Cross-validated Values Research Articles

Prediction of Dissolution of Sustained Release Coated Ciprofloxacin Beads Using Near-infrared Spectroscopy and Process Parameters: a Data Fusion Approach.

The aim of the work is to develop a data fusion model using near-infrared (NIR) and process parameters for the predictions of drug dissolution from controlled release multiparticulate beads. Using a design of experiments, ciprofloxacin-coated beads were manufactured and critical process parameters such as air volume, product temperature, curing temperature, and curing time were measured; environmental humidity was monitored usinga Pyrobuttons®. The NIR spectra were decomposed using principal component analysis (PCA). The PCA scores were fused with process measurements and all variables were autoscaled. The autoscaled variables were regressed against measured dissolution data at 1h and 2h time points; the PLS regression used quadratic and cross terms. The NIR spectra only model using data collected at the end of bead curing generated a PLS model using 5 latent variables with R2 equal to 0.245 and 0.299 and RMSECV 13.23 and 13.12 for the 1h and 2h dissolution time points, respectively. The low R2 and high root mean square error of cross validation (RMSECV) values indicate that NIR spectra alone were insufficient to model the drug release. Similar results were obtained for NIR model using data collected at the end of spraying phase. Models with fused spectral and process data yielded better prediction with R2 above 0.88 and RMSECV less than 5% for the 1h and 2h dissolution time points. The data fusion model predicted dissolution profiles with an error less than 10%.

Towards an optical multisensor system for dairy: Global calibration for fat analysis in homogenized milk

A new approach to constructing global calibration models for optical spectroscopic determination of fat content in normalized milk (homogenized milk with standardized nutrient content) in the region 400–1100 nm has been developed. Following the global modelling approach attempting to cover all possible variability of analyzed object, calibration models were built on a large set of commercial milk samples from many producers, collected in two different countries and varying from each other in nutrient composition and methods of technological processing. Suggested method employs spectral information related to the light scatter effect by colloidal particles of the milk constituents. Due to the application of advanced variable interval selection method, the root mean-square error of cross-validation (RMSECV) value of 0.12% in the range 1.5–6.0% of fat content has been achieved in the calibration by partial least-squares (PLS) regression method. Selected spectral intervals can be used for the development of an inexpensive and compact optical sensor for milk analysis based on light-emitting diodes (LEDs). Global calibration models developed in this study can be used for the practical analysis of fat in various normalized milk samples.

Kernel-Spline Estimation of Additive Nonparametric Regression Model

In this paper, we model the open unemployment rate with the Kernel-Spline model. We investigate and compare the performance of model Kernel-Spline by varying the Kernel function. The performance model has been compared with five Kernel function i.e. Kernel functions Uniform, Epanechnikov, Quartic, Gaussian, and Triweight. For these models, we conducted a comparison based on actual data sets, the unemployment rate in East Java. The best model was chosen based on the Generalized Cross Validation value and the coefficient of determination criteria. The empirical results obtained have shown that Spline-Kernel model by using the Gaussian Kernel better than other models.

A hydrochemically guided landscape classification system for modelling spatial variation in multiple water quality indices: Process-attribute mapping

Spatial variation in landscape attributes can account for much of the variability in water quality relative to land use on its own. Such variation results from the coupling between the dominant processes governing water quality, namely hydrological, redox, and weathering and gradients in key landscape attributes, such as topography, geology, and soil drainage. Despite the importance of ‘process-attribute’ gradients (PAG), few water quality models explicitly account for their influence. Here a processes-based water quality modelling framework is presented that more completely accounts for the role of landscape variability over water quality – Process-Attribute Mapping (PoAM). Critically, hydrochemical measures form the basis for the identification and mapping of effective landscape attributes, producing PAG maps that attempt to replicate the natural landscape gradients governing each dominant process. Application to the province of Southland (31,824 km2), New Zealand, utilised 12 existing geospatial datasets and a total of 28,626 surface water, groundwater, spring, soil water, and precipitation analyses to guide the identification and mapping of 11 individual PAG. The ability of PAGs to replicate regional hydrological, redox, and weathering gradients was assessed on the accuracy with which the hydrochemical indicators of each dominant process (e.g. hydrological tracers, redox indicators) were estimated across 93 long-term surface water monitoring sites (cross-validated R2 values of 0.75–0.95). Given hydrochemical evidence that PAGs replicate actual landscape gradients governing the dominant processes, they were combined with a land use intensity layer and used to estimate steady-state surface water quality. Cross-validated R2 values ranged between 0.81 and 0.92 for median total nitrogen, total oxidised nitrogen, total phosphorus and dissolved reactive phosphorus. Models of particulate species E. coli and total suspended sediment, although reasonable (R2 0.72–0.73), were less accurate, suggesting finer-grained land use, landscape attribute, and/or flow normalised measures are required to improve estimation.

