Q2 Values Research Articles

Probing the structural requirements for angiotensin II receptor: molecular modeling studies

The present study describes the development of 3D-QSAR studies on angiotensin II receptor based on the selected pharmacophore model. A four-point pharmacophore with one hydrogen bond acceptor (A), one hydrophobic (H), and two aromatic ring features (R) as pharmacophore features was developed by PHASE module. The pharmacophore hypothesis yielded a statistically significant model with good partial least-squares results. Thus, obtained 3D-QSAR model with partial least-squares (PLS) factor regression coefficient value (r2 = 0.9547) for training set and high value of cross-validated correlation coefficient Q2 = 0.7418, root-mean-squared error RMSE = 0.1257. This model was found to yield reliable clues for the further optimization of benzimidazole derivatives in the dataset, which plays an important role in molecular drug design approach.

Hyphenated 3D-QSAR statistical model-drug repurposing analysis for the identification of potent neuraminidase inhibitor.

The Influenza A virus is one of the principle causes of respiratory illness in human. The surface glycoprotein of the influenza virus, neuraminidase (NA), has a vital role in the release of new viral particle and spreads infection in the respiratory tract. It has been long recognized as a valid drug target for influenza A virus infection. Oseltamivir is used as a standard drug of choice for the treatment of influenza. However, the emergence of mutants with novel mutations has increased the resistance to potent NA inhibitor. In the present investigation, we have employed computer-assisted combinatorial techniques in the screening of 8621 molecules from Drug Bank to find potent NA inhibitors. A three-dimensional pharmacophore model was generated from the previously reported 28 carbocylic influenza NA inhibitors along with oseltamivir using PHASE module of Schrödinger Suite. The model generated consists of one hydrogen bond acceptor (A), one hydrogen bond donors (D), one hydrophobic group (H), and one positively charged group (P), ADHP. The hypothesis was further validated for its integrity and significance using enrichment analysis. Subsequently, an atom-based 3D-QSAR model was built using the common pharmacophore hypothesis (CPH). The developed 3D-QSAR model was found to be statistically significant with R2 value of 0.9866 and Q2 value of 0.7629. Further screening was accomplished using three-stage docking process using the Glide algorithm. The resultant lead molecules were examined for its drug-like properties using the Qikprop algorithm. Finally, the calculated pIC50 values of the lead compounds were validated by the AutoQSAR algorithm. Overall, the results from our analysis highlights that lisinopril (DB00722) is predicted to bind better with NA than currently approved drug. In addition, it has the best match in binding geometry conformations with the existing NA inhibitor. Note that the antiviral activity of lisinopril is reported in the literature. However, our paper is the first report on lisinopril activity against influenza A virus infection. These results are envisioned to help design the novel NA inhibitors with an increased antiviral efficacy.

Prediction of Aqueous pK a Values for Guanidine-Containing Compounds Using Ab Initio Gas-Phase Equilibrium Bond Lengths.

In this work, we demonstrate the existence of linear relationships between gas-phase equilibrium bond lengths of the guanidine skeleton of 2-(arylamino)imidazolines and their aqueous pKa value. For a training set of 22 compounds, in the most stable conformation of their lowest energy tautomeric form, three bonds were found to exhibit r2 and q2 values >0.95 and root-mean-squared-error of estimation values ≤0.25 when regressed individually against pKa. The equations describing these one-bond-length linear relationships, in addition to a multiple linear regression model using all three bond lengths, were then used to predict the experimental pKa values of an external test set of further 27 derivatives. The optimal protocol we derive here shows an overall mean absolute error (MAE) of 0.20 and standard deviation of errors of 0.18 for the test set. Predictions for a second test set of diphenyl-based bis(2-iminoimidazolidines) yielded an MAE of 0.27 and a standard deviation of 0.10. The predictive power of the optimal model is further demonstrated by its ability to correct erroneously reported experimental values. Finally, a previously established guanidine model is recalibrated at a new level of theory, and predictions are made for novel phenylguanidine derivatives, showing an MAE of just 0.29. The protocols established and tested here pass both of Roy’s modern and stringent MAE-based criteria for a “good” quantitative structure–activity relationship/quantitative structure–property relationship model predictivity. Notably, the ab initio bond length high correlation subset protocol developed in this work demonstrates lower MAE values than the Marvin program by ChemAxon for all test sets.

