Number Of Rotatable Bonds Research Articles

Synthesis and antifungal activity of novel amide derivatives from quinic acid against the sweet potato pathogen Ceratocystis fimbriata.

Ceratocystis fimbriata is a fungal pathogen that infects sweet potato roots, producing enormous economic losses. Cyclic polyhydroxy compound quinic acid is a common metabolite synthesized in plant tissues, including sweet potato tubers, showing weak antifungal properties. Although several O-acylated quinic acid derivatives have been synthesized and found in nature and their antifungal properties have been explored, derivatives based on modification of the carboxylic acid have never been evaluated. In this study, amide derivatives were synthesized via linkage of amines with the carboxylic acid moiety of quinic acid. Derivatives with high dipolar moments and a low number of rotatable bonds showed greater antifungal activities toward C. fimbriata in vitro than quinic and chlorogenic acids. Derivative 5b, which was synthesized by coupling p-aminobenzoic acid (pABA) with quinic acid, had the greatest antifungal activity. 5b showed iron(II)-chelating properties and reduced ergosterol content in C. fimbriata cells, causing irregularities in the fungal cell wall and inhibiting conidia agglutination. Application of 3 mm 5b reduced black rot symptoms in sweet potatoes by 70.1%. Collectively, derivatization of the carboxylic acid from quinic acid was demonstrated to be a suitable strategy to improve the antifungal properties of this compound. This study reveals a new efficient strategy for management of the sweet potato pathogen C. fimbriata. © 2024 Society of Chemical Industry.

Heat diffusion coefficient study of polymers based on interpretable machine learning

Abstract. Polymers hold significant application value across various fields of modern society, with different application scenarios requiring specific thermal diffusivity coefficients. Finding polymer materials with targeted thermal diffusivities is crucial. However, due to the vast variety and complex structures of polymers, constructing a unified structured dataset for machine learning modeling is challenging. Although machine learning has shown great potential in materials science, it has rarely been applied to predict the heat diffusion coefficient of polymers. This paper constructs a dataset for predicting the thermal diffusion coefficient of polymers using a publicly available dataset by transforming the SMILES code of polymers into eight features with practical physical and chemical meanings. Using the Random Forest algorithm, training with 400 of these data and randomly selecting 200 of them for cross-validation, the accuracy of the test set reached 0.9. Additionally, through interpretability analysis, we found that the molecular weight of the polymer monomers, the TPSA (the polar surface area of the molecule), and the NRB (the number of rotatable bonds) are the main features affecting the polymer thermal diffusion coefficient. An increase in the TPSA and the NRB positively contributes to the thermal diffusivity, while an increase in molecular weight negatively contributes. Our study provides a new method for the prediction of polymer thermal diffusivity and creates a new paradigm for the study of polymer thermal diffusivity, promoting further development in this field.

How the Spacer Influences the Stability of 2D Perovskites?

Two-dimensionallead halide perovskites (2D HPs) represent as an emerging class of materials given their tunable optoelectronic properties and long-term stability in perovskite solar cells. However, the ever-growing field of optoelectronic devices using 2D HPs requires fundamental understanding of the influence of the spacer on the physiochemical properties and stability of perovskites as well as establish which cation properties are closely related to suppress the halogen ion mobility. This study focuses on investigating the influence of organic spacers with intrinsic properties (e.g., rigidity and flexibility, special groups) and variations of material dimensions on the stability of halogen ions and inorganic frameworks in 2D HPs. It is found that the perovskite structure composed of rigidity molecules owns better stability of halogen ion and inorganic framework than that of flexible molecules. The stability of ions exhibits a negative correlation with the dimensions of perovskite. More importantly, a simple descriptor for measuring the stability of halogen ions in 2D HPs is constructed. By causal discovery algorithms with more physical and chemical significance, the Kappa shape index, number of rotatable bonds, and aromatic carbocycles in organic spacers are identified as causal and important features for the stability of halogen ions in 2D HPs.