Two- and three-dimensional QSAR studies on hURAT1 inhibitors with flexible linkers: topomer CoMFA and HQSAR.

hURAT1 (human urate transporter 1) is a successful target for hyperuricemia. Recently, the modification work on hURAT1 inhibitors showed that the flexible linkers would benefit biological activity. The study aimed to investigate the contribution of the linkers and give modification strategies on this kind of structures based on QSAR models (HQSAR and topomer CoMFA). The most effective HQSAR and topomer CoMFA models were generated by applying the training set containing 63 compounds, with the cross-validated q2 values of 0.869/0.818 and the non-cross-validated correlation coefficients r2 of 0.951/0.978, respectively. The Y-randomization test was applied to ensure the robustness of the models. The external predictive correlation coefficient (rpred2) grounded on the external test set (21 compounds) of two models was 0.910 and 0.907, respectively. In addition, the models were validated by Golbraikh-Tropsha and Roy methods, as well as other statistical metrics. The results showed that both models were reliable. Topomer CoMFA steric/electrostatic contours and HQSAR atomic contribution maps illustrated the structural features which governed their inhibitory potency. The dependable results could provide important insights to guide the designing of more potential hURAT1 inhibitors.

Predicting Occupational Exposures to Carbon Nanotubes and Nanofibers Based on Workplace Determinants Modeling.

Recent cross-sectional epidemiologic studies have examined the association between human health effects and carbon nanotube and nanofiber (CNT/F) workplace exposures. However, due to the latency of many health effects of interest, cohort studies with sufficient follow-up will likely be needed. The objective of this study was to identify workplace determinants that contribute to exposure and develop predictive models to estimate CNT/F exposures for future use in epidemiologic studies. Exposure measurements were compiled from 15 unique facilities for the metrics of elemental carbon (EC) mass at both the respirable and inhalable aerosol size fractions as well as a quantitative analysis performed by transmission electron microscopy (TEM). These metrics served as the dependent variables in model development. Repeated personal samples were collected from most of the 127 CNT/F worker participants for 252 total observations. Determinants were categorized as company-level or worker-level and used to describe the exposure relationship within the dependent variables. The influence of determinants on variance components was explored using mixed linear models that utilized a backwards stepwise selection process with a lowering of the AIC for model determinant selection. Additional ridge regression models were created that examined predictive performance with and without all two-way interactions. Cross-validation was performed on each model to evaluate the generalizability of its predictive capabilities while predictive performance was evaluated according to the corresponding R2 value and root mean square error (RMSE). Determinants at the company-level that increased exposure included an inadequate or semi-adequate engineering control rating, increasing average CNT/F diameter/length, daily quantities of material handled from 101 g to >1 kg and >1 kg, the use of CNF materials, the industry type of hybrid producer/user, and the expert assessment of a high exposure potential. Worker-level determinants associated with higher exposure included handling the dry-powdered form of CNT/F, handling daily quantities of material >1 kg, direct/indirect exposure, having the job title of engineer, using a respirator, using a ventilated or unventilated enclosure, and the job task of powder handling. The mixed linear models explained >60% of the total variance when using all company- and worker-level determinants to create the three exposure models. The cross-validated RMSE values for each of the three mixed models ranged from 2.50 to 4.23. Meanwhile, the ridge regression models, without all two-way interactions, estimated cross-validated RMSE values of 2.85, 2.23, and 2.76 for the predictive models of inhalable EC, respirable EC, and TEM, respectively. The ridge regression models demonstrated the best performance for predicting exposures to CNT/F for each exposure metric, although they only provided a modest predictive capability. Therefore, it was concluded that the models alone would not be adequate in predicting workplace exposures and would need to be integrated with other methods.

Integrated Ligand and Structure-Based Investigation of Structural Requirements for Silent Information Regulator 1 (SIRT1) Activation.