In-line Fourier-transform infrared spectroscopy as a versatile process analytical technology for preparative protein chromatography

Fourier-transform infrared spectroscopy (FTIR) is a well-established spectroscopic method in the analysis of small molecules and protein secondary structure. However, FTIR is not commonly applied for in-line monitoring of protein chromatography. Here, the potential of in-line FTIR as a process analytical technology (PAT) in downstream processing was investigated in three case studies addressing the limits of currently applied spectroscopic PAT methods. A first case study exploited the secondary structural differences of monoclonal antibodies (mAbs) and lysozyme to selectively quantify the two proteins with partial least squares regression (PLS) giving root mean square errors of cross validation (RMSECV) of 2.42 g/l and 1.67 g/l, respectively. The corresponding Q2 values are 0.92 and, respectively, 0.99, indicating robust models in the calibration range. Second, a process separating lysozyme and PEGylated lysozyme species was monitored giving an estimate of the PEGylation degree of currently eluting species with RMSECV of 2.35 g/l for lysozyme and 1.24 g/l for PEG with Q2 of 0.96 and 0.94, respectively. Finally, Triton X-100 was added to a feed of lysozyme as a typical process-related impurity. It was shown that the species could be selectively quantified from the FTIR 3D field without PLS calibration. In summary, the proposed PAT tool has the potential to be used as a versatile option for monitoring protein chromatography. It may help to achieve a more complete implementation of the PAT initiative by mitigating limitations of currently used techniques.

Assessing yield and fertilizer response in heterogeneous smallholder fields with UAVs and satellites

Agricultural intensification and efficient use and targeting of fertilizer inputs on smallholder farms is key to sustainably improve food security. The objective of this paper is to demonstrate how high-resolution satellite and unmanned aerial vehicle (UAV) images can be used to assess the spatial variability of yield, and yield response to fertilizer. The study included 48 and 50 smallholder fields monitored during the 2014 and 2015 cropping seasons south-east of Koutiala (Mali), cropped with the five major crops grown in the area (cotton, maize, sorghum, millet and peanuts). Each field included up to five plots with different fertilizer applications and one plot with farmer practice. Fortnightly, in-situ in each field data were collected synchronous with UAV imaging using a Canon S110 NIR camera. A concurrent series of very high-resolution satellite images was procured and these images were used to mask out trees. For each plot, we calculated vegetation index means, medians and coefficients of variation. Cross-validated general linear models were used to assess the predictability of relative differences in crop yield and yield response to fertilizer, explicitly accounting for the effects of fertility treatments, between-field and within-field variabilities. Differences between fields accounted for a much larger component of variation than differences between fertilization treatments.Vegetation indices from UAV images strongly related to ground cover (R2 = 0.85), light interception (R2 = 0.79) and vegetation indices derived from satellite images (R2 values of about 0.8). Within-plot distributions of UAV-derived vegetation index values were negatively skewed, and within-plot variability of vegetation index values was negatively correlated with yield. Plots on shallow soils with poor growing conditions showed the largest within-plot variability. GLM models including UAV derived estimates of light interception explained up to 78% of the variation in crop yield and 74% of the variation in fertilizer response within a single field. These numbers dropped to about 45% of the variation in yield and about 48% of the variation in fertilizer response when lumping all fields of a given crop, with Q2 values of respectively 22 and 40% respectively when tested with a leave-field-out procedure. This indicates that remotely sensed imagery doesn’t fully capture the influence of crop stress and management. Assessment of crop fertilizer responses with vegetation indices therefore needs a reference under similar management. Spatial variability in UAV-derived vegetation index values at the plot scale was significantly related to differences in yields and fertilizer responses. The strong relationships between light interception and ground cover indicate that combining vertical photographs or high-resolution remotely sensed vegetation indices with crop growth models allows to explicitly account for the spatial variability and will improve the accuracy of yield and crop production assessments, especially in heterogeneous smallholder conditions.

