Q2 Values Research Articles

Electromagnetic |GE/GM| ratios of hyperons at large timelike q2

In recent years, it has become possible to measure not only the magnitude of the electric (GE) and magnetic (GM) form factors of spin 12 baryons, but also to measure the relative phases of those quantities in the timelike kinematic region. Aiming to interpret present |GE/GM| data on hyperons of the baryon octet, as well as to predict future data, we present model calculations of that ratio for large invariant 4-momentum square q2 in the timelike region (q2>0). Without any further parameter fitting, we extend to the timelike region a covariant quark model previously developed to describe the kinematic spacelike region (q2≤0) of the baryon octet form factors. The model takes into account both the effects of valence quarks and the excitations of the meson cloud which dresses the baryons. This application to the timelike region assumes an approximation based on unitarity and analyticity that is valid only in the large q2 region. Using the recent data from BESIII we establish the regime of validity of this approximation. We report here that our results for the effective form factor (combination of |GE| and |GM|) are in good agreement with the data already for q2 values above 15 GeV2. In addition, a more conservative onset of the validity of the approximation is provided by the newly available |GE/GM| data which suggest that our predictions may be compared against data for q2≥ 20 GeV2. This is expected in the near future, when the range of the present measurements is expanded to the 20–50 GeV2 region.

Machine learning approaches in designing anti-HIV nitroimidazoles: 2D/3D QSAR, kNN-MFA, docking, dynamics, PCA analysis and MMGBSA studies

In this study, newly synthesized 20 nitroimidazole derivatives were subjected to 2D and 3D Quantitative Structure-Activity Relationship (QSAR) study to investigate their anti-HIV activity against both ROD and IIIB strains. Later, proposed hypothesis was virtually proved by further in-silico studies. In statistically significant 2D-QSAR models r2 values for IIIB strains 0.9241 and for ROD strains 0.9412 with corresponding q2 values of 0.7706 and 0.8299, were obtained, respectively. Different models were constructed using three different kNN-MFA 3D QSAR approaches such as SW-FB, SA score, and GA. By using the generated hypothesis, newer analogues of nitroimidazole derivatives was designed and molecular modelling studies were conducted to prove the hypothesis. The three molecules were displayed the good docking scores compared to the reference molecule. The stabilities of docked complexes were analyzed by MD simulations and MMGB/SA calculations. These results offer insightful design guidance for novel anti-HIV compounds synthesis and suggest interesting directions for future pharmaceutical research.

Biomarker patterns and mechanistic insights into hypothermia from a postmortem metabolomics investigation

Postmortem metabolomics holds promise for identifying crucial biological markers relevant to death investigations and clinical scenarios. We aimed to assess its applicability in diagnosing hypothermia, a condition lacking definitive biomarkers. Our retrospective analysis involved 1095 postmortem femoral blood samples, including 150 hypothermia cases, 278 matched controls, and 667 randomly selected test cases, analyzed using UHPLC-QTOF mass spectrometry. The model demonstrated robustness with an R2 and Q2 value of 0.73 and 0.68, achieving 94% classification accuracy, 92% sensitivity, and 96% specificity. Discriminative metabolite patterns, including acylcarnitines, stress hormones, and NAD metabolites, along with identified pathways, suggest that metabolomics analysis can be helpful to diagnose fatal hypothermia. Exposure to cold seems to trigger a stress response in the body, increasing cortisol production to maintain core temperature, possibly explaining the observed upregulation of cortisol levels and alterations in metabolic markers related to renal function. In addition, thermogenesis seems to increase metabolism in brown adipose tissue, contributing to changes in nicotinamide metabolism and elevated levels of ketone bodies and acylcarnitines, these findings highlight the effectiveness of UHPLC-QTOF mass spectrometry, multivariate analysis, and pathway identification of postmortem samples in identifying metabolite markers with forensic and clinical significance. The discovered patterns may offer valuable clinical insights and diagnostic markers, emphasizing the broader potential of postmortem metabolomics in understanding critical states or diseases.