A series of imidazothiazole and oxazolopyridine derivatives as human Silent Information Regulator 1 (SIRT1) activators were subjected to the integrated 2D and 3D QSAR approaches. The derived 3D QSAR models yielded high cross-validated q2 values of 0.682 and 0.628 for CoMFA and CoMSIA, respectively. The non-cross validated values of r2 training = 0.89; predictive r2 test = 0.69 for CoMFA and r2=0.87; predictive r2 test =0.67 for CoMSIA reflected the statistical significance of the developed model. The steric, electrostatic, hydrophobic and hydrogen bond acceptor interactions have been found important in describing the variation in human SIRT1 activation. Further, 2D QSAR model for the same dataset yielded high statistical significance and derived 2D model's parameters corroborated with the 3D model in terms of features. Derived model was also validated by the crystal structure of active conformation of SIRT1. Developed models may be useful for the identification of potential novel human SIRT1 activators as a therapeutic agent.

Tuning CdTe quantum dots reactivity for multipoint detection of mercury(II), silver(I) and copper(II)

In this work we tuned the surface chemistry of distinctly-sized CdTe quantum dots (QDs), by using a variety of capping ligands with distinctive functional moieties, and combined them in multi-QDs probing solutions for Hg2+, Cu2+ and Ag+. Upon the individual interaction of the distinctly-capped QDs with each of the abovementioned metal ions, several parameters such as Stern-Volmer constants, equilibrium binding constants, the red-shift magnitude and photoluminescence (PL) lifetime values were thoroughly evaluated.Due to their great reactivity, selectivity could become a major issue in the application of semiconductor quantum dots in chemical analysis. Therefore, the combination in the same analysis of multiple QDs, capped with distinct ligands and emitting at different wavelengths, was exploited to guarantee a specific analyte-response profile. When coupled with chemometrics, for data analysis and processing, the developed approach provided enhanced selectivity and accuracy. In fact, the obtained results of all the partial least squares (PLS) models coefficient of determination of cross-validation (R2CV) close to 1, root mean square error of calibration (RMSEC) and root mean square error of cross-validation (RMSECV) values ranging 0.0094–0.15 and 0.013–0.19 mg L−1, respectively. demonstrated the possibility to perform accurate predictions of Hg2+ and Cu2+. Additionally, the sensitivity of the developed models was also calculated and allowed us to conclude which combination of distinctly-capped QDs assured a more sensitive quantification of Cu2+ (sensitivity = 653 L−1 mg for 3-mercaptopropionic acid (MPA)/MPA conjugated probe) and Hg2+ (sensitivity = 14,017 L−1 mg for reduced glutathione (GSH)/MPA-conjugated probe).

Chemometric classification and quantification of cold pressed grape seed oil in blends with refined soybean oils using attenuated total reflectance–mid infrared (ATR–MIR) spectroscopy

Of the cold pressed edible oils, grape seed oil (GSO) is one of the highest quality and, consequently the most expensive oil. Because of that, adulteration by mixing GSOs up with lower price edible oils is frequently seen. The classification and determination of adulterant concentration for GSO is hence a focus of great interest. The aim of this study was to investigate the feasibility of ATR–MIR spectroscopy coupled with the multivariate methodology for qualitative and quantitative analyses of cold pressed GSO adulteration with refined soybean oil (SBO), by using characteristic wavenumber regions. Thirty three pure oils and ninety six blends were analyzed using ATR–MIR spectroscopy in combination with PCA, LDA, SIMCA and PLSR analysis. SIMCA models provided an excellent classification for pure GSO and other samples. The classification limits by ATR–MIR spectroscopy was also below 5%. Quantitative analyses were performed by minimization of RMSE of cross-validation values, which resulted in excellent predictions (R2 > 0.99). The prediction parameters for the sample sets were: the RMSEC were in the range 0.59–2.09% and, RMSECV were in the range 0.92–5.60%, hence on the basis of the PLSR models, quantification of adulterant could be determined at levels <0.59%.

Rapid differentiation of Staphylococcus aureus subspecies based on MALDI-TOF MS profiles.

