In silico screening of Fyn kinase inhibitors using classification-based QSAR model, molecular docking, molecular dynamics and ADME study.

This study aimed to use a computational approach that combined the classification-based QSAR model, molecular docking, ADME studies, and molecular dynamics (MD) to identify potential inhibitors of Fyn kinase. First, a robust classification model was developed from a dataset of 1,078 compounds with known Fyn kinase inhibitory activity, using the XGBoost algorithm. After that, molecular docking was performed between potential compounds identified from the QSAR model and Fyn kinase to assess their binding strengths and key interactions, followed by MD simulations. ADME studies were additionally conducted to preliminarily evaluate the pharmacokinetics and drug-like characteristics of these compounds. The results showed that our obtained model exhibited good predictive performance with an accuracy of 0.95 on the test set, affirming its reliability in identifying potent Fyn kinase inhibitors. Through the application of this model in conjunction with molecular docking and ADME studies, nine compounds were identified as potential Fyn kinase inhibitors, including 208 (ZINC70708110), 728 (ZINC8792432), 734 (ZINC8792187), 736 (ZINC8792350), 738 (ZINC8792286), 739 (ZINC8792309), 817 (ZINC33901069), 852 (ZINC20759145), and 1227 (ZINC100006936). MD simulations further demonstrated that the four most promising compounds, 728, 734, 736, and 852 exhibited stable binding with Fyn kinase during the simulation process. Additionally, a web-based platform ( https://fynkinase.streamlit.app/ ) has been developed to streamline the screening process. This platform enables users to predict the activity of their substances of interest on Fyn kinase from their SMILES, using our classification-based QSAR model and molecular docking.

Identification of potential FAK inhibitors using mol2vec molecular descriptor-based QSAR, molecular docking, ADMET study, and molecular dynamics simulation.

This study aims to identify potential focal adhesion kinase (FAK) inhibitors through an integrated computational approach, combining mol2vec descriptor-based QSAR, molecular docking, ADMET study, and molecular dynamics simulation. A dataset of 437 compounds with known FAK inhibitory activities was used to develop QSAR models using machine learning algorithms combined with mol2vec descriptors. Subsequently, the most promising compounds were subjected to molecular docking against FAK to evaluate their binding affinities and key interactions. ADMET study and molecular dynamics simulation were also employed to investigate the pharmacokinetic, drug-like properties, and the stability of the protein-ligand complexes. The results showed that the mol2vec descriptor-based QSAR model established by support vector regression demonstrated good predictive performance (R2 = 0.813, RMSE = 0.453, MAE = 0.263 in case of training set, and R2 = 0.729, RMSE = 0.635, MAE = 0.477 in case of test set), indicating their reliability in identifying potent FAK inhibitors. Using this QSAR model and molecular docking, compound 21 (ZINC000004523722) was identified as the most potential compound, with predicted logIC50 value and binding energy of 2.59 and - 9.3 kcal/mol, respectively. The results of molecular dynamics simulation and ADMET study also further suggested its potential as a promising drug candidate. However, because our research was merely theoretical, additional in vitro and in vivo studies are required for the verification of these results.

Deep eutectic solvent combined with soybean as an efficient approach to enhance the content of apigenin in the Chrysanthemum indicum L. extract.

Our research aimed to cost-effectively enhance apigenin content in Chrysanthemum indicum L. extract using soybeans combined with a deep eutectic solvent. First, various deep eutectic solvents were investigated for the extraction of apigenin, followed by soybean treatment to increase aglycon levels. Combining single factor experiments with response surface methodology and optimization algorithms (genetic algorithm and particle swarm optimization), the optimal conditions were also determined. The results revealed that choline chloride-propylene glycol emerged as the optimal solvent. The optimized treatment conditions involved a temperature of 54 °C, a time of 2 h, and the addition of 3 mL of soybean extract, yielding an apigenin content of 3.380 ± 0.031 mg/g - a remarkable eightfold increase compared to the initial extract. The computational study suggested that the deep eutectic solvent may play an important role in stabilizing ß-glucosidase in soybeans. However, further research is needed to scale up and fully elucidate soybean's mechanism.

