Molecular Recognition between Carbon Dioxide and Biodegradable Hydrogel Models: A Density Functional Theory (DFT) Investigation.

In this research, we explore the potential of employing density functional theory (DFT) for the design of biodegradable hydrogels aimed at capturing carbon dioxide (CO2) and mitigating greenhouse gas emissions. We employed biodegradable hydrogel models, including polyethylene glycol, polyvinylpyrrolidone, chitosan, and poly-2-hydroxymethacrylate. The complexation process between the hydrogel and CO2 was thoroughly investigated at the ωB97X-D/6-311G(2d,p) theoretical level. Our findings reveal a strong affinity between the hydrogel models and CO2, with binding energies ranging from -4.5 to -6.5 kcal/mol, indicative of physisorption processes. The absorption order observed was as follows: chitosan > PVP > HEAC > PEG. Additionally, thermodynamic parameters substantiated this sequence and even suggested that these complexes remain stable up to 160 °C. Consequently, these polymers present a promising avenue for crafting novel materials for CO2 capture applications. Nonetheless, further research is warranted to optimize the design of these materials and assess their performance across various environmental conditions.

Synthesis, Photophysical Properties, Theoretical Studies, and Living Cancer Cell Imaging Applications of New 7-(Diethylamino)quinolone Chalcones.

In this article, three unsymmetrical 7-(diethylamino)quinolone chalcones with D-π-A-D and D-π-A-π-D type push-pull molecular arrangements were synthesized via a Claisen-Schmidt reaction. Using 7-(diethylamino)quinolone and vanillin as electron donor (D) moieties, these were linked together through the α,ß-unsaturated carbonyl system acting as a linker and an electron acceptor (A). The photophysical properties were studied, revealing significant Stokes shifts and strong solvatofluorochromism caused by the ICT and TICT behavior produced by the push-pull effect. Moreover, quenching caused by the population of the TICT state in THF-H2O mixtures was observed, and the emission in the solid state evidenced a red shift compared to the emission in solution. These findings were corroborated by density functional theory (DFT) calculations employing the wb97xd/6-311G(d,p) method. The cytotoxic activity of the synthesized compounds was assessed on BHK-21, PC3, and LNCaP cell lines, revealing moderate activity across all compounds. Notably, compound 5b exhibited the highest activity against LNCaP cells, with an LC50 value of 10.89 µM. Furthermore, the compounds were evaluated for their potential as imaging agents in living prostate cells. The results demonstrated their favorable cell permeability and strong emission at 488 nm, positioning them as promising candidates for cancer cell imaging applications.

Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging.

Sorbitol derivatives and other additives are commonly used in various products, such as packaging or food packaging, to improve their mechanical, physical, and optical properties. To accurately and precisely evaluate the efficacy of adding sorbitol-type nucleating agents to these articles, their quantitative determination is essential. This study systematically investigated the quantification of sorbitol-type nucleating agents in food packaging made from impact copolymers of polypropylene (PP) and polyethylene (PE) using attenuated total reflectance infrared spectroscopy (ATR-FTIR) together with analysis of principal components (PCA) and machine learning algorithms. The absorption spectra revealed characteristic bands corresponding to the C-O-C bond and hydroxyl groups attached to the cyclohexane ring of the molecular structure of sorbitol, providing crucial information for identifying and quantifying sorbitol derivatives. PCA analysis showed that with the selected FTIR spectrum range and only the first two components, 99.5% of the variance could be explained. The resulting score plot showed a clear pattern distinguishing different concentrations of the nucleating agent, affirming the predictability of concentrations based on an impact copolymer. The study then employed machine learning algorithms (NN, SVR) to establish prediction models, evaluating their quality using metrics such as RMSE, R2, and RMSECV. Hyperparameter optimization was performed, and SVR showed superior performance, achieving near-perfect predictions (R2 = 0.9999) with an RMSE of 0.100 for both calibration and prediction. The chosen SVR model features two hidden layers with 15 neurons each and uses the Adam algorithm, balanced precision, and computational efficiency. The innovative ATR-FTIR coupled SVR model presented a novel and rapid approach to accurately quantify sorbitol-type nucleating agents in polymer production processes for polymer research and in the analysis of nucleating agent derivatives. The analytical performance of this method surpassed traditional methods (PCR, NN).