Assessment of the exchange-hole dipole moment dispersion correction for the energy ranking stage of the seventh crystal structure prediction blind test.

The seventh blind test of crystal structure prediction (CSP) methods substantially increased the level of complexity of the target compounds relative to the previous tests organized by the Cambridge Crystallographic Data Centre. In this work, the performance of density-functional methods is assessed using numerical atomic orbitals and the exchange-hole dipole moment dispersion correction (XDM) for the energy-ranking phase of the seventh blind test. Overall, excellent performance was seen for the two rigid molecules (XXVII, XXVIII) and for the organic salt (XXXIII). However, for the agrochemical (XXXI) and pharmaceutical (XXXII) targets, the experimental polymorphs were ranked fairly high in energy amongst the provided candidate structures and inclusion of thermal free-energy corrections from the lattice vibrations was found to be essential for compound XXXI. Based on these results, it is proposed that the importance of vibrational free-energy corrections increases with the number of rotatable bonds.

Treatability of odorous dioxanes/dioxolanes in source water: How does molecular flexibility and pre-oxidation affect odorant adsorption

Odorous dioxanes and dioxolanes, a class of cyclic acetals often produced as byproducts in polyester resin manufacturing, are problematic in drinking water treatment due to their low odor thresholds and resistance to conventional treatment technology. Our research focuses on the removal of ten dioxane/dioxolane compounds through oxidation and adsorption processes, exploring the key molecular properties that govern the treatmentability. We discovered that both chlorination and permanganate oxidation were largely ineffective at degrading cyclic acetals, achieving less than 20% removal even at high applicable doses. Conversely, powdered activated carbon (PAC) adsorption proved to be a more effective method, with a removal of > 90% at a PAC dosage of 10 mg/L for seven out of ten compounds. The presence of natural organic matter (NOM) reduced PAC adsorbability for all odorants, but the deterioration level substantially varied and mostly affected by structural flexibility as indicated by the number of rotatable bonds. The results of both the experimental investigation and molecular simulation corroborated the hypothesis that more rotatable bonds (from one to three here) are indicative of greater structural flexibility, which in consequence determines the susceptibility of cyclic acetals to NOM competitive adsorption. Increased structural flexibility could facilitate greater entry into silt-like micropores or achieve preferential adsorption sites with more compatible morphology against NOM competition. When pre-oxidation (chlorination and permanganate oxidation) and adsorption were applied sequentially, additional low molecular weight NOM components produced by pre-oxidation resulted in intensified NOM competition and decreased odorant adsorbability. If this combination is inevitably required for algae and odorant control, it would be beneficial to utilize a wise screen for oxidants and a reduced oxidant dose (less than 2 mg/L) to mitigate the deterioration of odorant adsorption. This study elucidates the roles of structural flexibility in influencing the treatability of dioxanes and dioxolanes, extending beyond the solely well-established effects of hydrophobicity. It also presents rational practice guidelines for the combination of pre-oxidation and adsorption in addressing odor incidents associated with dioxane and dioxolane compounds.

Quantum Chemistry-Based Prediction of Electron Ionization Mass Spectra for Environmental Chemicals.

There is a lack of experimental electron ionization high-resolution mass spectra available to assist compound identification. The in silico generation of mass spectra by quantum chemistry can aid annotation workflows, in particular to support the identification of compounds that lack experimental reference spectra, such as environmental chemicals. We present an open-source, semiautomated workflow for the in silico prediction of electron ionization high-resolution mass spectra at 70 eV based on the QCxMS software. The workflow was applied to predict the spectra of 367 environmental chemicals, and the accuracy was evaluated by comparison to experimental reference spectra acquired. The molecular flexibility, number of rotatable bonds, and number of electronegative atoms of a compound were negatively correlated with prediction accuracy. Few analytes are predicted to sufficient accuracy for the direct application of predicted spectra in spectral matching workflows (overall average score 428). The m/z values of the top 5 most abundant ions of predicted spectra rarely match ions in experimental spectra, evidencing the disconnect between simulated fragmentation pathways and empirical reaction mechanisms.