Towards safer and efficient formulations: Machine learning approaches to predict drug-excipient compatibility.

Predicting drug-excipient compatibility is a critical aspect of pharmaceutical formulation design. In this study, we introduced an innovative approach that leverages machine learning techniques to improve the accuracy of drug-excipient compatibility predictions. Mol2vec and 2D molecular descriptors combined with the stacking technique were used to improve the performance of the model. This approach achieved a significant advancement in the predictive capacity as demonstrated by the accuracy, precision, recall, AUC, and MCC of 0.98, 0.87, 0.88, 0.93 and 0.86, respectively. Using the DE-INTERACT model as the benchmark, our stacking model could remarkably detect drug-excipient incompatibility in 10/12 tested cases, while DE-INTERACT managed to recognize only 3 out of 12 incompatibility cases in the validation experiments. To ensure user accessibility, the trained model was deployed to a user-friendly web platform (URL: https://decompatibility.streamlit.app/). This interactive interface accommodated inputs through various types, including names, PubChem CID, or SMILES strings. It promptly generated compatibility predictions alongside corresponding probability scores. However, the continual refinement of model performance is crucial before applying this model in practice.

Antimicrobial, Cytotoxic, and α-Glucosidase Inhibitory Activities of Ethanol Extract and Chemical Constituents Isolated from Homotrigona apicalis Propolis-In Vitro and Molecular Docking Studies.

The chemical investigation of Homotrigona apicalis propolis collected in Binh Dinh province, Vietnam, led to the isolation of nine compounds, including four sesquiterpenes: spathulenol (1), 1αH,5ßH-aromandendrane-4ß,10α-diol (2), 1ß,6α-dihydroxy-4(15)-eudesmene (3), and 1ßH,5ßH-aromandendrane-4α,10ß-diol (4); three triterpenes: acetyl oleanolic acid (5), 3α-hydroxytirucalla-8,24-dien-21-oic acid (6), and ursolic acid (7); and two xanthones: cochinchinone A (8) and α-mangostin (9). Sesquiterpens 1-4 and triterpene 6 were isolated for the first time from stingless bee propolis. Plants in the Cratoxylum and Aglaia genus were suggested as resin sources of the propolis sample. In the antibacterial activity evaluation, the EtOH extract only showed moderate activity on S. aureus, while the isolated compounds 7-9 showed good antibacterial activity, with IC50 values of 0.56 to 17.33 µg/mL. The EtOH extract displayed selective cytotoxicity against the A-549 cancer cell line, with IC50 values of 22.82 ± 0.86 µg/mL, and the xanthones 8 and 9 exhibited good activity against the KB, HepG-2, and A-549 cancer cell lines, with IC50 values ranging from 7.55 ± 0.25 µg/mL to 29.27 ± 2.07 µg/mL. The cytotoxic effects of xanthones 8 and 9 were determined by the inhibition of the EGFR and HER2 pathways using a molecular docking study. Compounds 8 and 9 displayed strong binding affinity with EFGR and HER2, with values of -9.3 to -9.9 kcal/mol. Compounds 5, 8, and 9 showed potential α-glucosidase inhibitory activities, which were further confirmed by computational studies. The binding energies of compounds 5, 8, and 9 were lower than that of arcabose.

Green extraction of apigenin and luteolin from celery seed using deep eutectic solvent.