Multiquery Similarity Searching Models: An Alternative Approach for Predicting Hemolytic Activity from Peptide Sequence.

The desirable pharmacological properties and a broad number of therapeutic activities have made peptides promising drugs over small organic molecules and antibody drugs. Nevertheless, toxic effects, such as hemolysis, have hampered the development of such promising drugs. Hence, a reliable computational tool to predict peptide hemolytic toxicity is enormously useful before synthesis and experimental evaluation. Currently, four web servers that predict hemolytic activity using machine learning (ML) algorithms are available; however, they exhibit some limitations, such as the need for a reliable negative set and limited application domain. Hence, we developed a robust model based on a novel theoretical approach that combines network science and a multiquery similarity searching (MQSS) method. A total of 1152 initial models were constructed from 144 scaffolds generated in a previous report. These were evaluated on external data sets, and the best models were fused and improved. Our best MQSS model I1 outperformed all state-of-the-art ML-based models and was used to characterize the prevalence of hemolytic toxicity on therapeutic peptides. Based on our model's estimation, the number of hemolytic peptides might be 3.9-fold higher than the reported.

Study of the Chemical Activities of Carbon Monoxide, Carbon Dioxide, and Oxygen Traces as Critical Inhibitors of Polypropylene Synthesis.

This study outlines the investigation into how the compounds CO2, CO, and O2 interact with the active center of titanium (Ti) on the surface of MgCl2 and how these interactions impact the productivity of the Ziegler-Natta catalyst, ultimately influencing the thermal stability of the produced polypropylene. The calculations revealed that the adsorption energies of Ti-CO2-CO and O2 were -9.6, -12.5, and -2.32 Kcal/mol, respectively. Using the density functional theory in quantum calculations, the impacts of electronic properties and molecular structure on the adsorption of CO, O2, and CO2 on the Ziegler-Natta catalyst were thoroughly explored. Additionally, the Gibbs free energy and enthalpy of adsorption were examined. It was discovered that strong adsorption and a significant energy release (-16.2 kcal/mol) during CO adsorption could explain why this gas caused the most substantial reductions in the ZN catalyst productivity. These findings are supported by experimental tests showing that carbon monoxide has the most significant impact on the ZN catalyst productivity, followed by carbon dioxide, while oxygen exerts a less pronounced inhibitory effect.

Rethinking the applicability domain analysis in QSAR models.

Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in "rational" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates.

Experimental-Density Functional Theory (DFT) Study of the Inhibitory Effect of Furan Residues in the Ziegler-Natta Catalyst during Polypropylene Synthesis.

In this experimental-theoretical study, the effect of furan on Ziegler-Natta catalyst productivity, melt flow index (MFI), and mechanical properties of polypropylene were investigated. Through the analysis of the global and local reactivity of the reagents, it was determined that the furan acts as an electron donor. In contrast, the titanium of the ZN catalyst acts as an electron acceptor. It is postulated that this difference in reactivity could lead to forming a furan-titanium complex, which blocks the catalyst's active sites and reduces its efficiency for propylene polymerization. Theoretical results showed a high adsorption affinity of furan to the active site of the Ti catalyst, indicating that furan tends to bind strongly to the catalyst, thus blocking the active sites and decreasing the availability for propylene polymerization. The experimental data revealed that the presence of furan significantly reduced the productivity of the ZN catalyst by 10, 20, and 41% for concentrations of 6, 12.23, and 25.03 ppm furan, respectively. In addition, a proportional relationship was observed between the furan concentration and the MFI melt index of the polymer, where the higher the furan concentration, the higher the MFI. Likewise, the presence of furan negatively affected the mechanical properties of polypropylene, especially the impact Izod value, with percentage decreases of 9, 18, and 22% for concentrations of 6, 12.23, and 25.03 ppm furan, respectively.