Large-scale statistical study of the dependence of retention index on heating rate in temperature-programmed gas chromatography

Retention indices are values that characterize the retention of a compound in gas chromatography. In practice, retention indices are often assumed to depend only on the structure of the molecule and the type of the stationary phase, but this approximation is incorrect. This study is devoted to studying the dependence of retention indices on the column heating rate in the linear temperature programming mode, using a large and diverse data set. In the NIST 20 database, most data records are recorded in this mode. For stationary phases based on poly(5%-diphenyl-95%-dimethyl)siloxane (5%-phenyl-PDMS), there is a high proportion of records with heating rates of 10-15 K/min. In practice, such a high heating rate is rarely used and the use of such data may cause errors. A search was made for groups of records that were taken from the same primary source, recorded for the same compound and the same stationary phase, but differing in a heating rate. For each of these groups, the value D, the angular coefficient (slope) of the dependence of the retention index on the heating rate, was calculated. This value can take both positive and negative values. The highest values and the greatest variation of D values are observed for polar stationary phases, but further consideration was performed for 5%-phenyl-PDMS due to its greater practical significance. For these stationary phases, the highest D values are observed for aromatic and polyaromatic molecules; oxygen-containing compounds, on the contrary, exhibit lower D values. Negative D values are observed for many trimethylsilyl derivatives. A data set of D values for 756 molecules was selected and published online. There is almost no correlation between D and the retention index, lipophilicity factor logP, and molecular weight. Significant correlations with the number of cycles, the number of rotatable bonds, and the number of aromatic atoms were observed. Linear equations quantitatively relating the molecular descriptors to the D value were constructed. A number of cycles and halogen atoms were shown to contribute positively to the D value, while a number of oxygen atoms and bonds subject to internal rotation contributed negatively. The strong influence of the values related to the conformational rigidity of molecules and the weak influence of polarity allow us to suppose that the entropic factor has a key influence on the D value. A simple empirical linear equation for estimating the value of D is derived and presented in this study. Several machine learning methods for predicting D are compared. The best results are shown by gradient boosting and a random forest. However, the random forest does not achieve high accuracy in predicting the retention indices themselves.

PICKAPEP: An application for parameter calculation and visualization of cyclized and modified peptidomimetics.

The interest in peptides and especially in peptidomimetic structures has risen enormously in the past few years. Novel modification strategies including nonnatural amino acids, sophisticated cyclization strategies, and side chain modifications to improve the pharmacokinetic properties of peptides are continuously arising. However, a calculator tool accompanying the current development in peptide sciences towards modified peptides is missing. Herein, we present the application PICKAPEP, enabling the virtual construction and visualization of peptidomimetics ranging from well-known cyclized and modified peptides such as ciclosporin A up to fully self-designed peptide-based structures with custom amino acids. Calculated parameters include the molecular weight, the water-octanol partition coefficient, the topological polar surface area, the number of rotatable bonds, and the peptide SMILES code. To our knowledge, PICKAPEP is the first tool allowing users to add custom amino acids as building blocks and also the only tool giving the possibility to process large peptide libraries and calculate parameters for multiple peptides at once. We believe that PICKAPEP will support peptide researchers in their work and will find wide application in current as well as future peptide drug development processes. PICKAPEP is available open source for Windows and Mac operating systems (https://urldefense.com/v3/__https://www.research-collection.ethz.ch/handle/20.500.11850/681174__;!!N11eV2iwtfs!qt5f_2lNd6IZUDH1HVSVwg0zYzS8-nFazQ8c61jS5GaD5vkVS5C3igyfh3haJRnaX8ugW7o9VWUiCihPqcptmaWoqwYf9LvZTQ$).