¹H-NMR Profiling and Chemometric Analysis of Selected Honeys from South Africa, Zambia, and Slovakia.

Honey is the natural sweet substance produced by honeybee from nectar or honeydew, exhibiting several nutritional and health benefits. It contains a complex mixture of compounds in different proportions, with sugars being the main component. The physicochemical characteristics of ten honeys were evaluated; represented by five, three, and two from South Africa, Slovakia, and Zambia, respectively. The range of values for the pH (3.75–4.38), electrical conductivity (99–659 µS/cm), and moisture content (14.2–17.7%) are within the recommended limits for quality honeys. 1H-NMR (Nuclear Magnetic Resonance) profiling of the honeys in D2O was determined, and the data were analysed by chemometrics. This method is fast, reproducible, and sample pre-treatment is not necessary. The 1H-NMR fingerprints of various chemical shift regions showed similarity or dissimilarity across geographical origins that are useful for identification, detection of adulteration, and quality control. The principal component analysis PCA and partial linear square discriminant analysis PLS-DA of the 1H-NMR profiles successively categorises the honeys into two chemically related groups. The R2 values are higher than the corresponding Q2 values for all samples, confirming the reliability of the model. Honeys in the same cluster contain similar metabolites and belong to the same botanic or floral origin.

Saturation model of DIS: an update

We present the results of new fits to the recently extracted data on F2 at low x with the GBW saturation model and its modification to cover high values of Q2. We find that the model stands the test of time and gives a good description of the data with slightly modified parameters. All the essential elements of the model, especially the saturation scale, are retained.

Structure-Activity Relationship of Xanthones as Inhibitors of Xanthine Oxidase.

Polygala plants contain a large number of xanthones with good physiological activities. In our previous work, 18 xanthones were isolated from Polygala crotalarioides. Extented study of the chemical composition of the other species Polygala sibirica led to the separation of two new xanthones—3-hydroxy-1,2,6,7,8-pentamethoxy xanthone (A) and 6-O-β-d-glucopyranosyl-1,7-dimethoxy xanthone (C)—together with 14 known xanthones. Among them, some xanthones have a certain xanthine oxidase (XO) inhibitory activity. Furthemore, 14 xanthones as XO inhibitors were selected to develop three-dimensional quantitative structure–activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. The CoMFA model predicted a q2 value of 0.613 and an r2 value of 0.997. The best CoMSIA model predicted a q2 value of 0.608 and an r2 value of 0.997 based on a combination of steric, electrostatic, and hydrophobic effects. The analysis of the contour maps from each model provided insight into the structural requirements for the development of more active XO inhibitors.

CoMFA and CoMSIA-based designing of resveratrol derivatives as amyloid-beta aggregation inhibitors against Alzheimer's disease

Resveratrol (3,4′,5-trihydroxystilbene) has been reported to promote degradation of intracellular amyloid-beta (Aβ) aggregates in Alzheimer’s disease (AD). In the present paper, we report comparative molecular field analysis (CoMFA)-based and comparative molecular similarity indices analysis (CoMSIA)-based three-dimensional quantitative structure–activity relationship (3D-QSAR) study of 30 resveratrol derivatives. The best predictive CoMFA model with correlation coefficients (r2) of 0.905 and 0.908 for training and test sets, respectively, and cross-validated correlation coefficient (q2) of 0.713 was obtained using training and test set of 20 and 10 compounds, respectively. Using the same sets of compounds, the best CoMSIA model exhibiting steric, electrostatic, and hydrophobic fields revealed r2 values of 0.938 and 0.966 for training and test sets, respectively, and q2 value of 0.726. The features obtained from contour maps of both CoMFA and CoMSIA models were used to design a library of hybrid resveratrol derivatives based on a set of six potent Aβ aggregation inhibitors. The inhibitory potential of the most active compound of this library was found to be close to that of the most active compound of the data set. The results obtained from the present study makes a foundation for the development of effective drugs against Aβ aggregation.