Elucidating the Racemization Mechanism of Aliphatic and Aromatic Amino Acids by In Silico Tools.

The racemization of biomolecules in the active site can reduce the biological activity of drugs, and the mechanism involved in this process is still not fully comprehended. The present study investigates the impact of aromaticity on racemization using advanced theoretical techniques based on density functional theory. Calculations were performed at the ωb97xd/6-311++g(d,p) level of theory. A compelling explanation for the observed aromatic stabilization via resonance is put forward, involving a carbanion intermediate. The analysis, employing Hammett's parameters, convincingly supports the presence of a negative charge within the transition state of aromatic compounds. Moreover, the combined utilization of natural bond orbital (NBO) analysis and intrinsic reaction coordinate (IRC) calculations confirms the pronounced stabilization of electron distribution within the carbanion intermediate. To enhance our understanding of the racemization process, a thorough examination of the evolution of NBO charges and Wiberg bond indices (WBIs) at all points along the IRC profile is performed. This approach offers valuable insights into the synchronicity parameters governing the racemization reactions.

Identifying, Quantifying, and Recovering a Sorbitol-Type Petrochemical Additive in Industrial Wastewater and Its Subsequent Application in a Polymeric Matrix as a Nucleating Agent.

Currently, polypropylene (PP) is highlighted using sorbitol-based clarifying agents since these agents are high quality, low cost, and work as a barrier against moisture, which makes PP ideal for packaging food, beverages, and medical products, among others. The use of analytical methods capable of recovering these additives in wastewater streams and then reusing them in the PP clarification stage represents an innovative methodology that makes a substantial contribution to the circular economy of the PP production industry. In this study, a method of extraction and recovery of the Millad NX 8000 was developed. The additive was recovered using GC-MS and extracted with an activated carbon column plus glass fiber, using an injection molded sample, obtaining a recovery rate greater than 96%. TGA, DSC, and FTIR were used to evaluate the recovered additive's glass transitions and purity. The thermal degradation of the recovered additive was found to be between 340 and 420 °C, with a melting temperature of 246 °C, adopting the same behavior as the pure additive. In FTIR, the characteristic absorption peak of Millad NX 8000 was observed at 1073 cm-1, which indicates the purity of the extracted compound. Therefore, this work develops a new additive recovery methodology with high purity to regulate the crystallization behavior and of PP.

A comprehensive study of product distributions and coke deposition during catalytic cracking of vacuum gas oil over hierarchical zeolites.

In this study, zeolites (Z) were used as catalysts in the cracking of a Colombian vacuum gas oil (VGO), with a focus on product distribution and coke deposition. The catalytic tests were carried out in a MAT-type reactor under typical conditions. The zeolites were subjected to alkaline treatment with NaOH at concentrations ranging from 0.05 to 0.4 mol/L, resulting in the creation of several samples (Z-0.05, Z-0.10, Z-0.20, Z-0.30 and Z-0.40) that were then hydrothermally stabilized (Z-0.05-M, Z-0.10-M, Z-0.20-M, Z-0.30-M and Z-0.40-M) to increase mesoporosity and reduced crystallinity. The increase in mesoporosity was accompanied by an improvement in acidity. Despite Z-0.30-M having higher acidity, Z-0.00-M and Z-0.10-M exhibited the highest activity due to their high crystallinity and microporosity, yielding the highest gas yields. Gasoline was the main product, with maximum yields exceeding 30%. Z-0.20-M produced more aromatic and olefin compounds than the others, resulting in higher quality gasoline. Coke formation followed the trend: Z-0.00-M < Z-0.10-M < Z-0.20-M < Z-0.30-M. The higher intracrystalline mesoporosity in the zeolites favored the formation of a more condensed coke.