Exploring the Nexus of Distance Learning Satisfaction: Perspectives from Accounting Students in Serbian Public Universities During the Pandemic

AbstractAmid the COVID-19 pandemic, teachers globally transitioned to distance learning, presenting significant challenges, particularly in developing countries. In that sense, the study investigates the usefulness and satisfaction (US) of distance learning (DL) among accounting students in Serbian public universities during the COVID-19 pandemic. The research focuses on three key factors affecting perceived US derived from existing literature, including teaching process quality (TPQ), technical qualities of distance learning platforms (TQ), and compatibility with social and pandemic conditions (CSPC). The developed theoretical model is predominantly based on the user satisfaction model. Data from 373 surveyed students, gathered through a validated questionnaire, underwent analysis using the partial least squares structural equation model (PLS-SEM). Results reveal that CSPC and TPQ significantly impact the US, whereas TQ has a minor and statistically insignificant effect. The R2 value indicates these three constructs explain a significant portion of the variance for the US, with moderate effects of CSPC and TPQ and a small effect of TQ, indicated by f2 values. Moreover, the model is found to be significantly predictive, according to the Q2 value. Besides theoretical implications, the study suggests important practical implications for university management, emphasizing regular student surveys, continuous improvement of DL systems, and effective and continuous teacher training as the vital measures for enhancing teaching quality. Improving TPQ and TQ also impacts the Serbian economy by fostering workforce development, enhancing digital transformation, stimulating telecommunication industry growth, driving digital information sector development, attracting investment and innovation, boosting global competitiveness, and promoting lifelong learning.

Bs→μ+μ−γ decay rate at large q2 from lattice QCD

We determine, by means of lattice QCD calculations, the local form factors describing the Bs→μ+μ−γ decay, in the so-called electroquenched approximation. For this analysis we make use of the gauge configurations produced by the ETM Collaboration with Nf=2+1+1 flavor of Wilson-Clover twisted-mass fermions at maximal twist. To obtain the Bs meson form factors, we perform simulations for several heavy-strange meson masses mHs in the range mHs∈[mDs,2mDs], and extrapolate to the physical Bs meson point mBs≃5.367 GeV making use of the HQET scaling laws. We cover the region of large dimuon invariant masses q2>4.16 GeV, and use our results to determine the branching fraction for Bs→μ+μ−γ, which has been recently measured by LHCb in the region q2>4.9 GeV. The largest contribution to the uncertainty in the partial branching fractions at values of q2<4.8 GeV is now due to resonance and other long-distance effects, including those from “charming penguins,” which we estimate by summing over the contributions from the JP=1− charmonium resonances. Published by the American Physical Society 2024

An in silico approach to identify novel and potential Akt1 (protein kinase B-alpha) inhibitors as anticancer drugs.

Akt1 (protein kinase B) has become a major focus of attention due to its significant functionality in a variety of cellular processes and the inhibition of Akt1 could lead to a decrease in tumour growth effectively in cancer cells. In the present work, we discovered a set of novel Akt1 inhibitors by using multiple computational techniques, i.e. pharmacophore-based virtual screening, molecular docking, binding free energy calculations, and ADME properties. A five-point pharmacophore hypothesis was implemented and validated with AADRR38. The obtained R2 and Q2 values are in the acceptable region with the values of 0.90 and 0.64, respectively. The generated pharmacophore model was employed for virtual screening to find out the potential Akt1 inhibitors. Further, the selected hits were subjected to molecular docking, binding free energy analysis, and refined using ADME properties. Also, we designed a series of 6-methoxybenzo[b]oxazole analogues by comprising the structural characteristics of the hits acquired from the database. Molecules D1-D10 were found to have strong binding interactions and higher binding free energy values. In addition, Molecular dynamic simulation was performed to understand the conformational changes of protein-ligand complex.

