Unveiling structural determinants for FXR antagonism in 1,3,4-trisubstituted-Pyrazol amide derivatives: A multi-scale in silico modelling approach.

Non-alcoholic fatty liver disease (NAFLD) is a growing global health concern due to its potential to progress into severe liver diseases. Targeting the bile acid receptor FXR has emerged as a promising strategy for managing NAFLD. Building upon our previous research on FXR partial agonism, the present study investigates a series of 1,3,4-trisubstituted-pyrazol amide derivatives as FXR antagonists, aiming to delineate the structural features for antagonism.

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.

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.

Systematic Development of Vanadium Catalysts for Sustainable Epoxidation of Small Alkenes and Allylic Alcohols.

The catalytic epoxidation of small alkenes and allylic alcohols includes a wide range of valuable chemical applications, with many works describing vanadium complexes as suitable catalysts towards sustainable process chemistry. But, given the complexity of these mechanisms, it is not always easy to sort out efficient examples for streamlining sustainable processes and tuning product optimization. In this review, we provide an update on major works of tunable vanadium-catalyzed epoxidations, with a focus on sustainable optimization routes.

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.

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).

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.

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.

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 tools such as 2D-quantitative structure-activity relationship (2D-QSAR), 3D-QSAR, structure-based pharmacophore modeling, molecular docking and molecular dynamics simulations.

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.

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.

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.

NO Hydrogenation on Silver Doped Gold Catalysts, a DFT Study.

In this study, the full reaction mechanism for NO 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 NO 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.

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.

Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents.

Two isoforms of extracellular regulated kinase (ERK), namely ERK-1 and ERK-2, are associated with several cellular processes, the aberration of which leads to cancer. The ERK-1/2 inhibitors are thus considered as potential agents for cancer therapy. Multitarget quantitative structure-activity relationship (mt-QSAR) models based on the Box-Jenkins approach were developed with a dataset containing 6400 ERK inhibitors assayed under different experimental conditions.

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 Pb 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.

Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis.

Background: Preeclampsia is a multifactorial disease with unknown pathogenesis. Even when recent studies explored this disease using several bioinformatics tools, the main objective was not directed to pathogenesis. Additionally, consensus prioritization was proved to be highly efficient in the recognition of genes-disease association.

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 (CH and COOH). The effect of chain lengths and functional groups on the formation of a monolayer of alkyl thiols on AuNPs is investigated.

Advanced In Silico Approaches for Drug Discovery: Mining Information from Multiple Biological and Chemical Data Through mtk- QSBER and pt-QSPR Strategies.

The last decade has been seeing an increase of public-private partnerships in drug discovery, mostly driven by factors such as the decline in productivity, the high costs, time, and resources needed, along with the requirements of regulatory agencies. In this context, traditional computer-aided drug discovery techniques have been playing an important role, enabling the identification of new molecular entities at early stages. However, recent advances in chemoinformatics and systems pharmacology, alongside with a growing body of high quality, publicly accessible medicinal chemistry data, have led to the emergence of novel in silico approaches.

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.

Classification of natural estrogen-like isoflavonoids and diphenolics by QSAR tools.

This work reports a detailed study of the ability of linear and non-linear classification methods to estimate the estrogenic activities of a series of 55 natural estrogen-like isoflavonoid and diphenolic compounds. In doing so, we examined the use of linear discriminant analysis (LDA) and nonlinear support vector machines (SVMs) techniques along with feature selection algorithms. The structural characteristics of each of the studied compounds were calculated from the optimized molecular geometries.

Solvent accessible surface area-based hot-spot detection methods for protein-protein and protein-nucleic acid interfaces.

Due to the importance of hot-spots (HS) detection and the efficiency of computational methodologies, several HS detecting approaches have been developed. The current paper presents new models to predict HS for protein-protein and protein-nucleic acid interactions with better statistics compared with the ones currently reported in literature. These models are based on solvent accessible surface area (SASA) and genetic conservation features subjected to simple Bayes networks (protein-protein systems) and a more complex multi-objective genetic algorithm-support vector machine algorithms (protein-nucleic acid systems).

Computer-aided discovery in antimicrobial research: In silico model for virtual screening of potent and safe anti-pseudomonas agents.

Resistance of bacteria to current antibiotics is an alarming health problem. In this sense, Pseudomonas represents a genus of Gram-negative pathogens, which has emerged as one of the most dangerous species causing nosocomial infections. Despite the effort of the scientific community, drug resistant strains of bacteria belonging to Pseudomonas spp.

Multitasking models for quantitative structure-biological effect relationships: current status and future perspectives to speed up drug discovery.

Introduction: Drug discovery is the process of designing new candidate medications for the treatment of diseases. Over many years, drugs have been identified serendipitously. Nowadays, chemoinformatics has emerged as a great ally, helping to rationalize drug discovery.

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.