Furan as Impurity in Green Ethylene and Its Effects on the Productivity of Random Ethylene-Propylene Copolymer Synthesis and Its Thermal and Mechanical Properties.

The presence of impurities such as H2S, thiols, ketones, and permanent gases in propylene of fossil origin and their use in the polypropylene production process affect the efficiency of the synthesis and the mechanical properties of the polymer and generate millions of losses worldwide. This creates an urgent need to know the families of inhibitors and their concentration levels. This article uses ethylene green to synthesize an ethylene-propylene copolymer. It describes the impact of trace impurities of furan in ethylene green and how this furan influences the loss of properties such as thermal and mechanical properties of the random copolymer. For the development of the investigation, 12 runs were carried out, each in triplicate. The results show an evident influence of furan on the productivity of the Ziegler-Natta catalyst (ZN); productivity losses of 10, 20, and 41% were obtained for the copolymers synthesized with ethylene rich in 6, 12, and 25 ppm of furan, respectively. PP0 (without furan) did not present losses. Likewise, as the concentration of furan increased, it was observed that the melt flow index (MFI), thermal (TGA), and mechanical properties (tensile, bending, and impact) decreased significantly. Therefore, it can be affirmed that furan should be a substance to be controlled in the purification processes of green ethylene.

Stochastic Nature and Intramolecular Coupling in Optical Response Profiles: Critical Analysis through Semiclassical Models.

We have studied the nonlinear absorptive and dispersive responses considering a molecular system consisting of two-levels, where aspects of the vibrational internal structure and intramolecular coupling are inserted, in addition to the considerations of interaction with the thermal reservoir. The Born-Oppenheimer electronic energy curve for this molecular model consists of two-intercrossing harmonic oscillator potentials with minima displaced in energy and nuclear coordinate. The results obtained show how these optical responses are sensitive to explicit considerations of both intramolecular coupling and the presence of the solvent through their stochastic interaction. Our study shows that the permanent dipoles of the system and the transition dipoles induced by electromagnetic field effects represent critical quantities for the analysis. The solvent action in our model is treated through the natural Bohr frequency shift to a time-dependent function, with explicit manifestations in its comparison as if the upper state were broadened. Significant variations in the nonlinear optical properties for cases of perturbative and saturative treatments, relaxation times, and optical propagation, mainly due to changes in the probe and pump intensities, are studied. Our studies relating the intramolecular effects with those generated by the presence of the solvent and its stochastic interaction with the solute of study, have allowed not only to analyze the influence of these in the profile of the optical responses, but they could also provide some insights into the analysis and characterization of molecular systems through nonlinear optical properties.

ElectroPredictor: An Application to Predict Mayr's Electrophilicity E through Implementation of an Ensemble Model Based on Machine Learning Algorithms.

Electrophilicity (E) is one of the most important parameters to understand the reactivity of an organic molecule. Although the theoretical electrophilicity index (ω) has been associated with E in a small homologous series, the use of w to predict E in a structurally heterogeneous set of compounds is not a trivial task. In this study, a robust ensemble model is created using Mayr's database of reactivity parameters. A combination of topological and quantum mechanical descriptors and different machine learning algorithms are employed for the model's development. The predictability of the model is assessed using different statistical parameters, and its validation is examined, including a training/test partition, an applicability domain, and a y-scrambling test. The global ensemble model presents a Q5-fold2 of 0.909 and a Qext2 of 0.912, demonstrating an excellent predictability performance of E values and showing that w is not a good descriptor for the prediction of E, especially for the case of neutral compounds. ElectroPredictor, a noncommercial Python application (https://github.com/mmoreno1/ElectroPredictor), is developed to predict E. QM9, a well-known large dataset containing 133885 neutral molecules, is used to perform a virtual screening (94.0% coverage). Finally, the 10 most electrophilic molecules are analyzed as possible new Mayr's electrophiles, which have not yet been experimentally tested. This study confirms the necessity to build an ensemble model using nonlinear machine learning algorithms, topographic descriptors, and separating molecules into charged and neutral compounds to predict E with precision.