#2194 Targeted metabolomics analysis identifies a specific metabolomic profile in patients with early chronic kidney disease

Abstract Background and Aims The complex interconnections between various metabolites and the kidney play a major role in the pathogenesis of chronic kidney disease (CKD). Hence, by the assessment of the relationship between metabolites and the kidney, more accurate biomarkers could be discovered to prevent and detect CKD at an early stage. The primary goal of this study was to find new serum and urine biomarkers for the diagnosis of early CKD by conducting a metabolomic profiling through targeted metabolomics of both blood and urine in patients with CKD. Method This cross-sectional study comprised 80 patients with CKD who were categorized into six subgroups (G1-G6) based on their eGFR according to the KDIGO Guidelines. Additionally, 20 healthy individuals were included as healthy control participants (group C). Ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry was used to perform serum and urine metabolomic profiling. The 6 metabolites evaluated by this study were selected based on the results of a previous untargeted analysis research. Both blood and urine samples underwent multivariate analysis, which was subsequently followed by univariate analysis and quantitative analysis. Initially, the PLSDA score plot and VIP score were utilized. The application of the cross-validation procedure to the initial set of 6 molecules provided outstanding results, including high accuracy, high R² values, and a significant Q2 value. Hence, the model might be deemed as predictive. Random Forest analysis was utilized to conduct biomarker analysis and prediction. The specificity and sensitivity of the compounds identified as possible biomarkers were assessed using the Receiver Operating Characteristic (ROC) curve and the area under the curve (AUC). Results There were strong positive correlations between eGFR and serum levels of Arginine (p < 0.05) and L-Acetylcarnitine (p < 0.05). Of note, the amounts of L-Methionine, L-Phenylalanine, Kynurenic acid, and Indoxyl sulfate in serum showed a progressive increase from group C to subgroups G1-G5. Furthermore, L-Methionine, L-Phenylalanine, Kynurenic acid, and Indoxyl sulfate exhibited a negative correlation with eGFR (p < 0.05). In addition, there was a positive correlation between the levels of urine Arginine, L-Methionine, and L-Phenylalanine and eGFR (p < 0.05). Urine levels of Kynurenic acid, Indoxyl sulfate, and L-Acetylcarnitine were higher in groups G4 and G5, as compared to groups G1-G3b and C, respectively. The urinary concentrations of Indoxyl sulfate, Kynurenic acid, Indoxyl sulfate, and L-Acetylcarnitine exhibited a negative correlation with eGFR (p < 0.05). Conclusion The current study shows that serum and urinary metabolites levels in patients with CKD display an alteration in production, secretion, and excretion in all CKD stages. There was demonstrated a distinct metabolomic profile in the course of CKD which points to novel biomarkers useful for the diagnosis and progression evaluation of CKD from its early stages.

Catalytic Activity of 2-Imino-1,10-phenthrolyl Fe/Co Complexes via Linear Machine Learning.

In anticipation of the correlations between catalyst structures and their properties, the catalytic activities of 2-imino-1,10-phenanthrolyl iron and cobalt metal complexes are quantitatively investigated via linear machine learning (ML) algorithms. Comparatively, the Ridge Regression (RR) model has captured more robust predictive performance compared with other linear algorithms, with a correlation coefficient value of R2= 0.952 and a cross-validation value of Q2= 0.871. It shows that different algorithms select distinct types of descriptors, depending on the importance of descriptors. Through the interpretation of the RR model, the catalytic activity is potentially related to the steric effect of substituents and negative charged groups. This study refines descriptor selection for accurate modeling, providing insights into the variation principle of catalytic activity.

Arylamines QSAR-Based Design and Molecular Dynamics of New Phenylthiophene and Benzimidazole Derivatives with Affinity for the C111, Y268, and H73 Sites of SARS-CoV-2 PLpro Enzyme.

