Design, synthesis, anti-inflammatory evaluation, and molecular docking studies of novel quinazoline-4(3)-one-2-carbothioamide derivatives.

In this paper, a series of novel quinazoline-4(3)-one-2-carbothioamide derivatives (8a-p) were designed and synthesized the Wilgerodt-Kindler reaction between 2-methylquinazoline-4-one 10 and amines using S/DMSO as the oxidizing system. Their characteristics were confirmed by IR, NMR, HRMS spectra, and their melting point. These novel derivatives (8a-p) were evaluated for their anti-inflammatory activity by inhibiting NO production in lipopolysaccharide (LPS)-activated RAW 264.

Searching for potential acetylcholinesterase inhibitors: a combined approach of multi-step similarity search, machine learning and molecular dynamics simulations.

Targeting acetylcholinesterase is one of the most important strategies for developing therapeutics against Alzheimer's disease. In this work, we have employed a new approach that combines machine learning models, a multi-step similarity search of the PubChem library and molecular dynamics simulations to investigate potential inhibitors for acetylcholinesterase. Our search strategy has been shown to significantly enrich the set of compounds with strong predicted binding affinity to acetylcholinesterase.

MedChemExpress compounds prevent neuraminidase N1 physics- and knowledge-based methods.

Influenza A viruses spread out worldwide, causing several global concerns. Hence, discovering neuraminidase inhibitors to prevent the influenza A virus is of great interest. In this work, a machine learning model was employed to evaluate the ligand-binding affinity of 10 000 compounds from the MedChemExpress (MCE) database for inhibiting neuraminidase.

Silver nanoparticles alter the dimerization of Aβ studied by REMD simulations.

The aggregation of amyloid beta (Aβ) peptides is associated with the development of Alzheimer's disease (AD). However, there has been a growing belief that the oligomerization of Aβ species in different environments has a neurotoxic effect on the patient's brain, causing damage. It is necessary to comprehend the compositions of Aβ oligomers in order to develop medications that may effectively inhibit these neurotoxic forms that affect the nervous system of AD patients.

Perceived intravaginal ejaculation latency time: The diagnosis of premature ejaculation among Vietnamese men.

Introduction: Premature ejaculation (PE) is a prevalent sexual dysfunction in men that greatly affects their quality of life. In PE, the duration of sexual performance is considered an important aspect. However, a self-estimated value of intravaginal ejaculation latency time (perceived IELT, PIELT) as a criterion for diagnosis has not been specified.

Upgrading nirmatrelvir to inhibit SARS-CoV-2 Mpro via DeepFrag and free energy calculations.

The first oral drug for the treatment of COVID-19, Paxlovid, has been authorized; however, nirmatrelvir, a major component of the drug, is reported to be associated with some side effects. Moreover, the appearance of many novel variants raises concerns about drug resistance, and designing new potent inhibitors to prevent viral replication is thus urgent. In this context, using a hybrid approach combining machine learning (ML) and free energy simulations, 6 compounds obtained by modifying nirmatrelvir were proposed to bind strongly to SARS-CoV-2 Mpro.

Plant Secondary Metabolites on Efflux-Mediated Antibiotic Resistant : Potential of Herbal-Derived Efflux Pump Inhibitors.

During the process of adapting to metal contamination, plants produce secondary metabolites that have the potential to modulate multidrug-resistant (MDR) phenotypes; this is achieved by inhibiting the activity of efflux pumps to reduce the minimum inhibitory concentrations (MICs) of antimicrobial substrates. Our study evaluated the effect of secondary metabolites of belowground parts of L. and two metal-tolerant plants from northern Vietnam, on six antibiotic-resistant strains possessing efflux pump resistance mechanisms that were isolated from soil and clinical samples.

Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds.

To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database.

Correction: Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro physics- and knowledge-based approaches.

Correction for 'Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro physics- and knowledge-based approaches' by Son Tung Ngo , , 2022, https://doi.org/10.1039/d2cp04476e.

Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro physics- and knowledge-based approaches.

Computational approaches, including physics- and knowledge-based methods, have commonly been used to determine the ligand-binding affinity toward SARS-CoV-2 main protease (Mpro or 3CLpro). Strong binding ligands can thus be suggested as potential inhibitors for blocking the biological activity of the protease. In this context, this paper aims to provide a short review of computational approaches that have recently been applied in the search for inhibitor candidates of Mpro.

Calculation of binding affinity of JAK1 inhibitors via accurately computational estimation.

Janus kinase 1 (JAK1) is a tyrosine kinase that is involved in the initiation of responses to a number of different cytokine receptor families. The JAK1-dependent pathway is a therapeutic target, and several JAK inhibitors have been developed thanks to intensive research. However, since the ATP binding sites of JAK family members are quite alike, JAK1 inhibitors can thus be less selective, resulting in unanticipated adverse effects.

Anti-multiple myeloma potential of resynthesized belinostat derivatives: an experimental study on cytotoxic activity, drug combination, and docking studies.

Multiple myeloma is a deadly cancer that is a complex and multifactorial disease. In the present study, 12 belinostat derivatives (four resynthesized and eight new), HDAC inhibitors, were resynthesized either Knoevenagel condensation, or Wittig reaction, or Heck reaction. Then an evaluation of the antiproliferative activities against myeloma cells MOPC-315 was carried out.

