Synthesis, DFT Analysis, and Evaluation of Antibacterial and Antioxidant Activities of Sulfathiazole Derivatives Combined with In Silico Molecular Docking and ADMET Predictions

Binding Mechanism and Surface-Enhanced Raman Scattering of the Antimicrobial Sulfathiazole on Gold Nanoparticles

A combined study using the surface-enhanced Raman scattering (SERS) technique and quantum chemical calculations was carried out to elucidate the adsorption behavior of sulfathiazole, an antibiotic drug, on gold nanoparticles. The tetrahedral Au20 cluster was used as a simple model to mimic a nanostructured gold surface. Computations using density functional theory with the PBE functional were performed in both the gas phase and aqueous medium using a continuum model. The drug is found to bind to the Au metals via the nitrogen of the thiazole ring. The interaction is also partially stabilized by the ring-surface π coupling rather than a sideway adsorption as previously proposed. In an aqueous solution, the drug molecule mainly exists as a deprotonated form, which gives rise to a much greater affinity toward Au nanoparticles as compared to the neutral forms. The drug adsorption further induces a significant alteration on the energy gap of the gold cluster Aun, which could result in an electrical noise. Notable SERS signals below 1600 cm-1, which result from a coupling of several vibrations including the ring breathing, C-C stretching, and N-H bending, could be employed for both qualitative and quantitative detection and assessment of sulfathiazole at trace concentrations.

New Heterocyclic Compounds from Oxazol-5(4H)-one and 1,2,4-Triazin-6(5H)-one Classes: Synthesis, Characterization and Toxicity Evaluation

This paper describes the synthesis of new heterocycles from oxazol-5(4H)-one and 1,2,4-triazin-6(5H)-one classes containing a phenyl-/4-bromophenylsulfonylphenyl moiety. The oxazol-5(4H)-ones were obtained via condensation of 2-(4-(4-X-phenylsulfonyl)benzamido)acetic acids with benzaldehyde/4-fluorobenzaldehyde in acetic anhydride and in the presence of sodium acetate. The reaction of oxazolones with phenylhydrazine, in acetic acid and sodium acetate, yielded the corresponding 1,2,4-triazin-6(5H)-ones. The structures of the compounds were confirmed using spectral (FT-IR, ¹H-NMR, ¹³C-NMR, MS) and elemental analysis. The toxicity of the compounds was evaluated on Daphnia magna Straus crustaceans and on the budding yeast Saccharomyces cerevisiae. The results indicate that both the heterocyclic nucleus and halogen atoms significantly influenced the toxicity against D. magna, with the oxazolones being less toxic than triazinones. The halogen-free oxazolone had the lowest toxicity, and the fluorine-containing triazinone exhibited the highest toxicity. The compounds showed low toxicity against yeast cells, apparently due to the activity of plasma membrane multidrug transporters Pdr5 and Snq2. The predictive analyses indicated an antiproliferative effect as the most probable biological action. The PASS prediction and CHEMBL similarity studies show evidence that the compounds could inhibit certain relevant oncological protein kinases. These results correlated with toxicity assays suggest that halogen-free oxazolone could be a good candidate for future anticancer investigations.

A machine learning-based approach to ERα bioactivity and drug ADMET prediction

By predicting ERα bioactivity and mining the potential relationship between Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) attributes in drug research and development, the development efficiency of specific drugs for breast cancer will be effectively improved and the misjudgment rate of R&D personnel will be reduced. The quantitative prediction model of ERα bioactivity and classification prediction model of Absorption, Distribution, Metabolism, Excretion, Toxicity properties were constructed. The prediction results of ERα bioactivity were compared by XGBoot, Light GBM, Random Forest and MLP neural network. Two models with high prediction accuracy were selected and fused to obtain ERα bioactivity prediction model from Mean absolute error (MAE), mean squared error (MSE) and R2. The data were further subjected to model-based feature selection and FDR/FPR-based feature selection, respectively, and the results were placed in a voting machine to obtain Absorption, Distribution, Metabolism, Excretion, Toxicity classification prediction model. In this study, 430 molecular descriptors were removed, and finally 20 molecular descriptors with the most significant effect on biological activity obtained by the dual feature screening combined optimization method were used to establish a compound molecular descriptor prediction model for ERα biological activity, and further classification and prediction of the Absorption, Distribution, Metabolism, Excretion, Toxicity properties of the drugs were made. Eighty variables were selected by the model ExtraTreesClassifier Classifie, and 40 variables were selected by the model GradientBoostingClassifier to complete the model-based feature selection. At the same time, the feature selection method based on FDR/FPR is also selected, and the three classification models obtained by the two methods are placed into the voting machine to obtain the final model. The experimental results showed that the model's evaluation indexes and roc diagram were excellent and could accurately predict ERα bioactivity and Absorption, Distribution, Metabolism, Excretion, Toxicity properties. The model constructed in this study has high accuracy, fast convergence and robustness, has a very high accuracy for Absorption, Distribution, Metabolism, Excretion, Toxicity and ERα classification prediction, has bright prospects in the biopharmaceutical field, and is an important method for energy conservation and yield increase in the future.

Clustering Analysis, Structure Fingerprint Analysis, and Quantum Chemical Calculations of Compounds from Essential Oils of Sunflower (Helianthus annuus L.) Receptacles

Sunflower (Helianthus annuus L.) is an appropriate crop for current new patterns of green agriculture, so it is important to change sunflower receptacles from waste to useful resource. However, there is limited knowledge on the functions of compounds from the essential oils of sunflower receptacles. In this study, a new method was created for chemical space network analysis and classification of small samples, and applied to 104 compounds. Here, t-SNE (t-Distributed Stochastic Neighbor Embedding) dimensions were used to reduce coordinates as node locations and edge connections of chemical space networks, respectively, and molecules were grouped according to whether the edges were connected and the proximity of the node coordinates. Through detailed analysis of the structural characteristics and fingerprints of each classified group, our classification method attained good accuracy. Targets were then identified using reverse docking methods, and the active centers of the same types of compounds were determined by quantum chemical calculation. The results indicated that these compounds can be divided into nine groups, according to their mean within-group similarity (MWGS) values. The three families with the most members, i.e., the d-limonene group (18), α-pinene group (10), and γ-maaliene group (nine members) determined the protein targets, using PharmMapper. Structure fingerprint analysis was employed to predict the binding mode of the ligands of four families of the protein targets. Thence, quantum chemical calculations were applied to the active group of the representative compounds of the four families. This study provides further scientific information to support the use of sunflower receptacles.