Quantitative Structure-Activity Relationship Model for HCVNS5B inhibitors based on an Antlion Optimizer-Adaptive Neuro-Fuzzy Inference System

Exploring gabosine and chlorogentisyl alcohol derivatives from a marine-derived fungus as EcGUS inhibitors with informatic assisted approaches

β-Glucuronidase catalyzes the cleavage of glucuronosyl-O-bonds, whose inhibitors reduce the level of toxic substances present in the intestine caused by anti-cancer and anti-inflammatory therapies. Herein, we presented a new tool, Bioactive Fractions Filtering Platform (BFFP), which is able to reliably discern active candidate node from crude extracts. The source code for the BFFP is available on GitHub (https://github.com/BioGavin/msbff). With the assistant of BFFP, 25 gabosine and chlorogentisyl alcohol derivatives including 19 new compounds were isolated from a marine-derived fungus Epicoccum sp. GST-5. Compounds 7, 9-15 possessed an unusual hybrid skeleton of gabosine and chlorogentisyl alcohol units. Compounds 9-12, 16 and 17 possessed a novel three-membered spiral ring skeleton with one/two gabosine and one/two chlorogentisyl alcohol units. Compound 25 represented new gabosine-derived skeleton possessing an unusual 6/6/6/5/6 condensed ring system. All isolates were evaluated for in vitro E. coli β-glucuronidase (EcGUS) inhibitory activity. 14 Compounds demonstrated superior inhibitory activity (IC₅₀ = 0.24-4.61 μM) to that of standard d-saccharic acid 1,4-lactone (DSL, IC₅₀ = 56.74 ± 4.01 μM). Compounds with chlorogentisyl alcohol moiety, such as 17 (IC₅₀ = 0.24 ± 0.02 μM) and 1 (IC₅₀ = 0.74 ± 0.03 μM), exhibited the most potent inhibitory activity. Furthermore, literature based QSAR profiling by applying PCA and OPLS analysis was carried out to analyze the features of compounds against EcGUS, revealing that the introduction of substituents able to form polar interactions with binding sites of receptor would lead to more active structures.

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids

Absorption has always been an attractive process for removing hydrogen sulfide (H₂S). Posing unique properties and promising removal capacity, ionic liquids (ILs) are potential media for H₂S capture. Engineering design of such absorption process needs accurate measurements or reliable estimation of the H₂S solubility in ILs. Since experimental measurements are time-consuming and expensive, this study utilizes machine learning methods to monitor H₂S solubility in fifteen various ILs accurately. Six robust machine learning methods, including adaptive neuro-fuzzy inference system, least-squares support vector machine (LS-SVM), radial basis function, cascade, multilayer perceptron, and generalized regression neural networks, are implemented/compared. A vast experimental databank comprising 792 datasets was utilized. Temperature, pressure, acentric factor, critical pressure, and critical temperature of investigated ILs are the affecting parameters of our models. Sensitivity and statistical error analysis were utilized to assess the performance and accuracy of the proposed models. The calculated solubility data and the derived models were validated using seven statistical criteria. The obtained results showed that the LS-SVM accurately predicts H₂S solubility in ILs and possesses R², RMSE, MSE, RRSE, RAE, MAE, and AARD of 0.99798, 0.01079, 0.00012, 6.35%, 4.35%, 0.0060, and 4.03, respectively. It was found that the H₂S solubility adversely relates to the temperature and directly depends on the pressure. Furthermore, the combination of OMIM⁺ and Tf₂N^-, i.e., [OMIM][Tf₂N] ionic liquid, is the best choice for H₂S capture among the investigated absorbents. The H₂S solubility in this ionic liquid can reach more than 0.8 in terms of mole fraction.

Modelling in Synthesis and Optimization of Active Vaccinal Components

Cancer is the second leading cause of mortality worldwide, behind heart diseases, accounting for 10 million deaths each year. This study focusses on adenocarcinoma, which is a target of a number of anticancer therapies presently being tested in medical and pharmaceutical studies. The innovative study for a therapeutic vaccine comprises the investigation of gold nanoparticles and their influence on the immune response for the annihilation of cancer cells. The model is intended to be realized using Quantitative-Structure Activity Relationship (QSAR) methods, explicitly artificial neural networks combined with fuzzy rules, to enhance automated properties of neural nets with human perception characteristics. Image processing techniques such as morphological transformations and watershed segmentation are used to extract and calculate certain molecular characteristics from hyperspectral images. The quantification of single-cell properties is one of the key resolutions, representing the treatment efficiency in therapy of colon and rectum cancerous conditions. This was accomplished by using manually counted cells as a reference point for comparing segmentation results. The early findings acquired are conclusive for further study; thus, the extracted features will be used in the feature optimization process first, followed by neural network building of the required model.