Antidiabetic Advancements In Silico: Pioneering Novel Heterocyclic Derivatives through Computational Design

Background:: Deficiency of insulin signaling in type 2 diabetes results from insulin resistance or defective insulin secretion and induced hyperglycemia. By reducing glycated hemoglobin, SGLT2 inhibitors improve hyperuricemia, blood lipids, and weight loss without increasing the risk of hypoglycemia. By targeting this pathway, SGLT2 inhibitors can become a prominent target in the management of type 2 diabetes. Objective:: This study aimed to carry out the molecular docking and ADMET prediction of novel imidazo(2,1-b)-1,3,4 thiadiazole derivatives as SGLT2 inhibitors. Methods:: The chemical structures of 108 molecules were drawn by using ChemDraw Professional 15.0. Further, their energy minimization was also carried out by using Chem Bio Draw three-dimensional (3D) Ultra 12.0. Molecular docking was also carried out using a Molegro Virtual Docker to identify the best-fitting molecules and to identify the potential leads on the basis of dock score. The predicted parameters of drug-likeness according to Lipinski’s rule of five, such as molecular weight, log P, hydrogen bond acceptor, hydrogen bond donors, and number of rotatable bonds of the selected compounds, were predicted using pKCSM software. Results:: About 108 molecules were designed by employing different substitutions on imidazothiadiazole nucleus as SGLT2 inhibitors. Out of these, 10 compounds were found to have better interactions with the active site of SGLT2 protein and the highest dock scores compared to canagliflozin. Compounds 39a and 39b demonstrated good interactions and the highest docking scores of -155.428 and -142.786, respectively. The in silico physicochemical properties of the best compounds were also determined. Additionally, these compounds suggested a good pharmacokinetic profile as per Lipinski's rule of five (orally active drugs). Conclusion:: Novel imidazo (2,1-b)-1,3,4 thiadiazole derivatives were strategically designed, and their binding affinity was meticulously evaluated against the SGLT2 protein. This endeavor yielded pioneering lead compounds characterized by ultimate binding affinity, coupled with optimal ADMET properties in adherence to Lipinski's rule of five and favourable noncarcinogenic profile.

Machine Learning model for the prediction of self-diffusion coefficients in liquids, compressed gases and supercritical fluids

Machine Learning (ML) models were developed in this work to predict self-diffusion coefficients (D11) of dense fluids using four training algorithms: Gradient Boosting, k-Nearest Neighbors, Decision Tree, and Random Forest. A database of 7931 experimental points from 223 substances, at different pressures and temperatures, was used to train the models. From an initial set of 34 input features (variables/properties), the eight most important ones, ranked by decreasing relevance, were: density, acentric factor, temperature, critical temperature, critical volume, number of NH and/or OH bonds, pressure, and number of rotatable bonds. The best performance was achieved by models using the first 5 and 8 input features (ML5-D11 and ML8-D11) using the Gradient Boosting algorithm, for which the average absolute relative deviations (AARDglobal) were 9.06 % and 7.14 %, for the test set. The performance of the ML5-D11 and ML8-D11 models was compared with four phenomenological models – the predictive Zhu et al. equation, the 2-parameters Dymond-Hildebrand-Batschinski correlation, and the 1-parameter and 4-parameters Lennard-Jones correlations (LJ1 and LJ4) – which showed AARDglobal of 104.05 %, 82.94 %, 16.92 % and 7.97 %, respectively, for the same test set. Despite the good results of LJ4 equation, it is worth noting it embodies 4 substance-specific parameters that must be fitted to experimental data in advance. The new ML5-D11 and ML8-D11 models are purely predictive and can be applied to polar/nonpolar, spherical/non-spherical, and even hydrogen-bonding molecules in liquids, compressed gases or supercritical fluids. The ML5-D11 and ML8-D11 are provided for use as a Python program.

Enhanced Calculation of Property Distributions in Chemical Fragment Spaces.