Searching for AChE inhibitors from natural compounds by using machine learning and atomistic simulations.

Acetylcholinesterase (AChE) is one of the most important drug targets for Alzheimer's disease treatment. In this work, a combined approach involving machine-learning (ML) model and atomistic simulations was established to predict the ligand-binding affinity to AChE of the natural compounds from VIETHERB database. The trained ML model was first utilized to rapidly and accurately screen the natural compound database for potential AChE inhibitors.

Molecular design of anticancer drugs from marine fungi derivatives.

Heat shock protein 90 (Hsp90) is one of the most potential targets in cancer therapy. We have demonstrated using a combination of molecular docking and fast pulling of ligand (FPL) simulations that marine fungi derivatives can be possible inhibitors, preventing the biological activity of Hsp90. The computational approaches were validated and compared with previous experiments.

Computational estimation of potential inhibitors from known drugs against the main protease of SARS-CoV-2.

The coronavirus disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide recently, leading to global social and economic disruption. Although the emergently approved vaccine programs against SARS-CoV-2 have been rolled out globally, the number of COVID-19 daily cases and deaths has remained significantly high. Here, we attempt to computationally screen for possible medications for COVID-19 rapidly estimating the highly potential inhibitors from an FDA-approved drug database against the main protease (Mpro) of SARS-CoV-2.

Effects of antibiotics on Vietnam koi, Anabas testudineus, exposed to Aeromonas dhakensis as a co-infection.

The dietary effects of antibiotics on aquatic disease is circumstantial and has not been investigated under infections. the efficacy of erythromycin, after 10 days in use and 10 days off, on the survival and infection rate of (Anabas testudineus) after co-infection with antibiotic-resistant Aeromonas dhakensis (isolate NV5M or V7L). The mortality rate observed in non-medicated groups of co-infected fish (93.

Umbrella Sampling-Based Method to Compute Ligand-Binding Affinity.

Many proteins have a solvent-exposed binding cleft, which permits their inhibitors to bind and unbind without significant protein conformation transforms. The binding/unbinding pathways of these protein-inhibitor complexes can be rather straightforwardly sampled by using umbrella sampling (US) simulation methods. During a US simulation, the C atoms of the protein are restrained via a harmonic force.

Improving ligand-ranking of AutoDock Vina by changing the empirical parameters.

AutoDock Vina (Vina) achieved a very high docking-success rate, , but give a rather low correlation coefficient, , for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters.

Cytotoxic and α-Glucosidase Inhibitory Xanthones from Garcinia mckeaniana Leaves and Molecular Docking Study.

A new racemic xanthone, garmckeanin A (1), and eight known analogs 2-9 were isolated from the ethyl acetate (AcOEt) extract of the Vietnamese Garcinia mckeaniana leaves. Their structures were determined by MS and NMR spectral analyses and compared with the literature. The AcOEt extract showed good cytotoxicity against cancer cell lines KB, Lu, Hep-G2 and MCF7, with IC values of 5.

Marine derivatives prevent MUS81 studies.

The winged-helix domain of the methyl methanesulfonate and ultraviolet-sensitive 81 (MUS81) is a potential cancer drug target. In this context, marine fungi compounds were indicated to be able to prevent MUS81 structure via atomistic simulations. Eight compounds such as (), (), (), (), (), (), () and () were indicated that they are able to prevent the conformation of MUS81 via forming a strong binding affinity to the enzyme via perturbation approach.

Benchmark of Popular Free Energy Approaches Revealing the Inhibitors Binding to SARS-CoV-2 Mpro.

The COVID-19 pandemic has killed millions of people worldwide since its outbreak in December 2019. The pandemic is caused by the SARS-CoV-2 virus whose main protease (Mpro) is a promising drug target since it plays a key role in viral proliferation and replication. Currently, developing an effective therapy is an urgent task, which requires accurately estimating the ligand-binding free energy to SARS-CoV-2 Mpro.

Antioxidant and Anticancer Properties of Functionalized Ferrocene with Hydroxycinnamate Derivatives-An Integrated Experimental and Theoretical Study.

Two ferrocenyl derivatives, and , were synthesized by a condensation reaction between the amino ferrocene and hydroxycinnamic acids, that is, caffeic acid () and ferulic acid (). The structures and purity of all compounds were characterized by H- and C NMR spectroscopies, Mass spectrometry (MS), and elemental analysis. The antioxidant properties of and and of its ligand were studied for free radical scavenging activity toward DPPH, superoxide anion (O), NO, and ABTS by UV-vis and electron spin resonance spectroscopies.

Rapid prediction of possible inhibitors for SARS-CoV-2 main protease using docking and FPL simulations.

Originating for the first time in Wuhan, China, the outbreak of SARS-CoV-2 has caused a serious global health issue. An effective treatment for SARS-CoV-2 is still unavailable. Therefore, in this study, we have tried to predict a list of potential inhibitors for SARS-CoV-2 main protease (Mpro) using a combination of molecular docking and fast pulling of ligand (FPL) simulations.