Synthesis and Quantitative Structure-activity Relationships Study for Arylpropenamide Derivatives as Inhibitors of Hepatitis B Virus Replication

Synthesis and Biological Evaluation of Novel Fusidic Acid Derivatives as Two-in-One Agent with Potent Antibacterial and Anti-Inflammatory Activity

Fusidic acid (FA), a narrow-spectrum antibiotics, is highly sensitive to various Gram-positive cocci associated with skin infections. It has outstanding antibacterial effects against certain Gram-positive bacteria whilst no cross-resistance with other antibiotics. Two series of FA derivatives were synthesized and their antibacterial activities were tested. A new aromatic side-chain analog, FA-15 exhibited good antibacterial activity with MIC values in the range of 0.781–1.563 µM against three strains of Staphylococcus spp. Furthermore, through the assessment by the kinetic assay, similar characteristics of bacteriostasis by FA and its aromatic derivatives were observed. In addition, anti-inflammatory activities of FA and its aromatic derivatives were evaluated by using a 12-O-tetradecanoylphorbol-13-acetate (TPA) induced mouse ear edema model. The results also indicated that FA and its aromatic derivatives effectively reduced TPA-induced ear edema in a dose-dependent manner. Following, multiform computerized simulation, including homology modeling, molecular docking, molecular dynamic simulation and QSAR was conducted to clarify the mechanism and regularity of activities. Overall, the present work gave vital clues about structural modifications and has profound significance in deeply scouting for bioactive potentials of FA and its derivatives.

Theoretical and experimental studies on the fluorescence properties of aluminum(III), cadmium(II), zinc(II), and copper(II) complexes of substituted 8-hydroxyquinoline

Fifty-five 8-hydroxyquinoline (8-HQ) derivatives are synthesized and the corresponding aluminum(III), cadmium(II), copper(II), and zinc(II) metal complexes are prepared and their fluorescent activities are evaluated. The results indicate that the aluminum complexes have the best fluorescence properties, followed by zinc and cadmium complexes, while almost no fluorescence is observed with the copper complexes. The relationship between the fluorescence properties and complex structure is summarized and a predictive three-dimensional quantitative structure–property relationship model is established using the multifit molecular alignment rule of Sybyl program. With the introduction of groups at the C-2 position or electron-withdrawing groups to the 8-hydroxyquinoline framework, fluorescence wavelength blue shifts are observed with the zinc, aluminum, and cadmium complexes. At the same time, a red shift of the fluorescence emission wavelength is detected for the aluminum and zinc complexes when C-5 of 8-hydroxyquinoline was substituted with halogens or a methyl group. Moreover, the zinc, cadmium, and aluminum complexes with 2,4-dimethyl substituents on the 8-hydroxyquinoline all show good fluorescence properties. The highest occupied molecular orbital and lowest unoccupied molecular orbital energies of the complexes are also calculated and the fluorescence properties of the metal complexes are analyzed from the viewpoint of molecular orbitals.

Prediction of Small Molecule Inhibitors Targeting the Severe Acute Respiratory Syndrome Coronavirus-2 RNA-dependent RNA Polymerase

The current COVID-19 outbreak warrants the design and development of novel anti-COVID therapeutics. Using a combination of bioinformatics and computational tools, we modelled the 3D structure of the RdRp (RNA-dependent RNA polymerase) of SARS-CoV2 (severe acute respiratory syndrome coronavirus-2) and predicted its probable GTP binding pocket in the active site. GTP is crucial for the formation of the initiation complex during RNA replication. This site was computationally targeted using a number of small molecule inhibitors of the hepatitis C RNA polymerase reported previously. Further optimizations suggested a lead molecule that may prove fruitful in the development of potent inhibitors against the RdRp of SARS-CoV2.

Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds

Retroviral infections, such as HIV, are, until now, diseases with no cure. Medicine and pharmaceutical chemistry need and consider it a huge goal to define target proteins of new antiretroviral compounds. ChEMBL manages Big Data features with a complex data set, which is hard to organize. This makes information difficult to analyze due to a big number of characteristics described in order to predict new drug candidates for retroviral infections. For this reason, we propose to develop a new predictive model combining perturbation theory (PT) bases and machine learning (ML) modeling to create a new tool that can take advantage of all the available information. The PTML model proposed in this work for the ChEMBL data set preclinical experimental assays for antiretroviral compounds consists of a linear equation with four variables. The PT operators used are founded on multicondition moving averages, combining different features and simplifying the difficulty to manage all data. More than 140 000 preclinical assays for 56 105 compounds with different characteristics or experimental conditions have been carried out and can be found in ChEMBL database, covering combinations with 359 biological activity parameters (c₀), 55 protein accessions (c₁), 83 cell lines (c₂), 64 organisms of assay (c₃), and 773 subtypes or strains. We have included 150 148 preclinical experimental assays for HIV virus, 1188 for HTLV virus, 84 for simian immunodeficiency virus, 370 for murine leukemia virus, 119 for Rous sarcoma virus, 1581 for MMTV, etc. We also included 5277 assays for hepatitis B virus. The developed PTML model reached considerable values in sensibility (73.05% for training and 73.10% for validation), specificity (86.61% for training and 87.17% for validation), and accuracy (75.84% for training and 75.98% for validation). We also compared alternative PTML models with different PT operators such as covariance, moments, and exponential terms. Finally, we made a comparison between literature ML models with our PTML model and also artificial neural network (ANN) nonlinear models. We conclude that this PTML model is the first one to consider multiple characteristics of preclinical experimental antiretroviral assays combined, generating a simple, useful, and adaptable instrument, which could reduce time and costs in antiretroviral drugs research.

Synthesis, Spectra, and Theoretical Investigations of 1,3,5-Triazines Compounds as Ultraviolet Rays Absorber Based on Time-Dependent Density Functional Calculations and three-Dimensional Quantitative Structure-Property Relationship

A series of 1,3,5-triazines were synthesized and their UV absorption properties were tested. The computational chemistry methods were used to construct quantitative structure-property relationship (QSPR), which was used to computer aided design of new 1,3,5-triazines ultraviolet rays absorber compounds. The experimental UV absorption data are in good agreement with those predicted data using the Time-dependent density functional theory (TD-DFT) [B3LYP/6-311 + G(d,p)]. A suitable forecasting model (R > 0.8, P < 0.0001) was revealed. Predictive three-dimensional quantitative structure-property relationship (3D-QSPR) model was established using multifit molecular alignment rule of Sybyl program, which conclusion is consistent with the TD-DFT calculation. The exceptional photostability mechanism of such ultraviolet rays absorber compounds was studied and confirmed as principally banked upon their ability to undergo excited-state deactivation via an ultrafast excited-state proton transfer (ESIPT). The intramolecular hydrogen bond (IMHB) of 1,3,5-triazines compounds is the basis for the excited state proton transfer, which was explored by IR spectroscopy, UV spectra, structural and energetic aspects of different conformers and frontier molecular orbitals analysis.