Chemical fragment spaces exceed traditional virtual compound libraries by orders of magnitude, making them ideal search spaces for drug design projects. However, due to their immense size, they are not compatible with traditional analysis and search algorithms that rely on the enumeration of molecules. In this paper, we present SpaceProp2, an evolution of the SpaceProp algorithm, which enables the calculation of exact property distributions for chemical fragment spaces without enumerating them. We extend the original algorithm by the capabilities to compute distributions for the TPSA, the number of rotatable bonds, and the occurrence of user-defined molecular structures in the form of SMARTS patterns. Furthermore, SpaceProp2 produces example molecules for every property bin, enabling a detailed interpretation of the distributions. We demonstrate SpaceProp2 on six established make-on-demand chemical fragment spaces as well as BICLAIM, the in-house fragment space of Boehringer Ingelheim. The possibility to search multiple SMARTS patterns simultaneously as well as the produced example molecules offers previously impossible insights into the composition of these vast combinatorial molecule collections, making it an ideal tool for the analysis and design of chemical fragment spaces.

Construction of An Oral Bioavailability Prediction Model Based on Machine Learning for Evaluating Molecular Modifications

ObjectiveThis study aims to explore the impact of ADME on the Oral Bioavailability (OB) of drugs and to construct a machine learning model for OB prediction. The model is then applied to predict the OB of modified berberine and atenolol molecules to obtain structures with higher OB. MethodsInitially, a drug OB database was established, and corresponding ADME characteristics were obtained. The relationship between ADME and OB was analyzed using machine learning, with Morgan fingerprints serving as molecular descriptors. Compounds from the database were input into Random Forest, XGBoost, CatBoost, and LightGBM machine learning models to train the OB 7prediction model and evaluate its performance. Subsequently, berberine and atenolol were modified using Chemdraw software with ten different substituents for mono-substitution, and chlorine atoms for a full range of double substitutions. The modified molecular structures were converted into the same format as the training set for OB prediction. The predicted OB values of the modified structures of berberine and atenolol were compared. ResultsAn OB database of 386 drugs was obtained. It was found that smaller molecular weight and a higher number of rotatable bonds (ten or less) could potentially lead to higher OB. The four machine learning models were evaluated using MSE, R2 score, MAE, and MFE as metrics, with Random Forest performing the best. The models' predictions for the test set were particularly accurate when OB ranged from 30% to 90%. After mono-substitution and double substitution of berberine and atenolol, the OB of both drugs was significantly improved. ConclusionsThis study found that some ADME properties of molecules do not have an absolute impact on OB. The database played a decisive role in the process of the machine learning OB prediction model, and the performance of the model was evaluated based on predictions within a range of strong generalization ability. In most cases, mono-substitution and double substitution were beneficial for enhancing the OB of berberine and atenolol. In summary, this study successfully constructed a machine learning regression prediction model that can accurately predict drug OB, which can guide drug design to achieve higher OB to some extent.

Modeling the Blood-Brain Barrier Permeability of Potential Heterocyclic Drugs via Biomimetic IAM Chromatography Technique Combined with QSAR Methodology.

Penetration through the blood-brain barrier (BBB) is desirable in the case of potential pharmaceuticals acting on the central nervous system (CNS), but is undesirable in the case of drug candidates acting on the peripheral nervous system because it may cause CNS side effects. Therefore, modeling of the permeability across the blood-brain barrier (i.e., the logarithm of the brain to blood concentration ratio, log BB) of potential pharmaceuticals should be performed as early as possible in the preclinical phase of drug development. Biomimetic chromatography with immobilized artificial membrane (IAM) and the quantitative structure-activity relationship (QSAR) methodology were successful in modeling the blood-brain barrier permeability of 126 drug candidates, whose experimentally-derived lipophilicity indices and computationally-derived molecular descriptors (such as molecular weight (MW), number of rotatable bonds (NRB), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), topological polar surface area (TPSA), and polarizability (α)) varied by class. The QSARs model established by multiple linear regression showed a positive effect of the lipophilicity (log kw, IAM) and molecular weight of the compound, and a negative effect of the number of hydrogen bond donors and acceptors, on the log BB values. The model has been cross-validated, and all statistics indicate that it is very good and has high predictive ability. The simplicity of the developed model, and its usefulness in screening studies of novel drug candidates that are able to cross the BBB by passive diffusion, are emphasized.