A non-structural SARS-CoV-2 protein, PLpro, is involved in post-translational modifications in cells, allowing the evasion of antiviral immune response mechanisms. In this study, potential PLpro inhibitory drugs were designed using QSAR, molecular docking, and molecular dynamics. A combined QSAR equation with physicochemical and Free-Wilson descriptors was formulated. The r2, q2, and r2test values were 0.833, 0.770, and 0.721, respectively. From the equation, it was found that the presence of an aromatic ring and a basic nitrogen atom is crucial for obtaining good antiviral activity. Then, a series of structures for the binding sites of C111, Y268, and H73 of PLpro were created. The best compounds were found to exhibit pIC50 values of 9.124 and docking scoring values of -14 kcal/mol. The stability of the compounds in the cavities was confirmed by molecular dynamics studies. A high number of stable contacts and good interactions over time were exhibited by the aryl-thiophenes Pred14 and Pred15, making them potential antiviral candidates.

Molecular Mechanistic Insights into Dipeptidyl Peptidase-IV Inhibitory Peptides to Decipher the Structural Basis of Activity.

Dipeptidyl peptidase-IV (DPP-IV) inhibiting peptides have attracted increased attention because of their possible beneficial effects on glycemic homeostasis. However, the structural basis underpinning their activities has not been well understood. This study combined computational and in vitro investigations to explore the structural basis of DPP-IV inhibitory peptides. We first superimposed the Xaa-Pro-type peptide-like structures from several crystal structures of DPP-IV ligand-protein complexes to analyze the recognition interactions of DPP-IV to peptides. Thereafter, a small set of Xaa-Pro-type peptides was designed to explore the effect of key interactions on inhibitory activity. The intramolecular interaction of Xaa-Pro-type peptides at the first and third positions from the N-terminus was pivotal to their inhibitory activities. Residue interactions between DPP-IV and residues of the peptides at the fourth and fifth positions of the N-terminus contributed significantly to the inhibitory effect of Xaa-Pro-type tetrapeptides and pentapeptides. Based on the interaction descriptors, quantitative structure-activity relationship (QSAR) studies with the DPP-IV inhibitory peptides resulted in valid models with high R2 values (0.90 for tripeptides; 0.91 for tetrapeptides and pentapeptides) and Q2 values (0.33 for tripeptides; 0.68 for tetrapeptides and pentapeptides). Taken together, the structural information on DPP-IV and peptides in this study facilitated the development of novel DPP-IV inhibitory peptides.

Impact of JLab data on the determination of GPDs at zero skewness and new insights from transition form factors N→Δ

It is well-established now that the generalized parton distributions (GPDs) at zero skewness are playing important roles in some physical process such as elastic electron-nucleon scattering, elastic (anti)neutrino-nucleon scattering, and wide-angle Compton scattering (WACS) via various types of form factors (FFs). In this study, we are going to utilize the recent JLab measurements of the elastic electron-nucleon scattering reduced cross section, namely GMp12, as a touchstone to unravel the tension observed between the measurements of the WACS cross section and the data of the proton magnetic FF GMp. We also investigate the impact of GMp12 data on valence unpolarized GPDs Hvq and Evq at zero skewness by performing some χ2 analyses of the related experimental data. By calculating the electric and scalar quadrupole ratios, REM and RSM, and magnetic transition FF GM*/3GD related to the nucleon-to-delta (N→Δ) transition using the extracted GPDs and comparing the results with corresponding experimental measurements, we show that our results are in an excellent consistency with experiments, indicating the universality property of GPDs. We emphasize that the inclusion of these data in the future analysis of GPDs can significantly affect the extracted GPDs especially their uncertainties at smaller values of Q2. Published by the American Physical Society 2024

Relationship between METS-IR and thyroid cancer incidence in Korea: a nationwide population-based study.