First report on 3D-QSAR and molecular dynamics based docking studies of GCPII inhibitors for targeted drug delivery applications

Prostate Specific Membrane Antigen (PSMA) or Glutamate carboxypeptidase II (GCPII) has been identified as an important target in diagnosis and therapy of prostate cancer. Among several types of inhibitors, urea based inhibitors are the most common and widely employed in preclinical and clinical studies. Computational studies have been carried out to uncover active sites and interaction of PSMA inhibitors with the protein by modifying the core structure of the ligand. Analysis of the literature, however, show lack of 3-D quantitative structure activity relationship (QSAR) and molecular dynamics based molecular docking study to identify structural modifications responsible for better GCPII inhibitory activity. The present study aims to fulfil this gap by analysing well known PSMA inhibitors reported in the literature with known experimental PSMA inhibition constants. Also in order to validate the in silico study, a new GCPII inhibitor 7 was designed, synthesized and experimental PSMA enzyme inhibition was evaluated by using freshly isolated PSMA protein from human cancer cell line derived from lymph node, LNCaP.3D-QSAR CoMFA models on 58 urea based GCPII inhibitors were generated, and the best correlation was obtained in Gast-Huck charge assigning method with q2, r2 and predictive r2 values as 0.592, 0.995 and 0.842 respectively. Moreover, steric, electrostatic, and hydrogen bond donor field contribution analysis provided best statistical values from CoMSIA model (q2, r2 and predictive r2 as 0.527, 0.981 and 0.713 respectively). Contour maps study revealed that electrostatic field contribution is the major factor for discovering better binding affinity ligands. Further molecular dynamic assisted molecular docking was also performed on GCPII receptor (PDB ID 4NGM) and most active GCPII inhibitor, DCIBzL. 4NGM co-crystallised ligand, JB7 was used to validate the docking procedure and the amino acid interactions present in JB7 are compared with DCIBzL. The results suggest that Arg210, Asn257, Gly518, Tyr552, Lys699, and Tyr700 amino acid residues may play a crucial role in GCPII inhibition. Molecular Dynamics Simulation provides information about docked pose stability of DCIBzL. By combination of CoMFA-CoMSIA field analysis and docking interaction analysis studies, conclusive SAR was generated for urea based derivatives based on which GCPII inhibitor 7 was designed and chemically synthesized in our laboratory. Evaluation of GCPII inhibitory activity of 7 by performing NAALADase assay provided IC50 value of 113 nM which is in close agreement with in silico predicted value (119 nM). Thus we have successfully validated our 3D-QSAR and molecular docking based designing of GCPII inhibitors methodology through biological experiments. This conclusive SAR would be helpful to generate novel and more potent GCPII inhibitors for drug delivery applications.

PREDICTING POTENT ANTIHYPERTENSIVE COMPOUNDS OF PYRIDINE DERIVATIVES BY MOLECULAR MODELING APPROACH

A quantitative structure activity relationship study was performed on a series of pyridine substituted compounds as angiotensin II receptors for establishing quantitative relationship between biological activity and their physicochemical structural properties. Partial least square (PLS) methodology coupled with stepwise forward backward method were applied to derive QSAR models, which were further validated for statistical significance and predictive ability by internal and external validation. A 2D QSAR model was generated with correlation coefficient (r2) of 0.8874, significant cross-validated correlation coefficient (q2) of 0.7498, r2 for external test set (pred_r2) 0.7869. A Zscore_r2 value of 3.9853, Zscore_q2 value of 2.8642 and Zscore_ pred_r2 value of 1.0583 statistically validates the significance of the obtained QSAR model. Molecular field analysis (MFA) was used to construct the best 3D-QSAR model showing good correlative and predictive capabilities in terms of q2= 0.7264 and pred_r2= 0.6988. The quantitative structure activity relationship study provides important structural insights in the designing of potent antihypertensive molecules.