Study of potential inhibition of the estrogen receptor α by cannabinoids using an in silico approach: Agonist vs antagonist mechanism.

Breast cancer is the main cancer type with more than 2.2 million cases in 2020, and is the principal cause of death in women; with 685000 deaths in 2020 worldwide. The estrogen receptor is involved at least in 70% of breast cancer diagnoses, and the agonist and antagonist properties of the drug in this receptor play a pivotal role in the control of this illness. This work evaluated the agonist and antagonist mechanisms of 30 cannabinoids by employing molecular docking and dynamic simulations. Compounds with docking scores < -8 kcal/mol were analyzed by molecular dynamic simulation at 300 ns, and relevant insights are given about the protein's structural changes, centered on the helicity in alpha-helices H3, H8, H11, and H12. Cannabicitran was the cannabinoid that presented the best relative binding-free energy (-34.96 kcal/mol), and based on rational modification, we found a new natural-based compound with relative binding-free energy (-44.83 kcal/mol) better than the controls hydroxytamoxifen and acolbifen. Structure modifications that could increase biological activity are suggested.

Searching glycolate oxidase inhibitors based on QSAR, molecular docking, and molecular dynamic simulation approaches.

Primary hyperoxaluria type 1 (PHT1) treatment is mainly focused on inhibiting the enzyme glycolate oxidase, which plays a pivotal role in the production of glyoxylate, which undergoes oxidation to produce oxalate. When the renal secretion capacity exceeds, calcium oxalate forms stones that accumulate in the kidneys. In this respect, detailed QSAR analysis, molecular docking, and dynamics simulations of a series of inhibitors containing glycolic, glyoxylic, and salicylic acid groups have been performed employing different regression machine learning techniques. Three robust models with less than 9 descriptors-based on a tenfold cross (Q2 CV) and external (Q2 EXT) validation-were found i.e., MLR1 (Q2 CV = 0.893, Q2 EXT = 0.897), RF1 (Q2 CV = 0.889, Q2 EXT = 0.907), and IBK1 (Q2 CV = 0.891, Q2 EXT = 0.907). An ensemble model was built by averaging the predicted pIC50 of the three models, obtaining a Q2 EXT = 0.933. Physicochemical properties such as charge, electronegativity, hardness, softness, van der Waals volume, and polarizability were considered as attributes to build the models. To get more insight into the potential biological activity of the compouds studied herein, docking and dynamic analysis were carried out, finding the hydrophobic and polar residues show important interactions with the ligands. A screening of the DrugBank database V.5.1.7 was performed, leading to the proposal of seven commercial drugs within the applicability domain of the models, that can be suggested as possible PHT1 treatment.

Effects of Different Concentrations of Arsine on the Synthesis and Final Properties of Polypropylene.

This article studies the effects of arsine on the synthesis and thermal degradation of 4 samples of virgin polypropylene (PP-virgin) and proposes reaction mechanisms that allow understanding of its behaviour. Different points are monitored during the polypropylene synthesis to perform TGA, DSC, FT-IR, RDX, and MFI analyses later. The content of AsH3 in polypropylene varies between 0.05 and 4.73 ppm, and of arsenic in virgin PP residues between 0.001 and 4.32 ppm for PP0 and PP10, increasing in fluidity index from 3.0 to 24.51. The origin of thermo-oxidative degradation is explained by the reaction mechanisms of the Molecule AsH3 with the active titanium center of the ZN catalyst and the subsequent oxidation to form radical complexes. OO-AsH-TiCl4-MgCl2 and (OO-as-OO)2 -TiCl4-MgCl2, which, by radical reactions, give rise to the formation of functional groups aldehyde, ketone, alcohol, carboxylic acid, CO, CO2, PP-Polyol, PP-Polyether, and PP-Isopropylethers. These species caused the TG and DTG curves to increase degradation peaks in pp samples.