Several previous studies found a positive relationship between metabolic syndrome (MetS) and thyroid cancer (TC) risk. However, there is no research that has studied the relationship between the metabolic score for insulin resistance (METS-IR), a novel surrogate marker for IR, and TC incidence. Thus, we designed this retrospective cohort study to evaluate the relationship between the incidence of TC and METS-IR. We analyzed a cohort of 314,321 Korean adults aged over 40 years who participated in the National Health Screening Program from 2009 to 2010. The individuals were divided into four groups based on METS-IR quartiles. Follow-up was until the diagnosis of TC or death, or until December 31, 2019, if neither. The relationship between METS-IR and TC incidence was analyzed using the Cox proportional-hazards model with multi-variable adjustments. A total of 4,137 participants (1.3%) were diagnosed with TC during a mean follow-up of 9.5 ± 1.5 years. The population with Q1 METS-IR scores showed higher disease-free probabilities than those with Q4 METS-IR scores (p <0.001). The hazard ratio (95% confidential interval) for TC incidence in Q2, Q3, and Q4 METS-IR value were 1.14 (1.05 to 1.25), 1.21 (1.11 to 1.33), and 1.30 (1.18 to 1.42) compared with Q1 of METS-IR, respectively. The incidence of TC tended to increase with increasing METS-IR values in the total population, especially the male population in the restricted cubic spline. In subgroup analysis, the TC risk was more pronounced in the subgroups under 65 and with a BMI < 25 kg/m2. METS-IR was positively correlated with TC incidence in Korea.

Combined 3D-QSAR, molecular docking, ADMET, and drug-likeness scoring of novel diaminodihydrotriazines as potential antimalarial agents

Plasmodium falciparum dihydrofolate reductase (PfDHFR) is a promising therapeutic target for antimalarial interventions. Diaminodihydrotriazine derivatives have demonstrated their efficacy as antimalarial agents by targeting PfDHFR inhibition. In this study, we used a multifaceted approach including 3D-QSAR, in silico pharmacokinetic/ADMET evaluation, molecular docking techniques, and molecular dynamics simulation to explore a series of diaminodihydrotriazine compounds, to identify new antimalarial drug candidates featuring the diaminodihydrotriazine scaffold. Using comparative molecular similarity indices analysis (CoMSIA), we built a robust 3D-QSAR model exhibiting favorable statistical parameters, with Q2 and R2 values of 0.553 and 0.981, respectively. External validation using a test set yielded an R2pre value of 0.787, confirming the model's predictive ability. In addition, we performed comprehensive ADMET and drug similarity profile analyses for potent designed compounds, yielding promising results. Molecular docking studies further complemented our investigation facilitating the elucidation of optimal interaction modes between these inhibitors and their target receptor. Additionally, molecular dynamics simulation was conducted for the promising compound 8a. This study not only provides valuable insights into the design of new antimalarial agents but also highlights the importance of computational methods for accelerating drug discovery processes, particularly in the context of infectious disease control.

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach.

Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

A computational model of motion sickness dynamics during passive self-motion in the dark.

Predicting the time course of motion sickness symptoms enables the evaluation of provocative stimuli and the development of countermeasures for reducing symptom severity. In pursuit of this goal, we present an Observer-driven model of motion sickness for passive motions in the dark. Constructed in two stages, this model predicts motion sickness symptoms by bridging sensory conflict (i.e., differences between actual and expected sensory signals) arising from the Observer model of spatial orientation perception (stage 1) to Oman's model of motion sickness symptom dynamics (stage 2; presented in 1982 and 1990) through a proposed "Normalized Innovation Squared" statistic. The model outputs the expected temporal development of human motion sickness symptom magnitudes (mapped to the Misery Scale) at a population level, due to arbitrary, 6-degree-of-freedom, self-motion stimuli. We trained model parameters using individual subject responses collected during fore-aft translations and off-vertical axis of rotation motions. Improving on prior efforts, we only used datasets with experimental conditions congruent with the perceptual stage (i.e., adequately provided passive motions without visual cues) to inform the model. We assessed model performance by predicting an unseen validation dataset, producing a Q2 value of 0.91. Demonstrating this model's broad applicability, we formulate predictions for a host of stimuli, including translations, earth-vertical rotations, and altered gravity, and we provide our implementation for other users. Finally, to guide future research efforts, we suggest how to rigorously advance this model (e.g., incorporating visual cues, active motion, responses to motion of different frequency, etc.).