Analysing the effect caused by increasing the molecular volume in M1-AChR receptor agonists and antagonists: a structural and computational study.

M1 muscarinic acetylcholine receptor (M1-AChR), a member of the G protein-coupled receptors (GPCR) family, plays a crucial role in learning and memory, making it an important drug target for Alzheimer's disease (AD) and schizophrenia. M1-AChR activation and deactivation have shown modifying effects in AD and PD preclinical models, respectively. However, understanding the pharmacology associated with M1-AChR activation or deactivation is complex, because of the low selectivity among muscarinic subtypes, hampering their therapeutic applications. In this regard, we constructed two quantitative structure-activity relationship (QSAR) models, one for M1-AChR agonists (total and partial), and the other for the antagonists. The binding mode of 59 structurally different compounds, including agonists and antagonists with experimental binding affinity values (pKi), were analyzed employing computational molecular docking over different structures of M1-AChR. Furthermore, we considered the interaction energy (Einter), the number of rotatable bonds (NRB), and lipophilicity (ilogP) for the construction of the QSAR model for agonists (R2 = 89.64, QLMO2 = 78, and Qext2 = 79.1). For the QSAR model of antagonists (R2 = 88.44, QLMO2 = 82, and Qext2 = 78.1) we considered the Einter, the fraction of sp3 carbons fCsp3, and lipophilicity (MlogP). Our results suggest that the ligand volume is a determinant to establish its biological activity (agonist or antagonist), causing changes in binding energy, and determining the affinity for M1-AChR.

Compound Absorption in Polymer Devices Impairs the Translatability of Preclinical Safety Assessments.

Organotypic and microphysiological systems (MPS) that can emulate the molecular phenotype and function of human tissues, such as liver, are increasingly used in preclinical drug development. However, despite their improved predictivity, drug development success rates have remained low with most compounds failing in clinical phases despite promising preclinical data. Here, it is tested whether absorption of small molecules to polymers commonly used for MPS fabrication can impact preclinical pharmacological and toxicological assessments and contribute to the high clinical failure rates. To this end, identical devices are fabricated from eight different MPS polymers and absorption of prototypic compounds with different physicochemical properties are analyzed. It is found that overall absorption is primarily driven by compound hydrophobicity and the number of rotatable bonds. However, absorption can differ by >1000-fold between polymers with polydimethyl siloxane (PDMS) being most absorptive, whereas polytetrafluoroethylene (PTFE) and thiol-ene epoxy (TEE) absorbed the least. Strikingly, organotypic primary human liver cultures successfully flagged hydrophobic hepatotoxins in lowly absorbing TEE devices at therapeutically relevant concentrations, whereas isogenic cultures in PDMS devices are resistant, resulting in false negative safety signals. Combined, these results can guide the selection of MPS materials and facilitate the development of preclinical assays with improved translatability.

Targeting the Interactions of Small GTPase ARF with C9ORF72:SMCR8:WDR41 Complexes Implicated in Amyotrophic Lateral Sclerosis/Frontotemporal Dementia