In recent years, deep eutectic solvent (DES) has attracted a great deal of attention as an environmentally friendly solvent and could be used as an alternative to conventional solvents. In this study, 34 choline and betaine-based deep eutectic solvents were prepared and investigated the ability to extract apigenin and luteolin from the celery seed. The results showed that DES composed of betaine hydrochloride and propylene glycol had the best extraction efficiency. Using one factor at a time combined with response surface methodology, the optimal conditions of extraction were determined as follows: time of extraction: 19 min, extraction temperature: 75 °C, the water content in solvent: 40% (w/w). Antisolvent (water) combined with distillation was proposed as an efficient method to recover apigenin and luteolin from the DES extract. The relationship between components of DES and solubility of apigenin, luteolin was also established which was the starting point for the prediction of extractability of DES. Molecular dynamics revealed that betaine hydrochloride and propylene glycol could interact with each other by the formation of two types of hydrogen bond while water molecules might play an important role in the ability to dissolve apigenin and luteolin of DES.

In vitro screening of siderophore-producing rice root endophytic bacteria from up-land paddies in north-western Vietnam for plant growth-promoting activities

Aims@#Endophytic bacteria (EB) living inside plant tissues possess different beneficial traits including siderophore production and other plant growth-promoting (PGP) activities. Siderophore-producing EB promote host plant growth by secreting ferrum in iron-deficient conditions. This study screened 19 siderophore producers in vitro, isolated from upland rice roots grown in mountain farms of Tung Village, Nậm Có Commune, Mù Cang Chải District, Yên Bái Province, Vietnam, for PGP traits, including phosphate solubilisation, indole-3-acetic acid (IAA), ammonia, gelatinase, amylase and catalase production.@*Methodology and results@#The bacteria were identified by Matrix assisted Laser Desorption Ionization Time of Flight mass spectrometry (MALDI-TOF MS). All 19 isolates were identified as genera Pseudomonas, Enterobacter, Pantoe, Bacillus, Burkholderia, Staphylococcus, Ralstonia and Cronotacter. The isolates produced catalase and ammonia. The amount of ammonia ranged from 60.74 ± 0.14 to 466.72 ± 0.18 mg/L. Out of the 19 siderophore producers, 17 (89.47%) were able to solubilise phosphate with solubilisation index (PSI) ranging from 1.12 ± 0.07 to 2.14 ± 0.15. The qualitative assays identified 12 isolates (63.15%) positive for IAA production with a tryptophan concentration of 5 mM, whereas 15 (78.94%) and 17 (89.47%) isolates were positive for gelatin and starch hydrolysis, respectively. Especially, 7 isolates were found to be positive for all tested assays in vitro including Pseudomonas rhodesiae (NC2), Enterobacter asburiae (NC50), Pantoea ananatis (NC63), Bacillus cereus (NC64), Burkholderia cenocepacia (NC110), Staphylococcus sciuri (NC112) and Ralstonia pickettii (NC122).@*Conclusion, significance and impact of study@#This study serves as crucial findings of multi-trait plant growth-promoting endophytic bacteria isolated from upland rice root in north-western Vietnam. The seven potential isolates positive for all tested assays could be effective PGP bacteria for bio-inoculants.

Spectrum-effect relationship between high-performance thin-layer chromatography data and xanthine oxidase inhibitory activity of celery seed extract.

Celery seeds are medicinal herbs used for the prevention and treatment of gout as these have the ability to inhibit the activity of xanthine oxidase and reduce the concentration of serum uric acid. In this study, the relationship between xanthine oxidase inhibitory effects and high-performance thin-layer chromatography data of celery seed extracts was established using multilayer neural network (MNN) in combination with principal component analysis (PCA). The constructed MNN-PCA model was stable and had accurate prediction ability with coefficient of determination = 0.9998, leave-one-out coefficient = 0.7371, root mean square error = 0.0025, and mean absolute deviation = 0.0019 for the training set and coefficient of determination = 0.8124, root mean square error = 0.0784, and mean absolute deviation = 0.0645 for the test set. This model can be used to identify the main compounds related to the xanthine oxidase inhibitory effect of celery seed extract. These results can be applied not only to celery extract but also to other herbal medicines.