A Combination of Pharmacophore Generation, Ligand-based Virtual Screening, Atom-based 3D-QSAR, and Molecular Docking Studies on Febuxostat-based Amides Analogues as Anti-inflammatory Agents.

A defence mechanism of the body includes inflammation. It is a process through which the immune system identifies, rejects, and starts to repair foreign and damaging stimuli. In the world, chronic inflammatory disorders are the leading cause of death. To obtain optimized pharmacophore, previously reported febuxostat- based anti-inflammatory amide derivatives series were subjected to pharmacophore hypothesis, ligand-based virtual screening, and 3D-QSAR studies in the present work using Schrodinger suite 2022-4. QuikProp module of Schrodinger was used for ADMET prediction, and HTVS, SP, and XP protocols of GLIDE modules were used for molecular docking on target protein (PDB ID:3LN1). Utilising 29 compounds, a five-point model of common pharmacophore hypotheses was created, having pIC50 ranging between 5.34 and 4.871. The top pharmacophore hypothesis AHHRR_ 1 model consists of one hydrogen bond acceptor, two hydrophobic groups and two ring substitution features. The hypothesis model AHHRR_1 underwent ligand-based virtual screening using the molecules from Asinex. Additionally, a 3D-QSAR study based on individual atoms was performed to assess their contributions to model development. The top QSAR model was chosen based on the values of R2 (0.9531) and Q2 (0.9424). Finally, four potential hits were obtained by molecular docking based on virtual screening. The virtual screen compounds have shown similar docking interaction with amino acid residues as shown by standard diclofenac sodium drugs. Therefore, the findings in the present study can be explored in the development of potent anti-inflammatory agents.

Metabolic and elemental profiling as potential discriminating features among the black mahlab seeds (Monechma ciliatum) grown in three different regions.

Black mahlab (Monechma ciliatum) seed is a rich source of metabolites and minerals and is usually believed to have a similar composition between different areas of cultivation. Until now, no studies have assessed changes in black mahlab seeds (BMSs) to determine those constituents that help to discriminate them according to geographical origin. The present study attempted to compare the metabolomics and elemental profiles of BMSs of different geographical origins and identified the potential markers using ultrahigh-performance liquid chromatography quadrupole Orbitrap tandem mass spectrometry (UHPLC-Q-Orbitrap-MS2), and inductively coupled plasma mass spectrometry (ICP-MS) techniques and established the chemometric model to identify the potential markers and discriminate them according to cultivation sites. In this work, data from metabolites analysis by UHPLC-Q-Orbitrap-MS2 and multi-elemental data obtained from ICP-MS were combined with chemometrics for tracing the geographical origin of BMSs. Principal component analysis (PCA) was used to evaluate the overall grouping of samples. In contrast, partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed for authentication. PLS-DA and OPLS-DA models were fully validated (R2Y and Q2 values > 0.5). Variable importance of various projections was applied to obtain valuable data about differential elements (seven markers were identified) and metabolites (23 markers were identified) with high discrimination potential. The outcomes presented in this study serve as an appropriate framework for developing novel discrimination approaches in food origin screening.