Al2O3-MgO Supported Ni, Mo, and NiMo Mixed Phosphidic-Sulphidic Phase for Hydrotreating of Stearic and Oleic Acids Into Green Diesel.

The effect of the sulfur and metal-type content of MoP-S/γ-Al2O3-MgO, NiMoP-S/γ-Al2O3-MgO, and NiP-S/γ-Al2O3-MgO phosphide on hydroprocessing (HDO, HDCx-HDCn, HCK, HYD, and HYG) of fatty acids was studied. The catalysts were characterized by XRF, XRD, textural properties, XPS, Raman, Py-TPD, and EDS elemental mapping. The chemical analyses by X-ray fluorescence (XRF), EDS elemental mapping, and CHNS-O elemental analysis showed stoichiometric values Al/Mg = 38-40, Mo:Ni:P ∼ 1, and S ≤ 4.5 wt % (this value means that the molar ratio Mo:S ∼ 1.0:1.6, i.e., MoS2); also EDS elemental mapping confirmed the presence of Mo, Ni, Al, O, P, Mg, and S with good distribution on Al2O3-MgO. The impregnation of metals leads to a decrease in the surface area and pore volume as follows NiMoP-S/γ-Al2O3-MgO < MoP-S/γ-Al2O3-MgO < NiP-S/γ-Al2O3-MgO < Al2O3-MgO < Al2O3 (unimodal pore size distribution), propitiating a pseudo bimodal pore size distribution with Dp-BJH between ∼5-7 nm and 11.8-14.2 nm for the presence of MgO. XRD confirmed differences between metallic phosphates and phosphides, and XPS confirmed the presence at the surface of Moδ+(0 < δ+ < 2), Mo4+, Mo6+, Niδ+(0 < δ+ < 2), Ni2+, S2-, SO4 2-, Pδ+, and P5+ species. Raman revealed the presence of MoS2 only in MoP-S/γ-Al2O3-MgO and NiMoP-S/γ-Al2O3-MgO, while the NiMoP-S/γ-Al2O3-MgO catalyst had a more significant number of Brønsted and Lewis sites, although the increasing temperature decreased the Lewis sites. MoP-S/γ-Al2O3-MgO was more active at HDO showing the highest production rate for octadecane of 53 mol/(gcat·h), whereas NiP-S/γ-Al2O3-MgO was more active at HDCx-HDCn [45 mol/(gcat·h)] and HCK [6 mol/(gcat·h)]; meanwhile, NiMoP-S/γ-Al2O3-MgO had a mix of HDO [47 mol/(gcat·h)] and HDCx-HDCn [41 mol/(gcat·h)]. This showed production towards octadecane, heptadecane, and light hydrocarbons, meaning that the fatty acids were deoxygenated through bifunctional sites for hydrogenation (HYD) and hydrogenolysis (HYG) as follows: MoP-S/γ-Al2O3-MgO (K1 = 0.08 and K2 = 0.03 L/mol) < NiMoP-S/γ-Al2O3-MgO (K1 = 0.25 and K2 = 0.45 L/mol) < NiP-S/γ-Al2O3-MgO (K1 = 2.5 and K2 = 6.5 L/mol). For this reason, we considered that phosphide acts as a structural promoter with sulfur on its surface as a "mixed phosphidic-sulphidic species", allowing the largest generation of heptadecane and octadecane by the presence of BRIM sites for HYD and CUS sites for HYG.

Changes in Protonation Sites of 3-Styryl Derivatives of 7-(dialkylamino)-aza-coumarin Dyes Induced by Cucurbit[7]uril.