Study on serum markers of prostate cancer by nuclear magnetic resonance-based metabolomics

Objective To investigate the clinical value of serum metabolomic profile of prostate cancer using nuclear magnetic resonance-based metabolomics. Methods The retrospective case control study was adopted.The clinical data of 31 patients with prostate cancer, 28 patients of prostatic hyperplasia and 31 healthy volunteers were enrolled in this study from May 2016 to May 2017 at the first affiliated hospital of Xinjiang medical university. In PCa group, the mean age was 66.3 years old, ranging 53-80 years old. In BPH group, the mean age was 59.3 years old, ranging 46-75 years old. In volunteer group, the mean age was 47.8 years old, ranging 35-62 years old.. The serum of the 3 groups was measured by 1H-NMR spectroscopy. Multivariate statistical analysis was used to analyze the serum differential metabolism of the 3 groups, including principal components analysis(PCA), partial least squares discriminant analysis(PLS-DA)and orthogonal partial least squares discriminant analysis(OPLS-DA). Results The multivariate statistical analysis of PCA that the rate of the first principal component 1(PC1) was 53.24%, the second principal component 2(PC2) was 25.31% and the cumulative contribution rate was 78.55%.Results of PLS-DA showed that partial data overlap of the three groups, but the separation trend was appeared.The variance of X(R2X) and Y(R2Y) matrixes and predictive value Q2 were 0.67, 0.60, and 0.42.The results of OPLS-DA showed that the difference among the PCa group and BPH group, healthy group were obvious. The separation trend were appeared and the differential metabolites could be screened effectively. The R2X、R2Y and Q2 was 0.24, 0.57, 0.21 and 0.30, 0.65, 0.36. 26 different serum metabolites were detected in the 3 groups, including citric acid, arginine, threonine, citrulline, glutamine, lactic acid, alanine, unsaturated fats, glycoprotein etc. Conclusions Compared with BPH group and healthy group, the serum of prostate cancer patients showed significant differences in metabolism. Nuclear magnetic resonance metabolomics analysis can effectively distinguish these serum metabolic differences. Key words: Prostate cancer; Nuclear magnetic resonance; Serum biomarkers; Metabolomics; Diagnosis

D → π and D → K semileptonic form factors with Nf = 2 + 1 + 1 twisted mass fermions

We present a lattice determination of the vector and scalar form factors of the D → π(K)lv semileptonic decays, which are relevant for the extraction of the CKM matrix elements |Vcd| and |Vcs| from experimental data. Our analysis is based on the gauge configurations produced by the European Twisted Mass Collaboration with Nf = 2 + 1 +1 flavors of dynamical quarks. We simulated at three different values of the lattice spacing and with pion masses as small as 210 MeV. The matrix elements of both vector and scalar currents are determined for a plenty of kinematical conditions in which parent and child mesons are either moving or at rest. Lorentz symmetry breaking due to hypercubic effects is clearly observed in the data and included in the decomposition of the current matrix elements in terms of additional form factors. After the extrapolations to the physical pion mass and to the continuum limit the vector and scalar form factors are determined in the whole kinematical region from q2 = 0 up to [see formula in PDF] accessible in the experiments, obtaining a good overall agreement with experiments, except in the region at high values of q2 where some deviations are visible.