AbstractBackgroundC9ORF72 plays important roles in many cellular processes, including autophagy, membrane trafficking and immune response. The C9ORF72 mutation is the most common cause of familial amyotrophic lateral sclerosis/frontotemporal dementia (ALS/FTD) which is attributed to both gain‐ and loss‐of‐function mechanisms. C9ORF72 forms a complex with SMCR8 and WDR41, which was reported to regulate signaling of small GTPases such as Rab and ARF proteins. Developing small molecules that modulate interfacial C9ORF72:SMCR8:WDR41 (CSW) and small GTPase interactions will aid in understanding their mode of actions and the potential roles of GTP‐dependent signaling in ALS/FTD. Since the C9ORF72 complex has been shown to elicit prominent GTPase activating protein (GAP) activity on ARF, a major regulator of membrane traffic, we aimed to develop novel small molecules that interfere with the binding between the C9ORF72 complex and ARF1.MethodTo identify novel ligands to interfere with the binding between CSW complex and ARF1, we applied in silico virtual screening methods. To this end, the crystal structure of the CSW in complex with ARF1 (PDB: 7MGE) was used for screening of 40,183,714 chemicals available in Mcule database to find hit compound (s).ResultWe defined the binding pocket in the interface of ARF1 and CSW complex including all essential amino acid residues. For filtering the compounds, stringent criteria such as the number of rotatable bonds and the number of heavy atoms were applied. The top 100 hits were identified using this structure‐based virtual screening which show various interactions with the defined binding site of target. Next, the prepared library (the 100 identified Hits) was used as input for virtual screening using Schrodinger software. Flexible docking was performed using Glide program running under LINUX operating system. Based on vina scoring function, 10% of most active compounds that showed the best docking scores were identified and kept for further analysis.ConclusionRational design, computer‐aided, and in silico screening help developing pharmacological modulators to regulate C9ORF72‐ARF signaling and investigate their potential roles in ALS/FTD.Acknowledgement: This study is supported in part by NIH GM146257 on ALS² Initiative and by The Wooten Foundation.

Novel ibuprofen prodrug: A possible promising agent for the management of complications of Alzheimer’s disease

BackgroundAlzheimer’s disease (AD) is a severe, varied, and complex brain condition that gradually impairs memory and cognitive function. Epidemiological studies have shown that patients who have a history of long-term NSAID use have a decreased risk of developing AD. The objective of this study is to conduct the structural analysis of a novel ibuprofen prodrug and test its anti-Alzheimer’s properties. MethodsComputational and docking studies were conducted using AMBER 18 package. The in-vivo studies were performed using aluminum chloride-induced experimental AD in rats. Adult Wistar rats of either sex were used and treated with aluminum chloride (32.5 mg/kg, p.o) and ibuprofen prodrug (50 mg/kg, p.o) daily for 30 days. The hole-board test and elevated plus maze were conducted on 10th, 20th and 30th day. Further, on 31st day, animals were euthanized and the brain tissue was used for histopathology. The results obtained were subjected to statistical analysis by one-way ANOVA and Dunnet’s test, p < 0.05 was considered to indicate the significance. ResultsThe structural configuration of the novel compound indicated the presence of several structures such as aliphatic, aromatic, and asymmetry in the compound. The geometrical analysis indicated that the ibuprofen conjugate has dreiding energy of 51.22 kcal/mol with a van der waals radius of 62.56 A. The Huckel analysis confirmed the presence of aromatic rings in the compound. The molecular docking studies suggested affinity towards beta-secretase and acetylcholinesterase, besides indicating that the compound has ideal characteristics for the oral route (Log P = 2.33), cellular absorption (TPSA = 95.50), and oral bioavailability (number of rotatable bonds = 10). The toxicity profile indicated devoid of major systemic toxicity with mild possibility of cytotoxicity. The in-vivo analysis showed that the Ibu-prodrug significantly (P < 0.001) reversed the changes induced by aluminum chloride and restored histomorphological features in brain tissue. ConclusionThe findings suggested that the ibuprofen conjugate might possess the potential to manage the complications of AD. The action appears to be mediated through inhibition of beta-secretase and acetylcholinesterase activities. More studies might aid in identifying a specific therapeutic intervention that is still lacking in the treatment of AD.

Neural Network Models for Predicting Solubility and Metabolism Class of Drugs in the Biopharmaceutics Drug Disposition Classification System (BDDCS).