Staphylococcus aureus encompasses 2 subspecies ( aureus and anaerobius) with significant differences in their epidemiology and pathogenicity. We evaluated the suitability of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) for the rapid identification of both subspecies using a panel of 52 S. aureus isolates (30 subsp. anaerobius and 22 subsp. aureus) recovered from different origins, countries, and years. The on-board library identification system correctly identified 42 of 52 (81%) S. aureus isolates at the species level with score values >2.0. Limited performance was observed for differentiation of S. aureus subspecies (particularly subsp. anaerobius). Visual inspection of MALDI-TOF MS profiles identified 5 subspecies-specific mass peaks ( m/ z 3430 and 6861 in S. aureus subsp. anaerobius, and m/ z 4046, 6890, and 8093 in S. aureus subsp. aureus) with 100% sensitivity and specificity values, which is potentially useful for differentiating these subspecies. The suitability of 3 models, Genetic Analysis (GA), Quick Classifier (QC), and Supervised Neural Network, for automatic identification of both subspecies was evaluated using the Recognition Capability (RC) and Cross Validation (CV) values provided by the on-board ClinProTools software. The GA and QC models reached RC and CV values of 100%. Both models were externally validated using a panel of 26 S. aureus isolates of both subspecies, with both models correctly classifying all isolates of both subspecies. MALDI-TOF MS coupled with ClinProTools software represents a rapid and simple approach for S. aureus subspecies discrimination.

Straightforward Design for Phenoxy-Imine Catalytic Activity in Ethylene Polymerization: Theoretical Prediction

The quantitative structure-activity relationship (QSAR) of 18 Ti-phenoxy-imine (FI-Ti)-based catalysts was investigated to clarify the role of the structural properties of the catalysts in polyethylene polymerization activity. The electronic properties of the FI-Ti catalysts were analyzed based on density functional theory with the M06L/6-31G** and LANL2DZ basis functions. The analysis results of the QSAR equation with a genetic algorithm showed that the polyethylene catalytic activity mainly depended on the highest occupied molecular orbital energy level and the total charge of the substituent group on phenylimine ring. The QSAR models showed good predictive ability (R2) and R2 cross validation (R2cv) values of greater than 0.927. The design concept is “head-hat”, where the hats are the phenoxy-imine substituents, and the heads are the transition metals. Thus, for the newly designed series, the phenoxy-imine substituents still remained, while the Ti metal was replaced by Zr or Ni transition metals, entitled FI-Zr and FI-Ni, respectively. Consequently, their polyethylene polymerization activities were predicted based on the obtained QSAR of the FI-Ti models, and it is noteworthy that the FI-Ni metallocene catalysts tend to increase the polyethylene catalytic activity more than that of FI-Zr complexes. Therefore, the new designs of the FI-Ni series are proposed as candidate catalysts for polyethylene polymerization, with their predicted activities in the range of 35,000–48,000 kg(PE)/mol(Cat.)·MPa·h. This combined density functional theory and QSAR analysis is useful and straightforward for molecular design or catalyst screening, especially in industrial research.

Measurement and prediction of bioconcentration factors of organophosphate flame retardants in common carp (Cyprinus carpio)

The increase in the production and usage plus the toxicity nature of organophosphate flame retardants (OPFRs) has become a concern. However, limited information is available about the bioaccumulation potential of OPFRs in fish. In this study, we determined the 96 h LC50 s, and evaluated the bioaccumulation potential of six most frequently reported OPFRs in gill, kidney, liver, and muscle tissues of common carp (Cyprinus carpio) for 48 d, and a quantitative structure-activity relationship (QSAR) model was developed to predict bioconcentration factors (BCFs) for the remaining 16 OPFRs. The BCFs and half-lives (t1/2) in the tissues ranged from 6.54 (Tris (2-chloroisopropyl) phosphate, (TCPP)) to 528.15 (Tris (2-ethylhexyl) phosphate (TEHP)), and 2.25–5.78 days, respectively. The tissue-specific concentration and BCFs values followed the order of liver > kidney ≥ intestine >> muscle. The proposed QSAR model with a high cross-validated value (Q2(cum)) of 0.930 and a correlation coefficient of 0.94 was obtained and was able to predict log BCF from parameters related to molar volume and isotropic average static field polarizability. The results show that the model has a high level of accuracy, making the proposed approach a suitable method for predicting the log BCF.

PENDEKATAN REGRESI SPLINE UNTUK MEMODELKAN POLA PERTUMBUHAN BERAT BADAN BALITA

The study is aimed to estimate the best spline regression model for toddler’s weight growth patterns. Spline is one of the nonparametric regression estimation method which has a high flexibility and is able to handle data that change in particular subintervals so thus resulting in model which fitted the data. This study uses data of toddler’s weight growth at Posyandu Mekar Sari, Desa Suwug, Kabupaten Buleleng. The best spline regression model is chosen based on the minimum Generalized Cross Validation (GCV) value. The study shows that the best spline regression model for the data is quadratic spline regression model with six optimal knot points. The minimum GCV value is 0,900683471925 with the determination coefficient equals to 0,954609.