Light meson form factors at high Q2 from lattice QCD

Measurements and theoretical calculations of meson form factors are essential for our understanding of internal hadron structure and QCD, the dynamics that bind the quarks in hadrons. The pion electromagnetic form factor has been measured at small space-like momentum transfer |q2| < 0.3 GeV2 by pion scattering from atomic electrons and at values up to 2.5 GeV2 by scattering electrons from the pion cloud around a proton. On the other hand, in the limit of very large (or infinite) Q2 = −q2, perturbation theory is applicable. This leaves a gap in the intermediate Q2 where the form factors are not known. As a part of their 12 GeV upgrade Jefferson Lab will measure pion and kaon form factors in this intermediate region, up to Q2 of 6 GeV2. This is then an ideal opportunity for lattice QCD to make an accurate prediction ahead of the experimental results. Lattice QCD provides a from-first-principles approach to calculate form factors, and the challenge here is to control the statistical and systematic uncertainties as errors grow when going to higher Q2 values. Here we report on a calculation that tests the method using an ηs meson, a ’heavy pion’ made of strange quarks, and also present preliminary results for kaon and pion form factors. We use the nf = 2 + 1 + 1 ensembles made by the MILC collaboration and Highly Improved Staggered Quarks, which allows us to obtain high statistics. The HISQ action is also designed to have small dicretisation errors. Using several light quark masses and lattice spacings allows us to control the chiral and continuum extrapolation and keep systematic errors in check.

PREDICTION OF SOIL AVAILABLE PHOSPHOROUS CONTENT USING SPECTRA-RADIOMETER AND GIS IN SOUTHERN OF IRAQ

In this study, soil samples were collected from two locations: Samawa and Rumetha in southern Iraq. The samples from each location were split into two datasets: calibration set and validation set. A representative soil sample for each location was chosen for application of 5 levels of potassium phosphate fertilizer in 3 replications. Vis-NIR reflectance (350-2500 nm) and GIS-Kriging were used in combination with Partial Least Square (PLS) to predict soil available P. According to the results of this study, three wavelength regions were reported as a main sensitive bands for soil available P. The best prediction ability was achieved for Rumetha location at 1400-1600 nm with an R2 of 0.85, lowest RMSE of 1.405, and lowest standard deviation of 1.577 and for Samawa location at 900-1000 nm with an R2 of 0.81, RMSE of 2.666 and lowest standard deviation of 2.879. The capability of the Vis-NIRS- based and GIS-Kriging prediction models were evaluated by using cross-validation values Q2 and R2 between measured and predicted soil available P of each model. The selection principle parameters showed best prediction by NIRS-models with an R2 of 0.79 for Rumetha and 0.75 for Samawa location. While the prediction ability of GIS-Kriging models were in worst with an Q2 of 0.17 for Samawa location and reasonable with an Q2 of 0.58 for Rumetha location. These empirically result is an evident of the superiority of NIRS-based models for prediction soil available P over the GIS-Kriging models.

QUANTITATIVE STRUCTURE - ACTIVITY RELATIONSHIP STUDIES OF SOME BENZIMIDAZOLE ANALOGUES BASING ON PARTIAL LEAST SQUARES METHOD

Quantitative structure-activity relationship studies on benzimidazole derivatives as angiotensin II receptor were performed to explore the structural requirements for biologic activity. QSAR model obtained was statistically significant with r2 value of 0.8847, cross validated correlation coefficient q2 value of 0.7538 and pred_r2 value of 0. 8135, coefficient of correlation of predicted data set (pred_r2se) 0.5487 was developed by stepwise PLSR method. The observed activity of these molecules was consistent with the model, suggesting that the model may be useful in the design of potent antihypertensive agents.

Computational Analysis of Artimisinin Derivatives on the Antitumor Activities

The study on antitumor activities of artemisinin and its derivatives has been closely focused on in recent years. Herein, 2D and 3D QSAR analysis was performed on the basis of a series of artemisinin derivatives with known bioactivities against the non-small-cell lung adenocarcinoma A549 cells. Four QSAR models were successfully established by CoMSIA, CoMFA, topomer CoMFA and HQSAR approaches with respective characteristic values q2 = 0.567, R2 = 0.968, ONC = 5; q2 = 0.547, R2 = 0.980, ONC = 7; q2 = 0.559, R2 = 0.921, ONC = 7 and q2 = 0.527, R2 = 0.921, ONC = 6. The predictive ability of CoMSIA with r2 = 0.991 is the best one compared with the other three approaches, such as CoMFA (r2 = 0.787), topomer CoMFA (r2 = 0.819) and HQSAR (r2 = 0.743). The final QSAR models can provide guidance in structural modification of artemisinin derivatives to improve their anticancer activities.