The incorporation of a guest, with different basic sites, into an organized system (host), such as macrocycles, could stabilize, detect, or promote the formation of a certain protomer. In this context, this work aimed to study the influence of cucurbit[7]uril (CB7) on dyes such as 7-(dimethylamino)-aza-coumarins, which have more than one basic site along their molecular structure. For this, three 3-styryl derivatives of 7-(dialkylamino)-aza-coumarin dyes (SAC1-3) were synthesized and characterized by NMR, ESI-HRMS and IR. The spectral behaviour of the SACs in the absence and presence of CB7 was studied. The results showed large shifts in the UV-vis spectrum in acid medium: a hypsochromic shift of ≈5400 cm-1 (SAC1-2) and ≈3500 cm-1 (SAC3) in the absence of CB7 and a bathochromic shift of ≈4500 cm-1 (SAC1-3) in the presence of CB7. The new absorptions at long and short wavelengths were assigned to the corresponding protomers by computational calculations at the density functional theory (DFT) level. Additionally, the binding mode was corroborated by molecular dynamics simulations. Findings revealed that in the presence of CB7 the heterocyclic nitrogen was preferably protonated instead of the dialkylamino group. Namely, CB7 induces a change in the protonation preference at the basic sites of the SACs, as consequence of the molecular recognition by the macrocycle.

In Silico Searching for Alternative Lead Compounds to Treat Type 2 Diabetes through a QSAR and Molecular Dynamics Study.

Free fatty acid receptor 1 (FFA1) stimulates insulin secretion in pancreatic ß-cells. An advantage of therapies that target FFA1 is their reduced risk of hypoglycemia relative to common type 2 diabetes treatments. In this work, quantitative structure-activity relationship (QSAR) approach was used to construct models to identify possible FFA1 agonists by applying four different machine-learning algorithms. The best model (M2) meets the Tropsha's test requirements and has the statistics parameters R2 = 0.843, Q2CV = 0.785, and Q2ext = 0.855. Also, coverage of 100% of the test set based on the applicability domain analysis was obtained. Furthermore, a deep analysis based on the ADME predictions, molecular docking, and molecular dynamics simulations was performed. The lipophilicity and the residue interactions were used as relevant criteria for selecting a candidate from the screening of the DiaNat and DrugBank databases. Finally, the FDA-approved drugs bilastine, bromfenac, and fenofibric acid are suggested as potential and lead FFA1 agonists.

QSAR Studies, Molecular Docking, Molecular Dynamics, Synthesis, and Biological Evaluation of Novel Quinolinone-Based Thiosemicarbazones against Mycobacterium tuberculosis.

In this study, a series of novel quinolinone-based thiosemicarbazones were designed in silico and their activities tested in vitro against Mycobacterium tuberculosis (M. tuberculosis). Quantitative structure-activity relationship (QSAR) studies were performed using quinolinone and thiosemicarbazide as pharmacophoric nuclei; the best model showed statistical parameters of R2 = 0.83; F = 47.96; s = 0.31, and was validated by several different methods. The van der Waals volume, electron density, and electronegativity model results suggested a pivotal role in antituberculosis (anti-TB) activity. Subsequently, from this model a new series of quinolinone-thiosemicarbazone 11a-e was designed and docked against two tuberculosis protein targets: enoyl-acyl carrier protein reductase (InhA) and decaprenylphosphoryl-ß-D-ribose-2'-oxidase (DprE1). Molecular dynamics simulation over 200 ns showed a binding energy of -71.3 to -12.7 Kcal/mol, suggesting likely inhibition. In vitro antimycobacterial activity of quinolinone-thiosemicarbazone for 11a-e was evaluated against M. bovis, M. tuberculosis H37Rv, and six different strains of drug-resistant M. tuberculosis. All compounds exhibited good to excellent activity against all the families of M. tuberculosis. Several of the here synthesized compounds were more effective than the standard drugs (isoniazid, oxafloxacin), 11d and 11e being the most active products. The results suggest that these compounds may contribute as lead compounds in the research of new potential antimycobacterial agents.