KAPABILITAS PROSES DENGAN ESTIMASI FUNGSI DENSITAS KERNEL PADA PRODUKSI DENIM DI PT APAC INTI CORPORA

The quality of production becomes one of the basic factors of consumer decisions in choosing a product. Quality control is needed to control the production process. Control chart is a tool used in performing statistical quality control. One of the alternatives used when the data obtained is not known distribution is analyzed by nonparametric approach based on estimation of kernel density function. The most important thing in estimating kernel density function is optimal bandwidth selection (h) which minimizes Cross Validation (CV) value. Some of the kernel functions used in this research are Rectangular, Epanechnikov, Triangular, Biweight, and Gaussian. If the process control chart is statistically controlled, a process capability analysis can be calculated using the process conformity index to determine the nature of the process capability. In this research, the kernel control chart and process conformity index were used to analyze the slope shift of Akira-F style fabric and Corvus-SI style on the production of denim fabric at PT Apac Inti Corpora. The results of the analysis show that the production process for Akira-F style is statistically controlled, but Ypk &gt; Yp is 0.889823 &gt; 0,508059 indicating that the process is still not in accordance with the specified limits set by the company, while for Corvus- SI is statistically controlled and Ypk &lt; Yp is 0.637742 &lt; 0.638776 which indicates that the process is in accordance with the specification limits specified by the company. Keywords: kernel density function estimation, Cross Validation, kernel control chart, denim fabric, process capability

Rapid Prediction of Low (

FDA ruled that partially hydrogenated oils were no longer "generally recognized as safe (GRAS)" for any use in human food. Consequently, we have proposed a rapid screening procedure, based on infrared spectroscopy and chemometrics, to rapidly and accurately predict low concentrations of trans fatty acids (<1% of total fatty acids) in edible oils and food lipid extracts.

Development and validation of NTCP models for acute side-effects resulting from proton beam therapy of brain tumours

BackgroundThe limited availability of proton beam therapy (PBT) requires individual treatment selection strategies, such as based on normal tissue complication probability (NTCP). We developed and externally validated NTCP models for common acute side-effects following PBT in brain tumour patients in effort to provide guidance on optimising patient quality of life. MethodsAn exploration cohort including 113 adult brain tumour patients who underwent PBT was investigated for the following endpoints: alopecia, scalp erythema, headache, fatigue and nausea. Dose–volume parameters of associated normal tissues were used for logistic regression modelling. Statistically significant parameters showing high area under the receiver operating characteristic curve (AUC) values in internal cross-validation were externally validated on two cohorts of 71 and 96 patients, respectively. ResultsStatistically significant correlations of dose–volume parameters of the skin for erythema and alopecia were found. In internal cross-validation, the following prognostic parameters were selected: V35Gy (absolute volume receiving 35 Gy) for erythema grade ≥1, D2% (dose to 2% of the volume) for alopecia grade ≥1 and D5% for alopecia grade ≥2. Validation was successful for both cohorts with AUC >0.75. A bivariable model for fatigue grade ≥1 could not be validated externally. No correlations of dose–volume parameters of the brain were seen for headache or nausea. ConclusionWe developed and successfully validated NTCP models for scalp erythema and alopecia in primary brain tumour patients treated with PBT.

Constraining chemical transport PM2.5 modeling outputs using surface monitor measurements and satellite retrievals: application over the San Joaquin Valley.

Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization by providing broad regional context and decreasing metric uncertainties and errors. The frequent, spatially extensive and radiometrically consistent instantaneous constraints can be especially useful in areas away from ground monitors and progressively downwind of emission sources. We present a physical approach to constraining regional-scale estimates of PM2.5, its major chemical component species estimates, and related uncertainty estimates of chemical transport model (CTM; e.g., the Community Multi-scale Air Quality Model) outputs. This approach uses ground-based monitors where available, combined with aerosol optical depth and qualitative constraints on aerosol size, shape, and light-absorption properties from the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System’s Terra satellite. The CTM complements these data by providing complete spatial and temporal coverage. Unlike widely used approaches that train statistical regression models, the technique developed here leverages CTM physical constraints such as the conservation of aerosol mass and meteorological consistency, independent of observations. The CTM also aids in identifying relationships between observed species concentrations and emission sources.Aerosol air mass types over populated regions of central California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) project. We investigate the optimal application of incorporating 275 m horizontal-resolution aerosol air-mass-type maps and total-column aerosol optical depth from the MISR Research Aerosol retrieval algorithm (RA) into regional-scale CTM output. The impact on surface PM2.5 fields progressively downwind of large single sources is evaluated using contemporaneous surface observations. Spatiotemporal R2 and RMSE values for the model, constrained by both satellite and surface monitor measurements based on 10-fold cross-validation, are 0.79 and 0.33 for PM2.5, 0.88 and 0.65 for NO3-, 0.78 and 0.23 for SO42-, and 1.01 for NH+, 0.73 and 0.23 for OC, and 0.31 and 0.65 for EC, respectively. Regional cross-validation temporal and spatiotemporal R2 results for the satellite-based PM2.5 improve by 30 % and 13 %, respectively, in comparison to unconstrained CTM simulations and provide finer spatial resolution. SO42- cross-validation values showed the largest spatial and spatiotemporal R2 improvement, with a 43 % increase. Assessing this physical technique in a well- instrumented region opens the possibility of applying it globally, especially over areas where surface air quality measurements are scarce or entirely absent.