A Novel Computational Method for Biomedical Binary Data Analysis: Development of a Thyroid Disease Index Using a Brute‐Force Search with MLR Analysis

The thyroid disease index (TDI), which estimates thyroid disease progress based on hormone concentration measurements and hormone pattern changes, was developed. In this study, we measured concentrations of hormone profiles in the androgen and estrogen metabolic pathways from 23 patients with thyroid disease, as well as 20 unaffected people. We illustrated that the hormones 2‐hydroxyestrone (2‐OH‐E1), 2‐hydroxyestradiol (2‐OH‐E2), 2‐methoxyestrone (2‐MeO‐E1), 2‐methoxyestradiol (2‐MeO‐E2), and 2‐methoxyestradiol‐3‐methylether (2‐MeO‐E2‐3‐methylether) are related to the development of thyroid disease through t‐tests. Though the concentration levels of these hormones generally increase as the disease progresses, big fluctuations cause the determining of a disease's progress by measuring hormone levels to be difficult. The differing patterns between the correlation matrices of the disease and control groups possibly indicates changes in hormone releasing patterns during the thyroid disease's progress. Because of a lack of progressive experimental data on thyroid disease, binary data for the two categories (the thyroid disease patients and the control group) was utilized. Binary logistic regression was used to analyze five risk factors associated with thyroid disease, and the highest overall accuracy was 97.7% with three risk factors. Logistic regression models, however, are unable to describe disease progress. Hence, the TDI was developed to estimate thyroid disease progress. An arbitrary ranking of disease progress was generated for the TDI equation. The ranking contained a total number of 29 030 400 entries with six stages from the control group and eight stages from the disease group. Multiple linear regression (MLR) analysis was performed with a brute‐force search. The best result among the MLR runs presented strong correlation (r2 values of 0.840 and q2 values of 0.663) between the selected hormones and the values of the disease progress in the training set. Overall accuracy of our novel method was 90.7%, which is worse than the 97.7% of logistic regression models. Brute‐force search with MLR analysis might classify different types of thyroid disease progress such as thyroid mass (0.8055), goiter (0.8806), thyroid mass which was a thyroid cancer before operation (0.8951 and 0.9112), and cancer (1.001–2.144). The results show that the TDI is a good indicator of thyroid disease progress and that brute‐force search with MLR analysis is useful for biomedical binary data analysis.

In silico screening for identification of pyrrolidine derivatives dipeptidyl peptidase-IV inhibitors using COMFA, CoMSIA, HQSAR and docking studies

To explore the relationship between the structures of substituted pyrrolidine derivatives and their inhibition of dipeptidyl peptidase IV inhibitors. The QSAR, including CoMFA, CoMSIA and HQSAR, were applied to identify the key structures impacting their inhibitory potencies. The CoMFA, CoMSIA and HQSAR with cross-validated correlation coefficient (q2) value of 0.727, 0.870 and 0.939 and r2 value of 0.973, 0.981 and 0.949. Based on the structure-activity relationship revealed by the present study, we have designed a set of novel dipeptidyl peptidase IV inhibitors that showed excellent potencies in the developed models. Thus, our results allowed us to design new derivatives with desired activities.

Prediction of aquatic toxicity of benzene derivatives using molecular descriptor from atomic weighted vectors

Several descriptors from atom weighted vectors are used in the prediction of aquatic toxicity of set of organic compounds of 392 benzene derivatives to the protozoo ciliate Tetrahymena pyriformis (log(IGC50)−1). These descriptors are calculated using the MD-LOVIs software and various Aggregation Operators are examined with the aim comparing their performances in predicting aquatic toxicity. Variability analysis is used to quantify the information content of these molecular descriptors by means of an information theory-based algorithm. Multiple Linear Regression with Genetic Algorithms is used to obtain models of the structure–toxicity relationships; the best model shows values of Q2=0.830 and R2=0.837 using six variables. Our models compare favorably with other previously published models that use the same data set. The obtained results suggest that these descriptors provide an effective alternative for determining aquatic toxicity of benzene derivatives.