Computational integration for antifungal 1,2,4-triazole inhibitors design: QSAR, molecular docking, molecular dynamics simulations, ADME/Tox, and retrosynthesis studies

Fungal infections are a growing public health problem worldwide. Despite the availability of several medicines, their efficacy is still constrained by fungal resistance. This research conducted the 2D/3D-QSAR analysis on twenty-nine triazole molecules previously evaluated for their antifungal activity. The HQSAR/B-H, CoMFA and CoMSIA models were built using twenty-three molecules in the training set. They show high Q2 values (0.646, 0.564 and 0.561, respectively) and important R2 values (0.764, 0.805 and 0.787, respectively). The predictive capacity of the established models was validated by external validation; they performed well. The contour maps derived from the HQSAR/B-H, CoMFA and CoMSIA models provide more detail to identify favorable and unfavorable groupings impacting the activity. Then, 4 proposed new triazole molecules with significant antifungal activity were suggested. In addition, the molecular docking results showed good binding energies and interactions of the proposed inhibitors in the active site of the receptor studied. The molecular dynamics and MM/PBSA methods confirmed and validated the molecular docking results. The new triazole molecules were evaluated for their oral bioavailability and toxicity using ADME/Tox properties. Finally, the retrosynthesis method created a synthetic pathway for the candidate inhibitor Z1.

Incorporation of Partial Least Squares-Discriminant Analysis with Ultra-High-Performance Liquid Chromatography Diode-Array Detector for Authentication of Skin Gelatine Sources

This research seeks to (1) authenticate sources of skin gelatine by combining putative 17 aminoacids (AAs) analysis with chemometrics by Ultra-High-Performance Liquid ChromatographyDiode-Array Detector (UHPLC-DAD) and (2) create AA profiles in skin gelatines. Theclassification capability of partial least square-discriminant analysis (PLS-DA) models wasassessed to determine the most effective discriminant model. Principal component analysis(PCA) with quartimax rotation was utilised to accurately organise gelatine clusters and assign thesignificantly contributing AAs to each cluster. The PLS-DA model with 13 AAs (PLS-DAVIPAA)outperformed the PLS-DA model with 17 AAs (PLS-DAAA) because its R2Y (0.938), R2X (0.881),and Q2 (0.929) values were greater. With 13 significant AAs, the PLS-DAVIPAA model obtainedcluster classification accuracy of 100% on training and cross-validation datasets and 93.3% ontesting and verification datasets. The chemical structure of gelatines may shed light on theinteractions between AAs. Following six quartimax rotations, the gelatines were groupedcorrectly. The PCA showed the dominant presence of these AAs: L-Valine, L-Phenylalanine andL-Tyrosine in porcine gelatine; Glycine, L-Threonine, L-Arginine, L-Methionine, L-Histidine andL-Serine in fish gelatine; and L-Hydroxyproline, L-Leucine and L-Proline in bovine gelatine. Theauthority could use this technique to set a standard for authenticating skin gelatine samples.

Clustering of atoms relative to vector space in the Z-matrix coordinate system and 'graphical fingerprint' analysis of 3D pharmacophore structure.

The behavior of a molecule within its environment is governed by chemical fields present in 3D space. However, beyond local descriptors in 3D, the conformations a molecule assumes, and the resulting clusters also play a role in influencing structure-activity models. This study focuses on the clustering of atoms according to the vector space of four atoms aligned in the Z-Matrix Reference system for molecular similarity. Using 3D-QSAR analysis, it was aimed to determine the pharmacophore groups as interaction points in the binding region of the β2-adrenoceptor target of fenoterol stereoisomers. Different types of local reactive descriptors of ligands have been used to elucidate points of interaction with the target. Activity values for ligand-receptor interaction energy were determined using the Levenberg-Marquardt algorithm. Using the Molecular Comparative Electron Topology method, the 3D pharmacophore model (3D-PhaM) was obtained after aligning and superimposing the molecules and was further validated by the molecular docking method. Best guesses were calculated with a non-output validation (LOO-CV) method. Finally, the data were calculated using the 'graphic fingerprint' technique. Based on the eLKlopman (Electrostatic LUMO Klopman) descriptor, the Q2 value of this derivative set was calculated as 0.981 and the R2ext value is calculated as 0.998.