O adatoms accumulation, which are strongly adsorbed on the surface.

Developing a Multi-target Model to Predict the Activity of Monoamine Oxidase A and B Drugs.

INTRODUCTION: Monoamine oxidase inhibitors (MAOIs) are compounds largely used in the treatment of Parkinson's disease (PD), Alzheimer's disease and other neuropsychiatric disorders since they are closely related to the MAO enzymes activity. The two isoforms of the MAO enzymes, MAO-A and MAO-B, are responsible for the degradation of monoamine neurotransmitters and due to this, relevant efforts have been devoted to finding new compounds with more selectivity and less side effects. One of the most used approaches is based on the use of computational approaches since they are time and money-saving and may allow us to find a more relevant structure-activity relationship. OBJECTIVE: In this manuscript, we will review the most relevant computational approaches aimed at the prediction and development of new MAO inhibitors. Subsequently, we will also introduce a new multitask model aimed at predicting MAO-A and MAO-B inhibitors. METHODS: The QSAR multi-task model herein developed was based on the use of the linear discriminant analysis. This model was developed gathering 5,759 compounds from the public dataset Chembl. The molecular descriptors used was calculated using the Dragon software. Classical statistical tests were performed to check the validity and robustness of the model. RESULTS: The herein proposed model is able to correctly classify all the 5,759 compounds. All the statistical performed tests indicated that this model is robust and reproducible. CONCLUSION: MAOIs are compounds of large interest since they are largely used in the treatment of very serious illness. These inhibitors may lose efficacy and produce severe side effects. Due to this, the development of selective MAO-A or MAO-B inhibitors is crucial for the treatment of these diseases and their effects. The herein proposed multi-target QSAR model may be a relevant tool in the development of new and more selective MAO inhibitors.

Removal of Pb(II) Ion Using PAMAM Dendrimer Grafted Graphene and Graphene Oxide Surfaces: A Molecular Dynamics Study.

The dendrimer polyamidoamine (PAMAM) has been widely applied in environmental applications as adsorbents for wastewater treatment. In this work, molecular dynamics simulations are conducted to understand the effect of dendrimer grafted graphene and graphene oxide on the structural and dynamical properties of the Pb2+ ion. The adsorption capacity of the metal ion is improved significantly, over 60%, using carboxyl terminal groups of a dendrimer molecule grafted on a graphene oxide surface. We examine the self-diffusion coefficient and residence time of Pb2+ ion near graphene and graphene oxide surfaces grafted with PAMAM dendrimers using terminal groups, -COO- and -OH. Further, the potential of mean force is analyzed to understand the role of different surface groups in enhancing the adsorption of the metal ion.

Janus Gold Nanoparticles from Nanodroplets of Alkyl Thiols: A Molecular Dynamics Study.

Janus particles provide an asymmetry in structure, which can impart diverse functionalities leading to immense importance in various applications, ranging from targeted delivery to interfacial phenomena, including catalysis, electronics, and optics. In this work, we present results of a molecular dynamics study of the growth mechanism of coating on gold nanoparticles (AuNPs) from droplets of n-alkyl thiols with different chain lengths (C5 and C11) and terminal groups (CH3 and COOH). The effect of chain lengths and functional groups on the formation of a monolayer of alkyl thiols on AuNPs is investigated. A two-step mechanism, initiated by the binding of the droplet to the nanoparticle surface with a time constant on the order of ∼1 ns, followed by the diffusion-driven growth with a larger time constant (on the order of 100 ns), is shown to capture the growth dynamics of the monolayer. It is observed that the time required for complete wetting increases with an increase in the chain length. Moreover, the monolayer formation is slowed down in the presence of carboxyl groups because of strong hydrogen bonding. The kinetics of the n-alkyl thiols coating on the nanoparticles is found to be independent of the droplet size but carboxyl-terminated thiols spread more with increasing droplet size. Furthermore, different time constants for different chains and functional groups yield Janus coating when two droplets of alkyl thiols with different terminal groups are allowed to form monolayers on the nanoparticle. The Janus balance (ß) for different combinations of alkyl thiols and nanoparticle sizes varies in the range of 0.42-0.71.

A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces.

Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set.

Computational modeling in nanomedicine: prediction of multiple antibacterial profiles of nanoparticles using a quantitative structure-activity relationship perturbation model.

AIMS: We introduce the first quantitative structure-activity relationship (QSAR) perturbation model for probing multiple antibacterial profiles of nanoparticles (NPs) under diverse experimental conditions. MATERIALS & METHODS: The dataset is based on 300 nanoparticles containing dissimilar chemical compositions, sizes, shapes and surface coatings. In general terms, the NPs were tested against different bacteria, by considering several measures of antibacterial activity and diverse assay times. The QSAR perturbation model was created from 69,231 nanoparticle-nanoparticle (NP-NP) pairs, which were randomly generated using a recently reported perturbation theory approach. RESULTS: The model displayed an accuracy rate of approximately 98% for classifying NPs as active or inactive, and a new copper-silver nanoalloy was correctly predicted by this model with consensus accuracy of 77.73%. CONCLUSION: Our QSAR perturbation model can be used as an efficacious tool for the virtual screening of antibacterial nanomaterials.

Density functional theory model study of size and structure effects on water dissociation by platinum nanoparticles.

Size and structure effects on the homolytic water dissociation reaction mediated by Pt nanoparticles have been investigated through density functional theory calculations carried out on a series of cubooctahedral Pt(n) nanoparticles of increasing sizes (n = 13, 19, 38, 55, 79, and 140). Water adsorption energy is not significantly influenced by the nanoparticle size. However, activation energy barrier strongly depends on the particle size. In general, the activation energy barrier increases with nanoparticles size, varying from 0.30 eV for Pt(19) to 0.70 eV for Pt(140). For the largest particle the calculated barrier is very close to that predicted for water dissociation on Pt(111) (0.78 eV) even though the reaction mediated by the Pt nanoparticles involves adsorption sites not present on the extended surface.