Sexual dimorphism of the first deciduous molar: A geometric morphometric approach

Estimating the sex of immature skeletal remains is important when reconstructing the biological profile of unknown individuals in anthropological practice. Teeth have a distinct importance as they are the most frequently recovered physical elements of an individual. They persist after death due to their hardness and resistance to postmortem insults. The aim of this study was to analyse the sexual dimorphism of the occlusal surface of the first deciduous molar using geometric morphometric techniques. This study evaluated 38 first maxillary deciduous molars (21 males, 17 females) and 30 first mandibular deciduous molars (15 males, 15 females) from the Granada osteological collection of identified subadults (Granada, Spain). The landmark and semilandmark coordinates were analysed by principal components analysis, canonical variate analysis and discriminant analysis. Only the first maxillary deciduous molar showed a significant sexual dimorphism, with cross-validation values for shape variables of 93.23% for males and 83.17% for females, and 100% for males and 87.50% for females when both shape and size variables were considered. Despite there being acceptable cross-validation classification for the first mandibular deciduous molar (82.35% for males and 92.31% for females for shape variables and 82.35% for males and 92.31% for females for shape and size variables), no significant differences indicating sexual dimorphism were identified. The results show that the first upper deciduous molar can assist in sex estimation, and that geometric morphometric analysis is a suitable technique to answer questions related to shape that cannot be observed with the naked eye.

Evaluation of ATR-FTIR spectrometry in the fingerprint region combined with chemometrics for simultaneous determination of benzene, toluene, and xylenes in complex hydrocarbon mixtures

An analytical method for rapid and simultaneous quantitative determination of benzene, toluene, and xylenes in complex hydrocarbon mixtures has been proposed. The method consists of obtaining infrared absorption data by attenuated total reflectance Fourier transform infrared spectrometric method in the fingerprint region (630–1000 cm−1). The spectroscopic data were treated by partial least squares and least squares support vector machine (LS-SVM) methods. The LS-SVM results were obtained using radial basis function and parameter optimization was performed using the simplex method. Root mean squares of cross-validation (RMSECV) values of 0.34 for benzene, 1.85 for toluene, and 1.24 for xylenes using PLS algorithm and RMSECV values of 0.37 for benzene, 1.34 for toluene, and 1.1 for xylenes using LS-SVM algorithm were obtained.

Combined topomer CoMFA and hologram QSAR studies of a series of pyrrole derivatives as potential HIV fusion inhibitors

To have a better understanding of the relationship between structures and inhibitory activities of HIV fusion inhibitors, topomer comparative molecular field analysis (Topomer CoMFA) and hologram quantitative structure–activity relationship (HQSAR) were applied to study a series of pyrrole derivatives as potential HIV fusion inhibitors. Statistical results from the Topomer CoMFA model showed believable predictability based on the non-cross-validated value (r2 = 0.96), and the cross-validated value (q2 = 0.63). The r2 and q2 from the HQSAR model were 0.96 and 0.66, respectively, which also indicated the excellent predictability. External validations further proved the predictive potent of these two models with the $$r_{pred}^2$$ values of 0.96 for both the two models. Statistical results of the two models showed that they are all credible. In addition, we found a phenomenon that for some compound, bulky and negatively charged substitutions were favourable to its activity in the Topomer COMFA, but nitrogen contributed more positively than the bulkier and more negative chorine in the HQSAR. Therefore, in order to obtain more potential hits, the two models should be combined together. These studies would provide insights for the structure–activity relationship understanding of the small-molecule fusion inhibitors and be helpful for the rational design of novel fusion inhibitors in the future.