The biopharmaceutics drug disposition classification system (BDDCS) categorizes drugs into four classes on the basis of their solubility and metabolism. This framework allows for the study of the pharmacokinetics of transporters and enzymatic metabolization on biopharmaceuticals, as well as drug-drug interactions in the body. The objective of the present study was to develop computational models by neural network models and structural parameters and physicochemical properties to estimate the class of a drug in the BDDCS system. In this study, deep learning methods were utilized to explore the potential of artificial and convolutional neural networks (ANNs and CNNs) in predicting the BDDCS class of 721 substances. The structural parameters and physicochemical properties [Abraham solvation parameters, octanol-water partition (log P) and over the pH range 1-7.5 (log D), number of rotatable bonds, hydrogen bond acceptor numbers, as well as hydrogen bond donor count] are calculated with various software. These compounds were then split into a training set consisting of 602 molecules and a test set of 119 compounds to validate the models. The results of this study showed that neural network models using applied parameters of the drug, i.e., log D and Abraham solvation parameters, are able to predict the class of solubility and metabolism in the BDDCS system with good accuracy. Neural network models are well equipped to deal with the relations between the structural parameters and physicochemical properties of drugs and BDDCS classes. In addition, log D is a more suitable parameter compared with log P in predicting BDDCS.

Absorption matters: A closer look at popular oral bioavailability rules for drug approvals.

This study examines how two popular drug-likeness concepts used in early development, Lipinski Rule of Five (Ro5) and Veber's Rules, possibly affected drug profiles of FDA approved drugs since 1997. Our findings suggest that when all criteria are applied, relevant compounds may be excluded, addressing the harmfulness of blindly employing these rules. Of all oral drugs in the period used for this analysis, around 66 % conform to the RO5 and 85 % to Veber's Rules. Molecular Weight and calculated LogP showed low consistent values over time, apart from being the two least followed rules, challenging their relevance. On the other hand, hydrogen bond related rules and the number of rotatable bonds are amongst the most followed criteria and show exceptional consistency over time. Furthermore, our analysis indicates that topological polar surface area and total count of hydrogen bonds cannot be used as interchangeable parameters, contrary to the original proposal. This research enhances the comprehension of drug profiles that were FDA approved in the post-Lipinski period. Medicinal chemists could utilize these heuristics as a limited guide to direct their exploration of the oral bioavailability chemical space, but they must also steer the wheel to break these rules and explore different regions when necessary.

Therapeutic Activity of Phyto Ligands from Honey and Standard Drugs against Staphylococcus aureus PBP2a (5M19)

Staphylococcus aureus is the most dangerous of all of the many common staphylococcal bacteria. These gram-positive, sphere-shaped (coccal) bacteria often cause skin infections but can cause pneumonia, heart valve infections, and bone infections and may be resistant to treatment with some antibiotics. Phenols were the most abundant phytochemical compounds and accounted for 25.8% of the phytochemical composition of the honey. The presence of these phytochemicals implied that the honey could have therapeutic properties like antimicrobial, anticancer, antioxidant, diuretic, and anti-inflammatory activities. two out of the six tested compounds faulted the Lipinski rule of 5 which states that Hydrogen Bond Donor (HBD) &lt; 5 Hydrogen –Bond Acceptor (HBA) &lt; , 10 Molecular Weight &lt; 500 &lt; 500 Molecular weight, &lt; 5 H-bond donors, &lt;10 H-bond acceptors.&lt; 10 number of rotatable bonds, and &lt; 140 Å2 PSA value In this study. favourable overall docking score was observed in the range of -3.085 to -8.724. Chlorogenic acid had the best docked score (lowest binding energy)when compared to the synthetic inhibitors and other phytoligands. The binding manners and geometrical orientation of the studied phyto ligand from Honey and standard drugs against Staphylococcus aureus PBP2a (5M19). The results revealed that, Chlorogenic acid showed pivotal binding interactions with PBP2a, as it exhibited the best binding energy (-8.724 kcal/mol) compared with to standard drugs (Cefoxitin; -5.985 kcal/mol, Oxacillin; -5.222 kcal/mol and methiclin -3.08 kcal/mol) and other phytoligands (protocatechuic acid -4.473 kcal/mol and Hesperetin -4.191 kcal/mol) tested. Hence, Chlorogenic acid could be studied as promising lead candidates acting as allosteric effectors of PBP2a that might be subjecte d to further structural optimization to enhance their biological activity and hence been synthesized and tested to evaluate their actual biological activity.