Combining ion mobility spectrometry and chemometrics for detecting synthetic colorants in black tea: A reliable and fast method.

Black tea (Camellia sinensis) is a widely consumed beverage and is subjected to adulteration. In this study, the combination of ion mobility spectrometry and machine learning techniques was employed to detect synthetic colorants in black tea. To accomplish our objective, six synthetic colorants (carmine, carmoisine, indigo carmine, brilliant blue, sunset yellow, and tartrazine) were added to pure tea at different concentrations. A qualitative model was built using partial least squares discriminant analysis (PLS-DA) for the collected data and exhibited 100% accuracy in identifying synthetic colorants in black tea. For quantitative analysis, a PLS regression model was employed. The R2 values obtained for the test set ranged from 0.986 to 0.997. The method developed in this study has proven to be reliable and effective in detecting synthetic colorants in black tea. Also, this method is a simple, rapid, and trustworthy tool for identifying adulteration in black tea.

Potential metabolic biomarkers of critical limb ischemia in people with type 2 diabetes mellitus.

INTRODUCTION: Type 2 diabetes mellitus (T2DM) is a significant risk factor for the development of critical limb ischemia (CLI), the most advanced stage of peripheral arterial disease. The concurrent existence of T2DM and CLI often leads to adverse outcomes, namely limb amputation. OBJECTIVE: To identify biomarkers for improving the screening of CLI in high-risk people with T2DM. METHODS: We investigated metabolome profiles in serum samples of 113 T2DM people with CLI (n = 23, G2) and without CLI (n = 45, G0: no lower limb stenosis (LLS) and n = 45, G1: LLS < 50%), using hydrogen nuclear magnetic resonance (1H NMR) approach. Principle component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to analyze 1H NMR data. RESULTS: Twenty potential metabolites that could discriminate people with T2DM and CLI (G2) from non-CLI patients without LLS (G0) were determined in serum samples. The correct percent of classification for the PLS-DA model for the test set samples was 85% (n = 20) and 100% (n = 5) for G0 and G2 groups, respectively. Non-CLI patients with LLS < 50% (G1) were projected on the PCA abstract space built using 20 discriminatory metabolites. Eleven people with T2DM and LLS < 50% were prospectively followed, and their ankle-brachial index (ABI) was measured after 4 years. A promising agreement existed between the PCA model's predictions and those obtained by ABI values. CONCLUSION: The findings suggest that confirmation of blood potential metabolic biomarkers as a complement to ABI for screening of CLI in a large group of high-risk people with T2DM is needed.

Breath biomarkers associated withnontuberculosis mycobacteriadisease status in persons with cystic fibrosis: a pilot study.

Pulmonary infections caused by mycobacteria cause significant mortality and morbidity in the human population. Diagnosing mycobacterial infections is challenging. An infection can lead to active disease or remain indolent with little clinical consequence. In patients with pulmonarynontuberculosis mycobacteria(PNTM) identification of infection and diagnosis of disease can take months to years. Our previous studies showed the potential diagnostic power of volatile molecules in the exhaled breath samples to detect active pulmonaryM. tuberculosisinfection. Herein, we demonstrate the ability to detect the disease status of PNTM in the breath of persons with cystic fibrosis (PwCF). We putatively identified 17 volatile molecules that could discriminate between active-NTM disease (n= 6), indolent patients (n= 3), and those patients who have never cultured an NTM (n= 2). The results suggest that further confirmation of the breath biomarkers as a non-invasive and culture-independent tool for diagnosis of NTM disease in a larger cohort of PwCF is warranted.

Looking at time dependent differentiation of mesenchymal stem cells by culture media using MALDI-TOF-MS.

Mesenchymal stem cells (MSCs) are multipotent cells which are popular in human regenerative medicine. These cells can renew themselves and differentiate into several specialized cell types including osteoblasts, adipocytes, and chondrocytes under physiological and experimental conditions. MSCs can secret a lot of components including proteins and metabolites. These components have significant effects on their surrounding cells and also can be used to characterize them. This characterization of multipotent MSCs plays a critical role in their therapeutic potential. The metabolic profile of culture media verified by applying matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-TOF-MS) techniques. Also, the differentiation and development of MSCs have monitored through culture media metabolome or secretome (secreted metabolites). Totally, 24 potential metabolites were identified. Between them 12 metabolites are unique to BM-MSCs and 5 metabolites are unique to AD-MSCs. Trilineage differentiation including chondrocytes, osteoblasts, and adipocytes, as well as metabolites that are being differentiated, have been shown in different weeks. In the present study, the therapeutic effects of MSCs analyzed by decoding the metabolome for MSCs secretome via metabolic profiling using MALDI-TOF-MS techniques.

Detection of fraud in lime juice using pattern recognition techniques and FT-IR spectroscopy.

The lime juice is one of the products that has always fallen victim to fraud by manufacturers for reducing the cost of products. The aim of this research was to determine fraud in distributed lime juice products from different factories in Iran. In this study, 101 samples were collected from markets and also prepared manually and finally derived into 5 classes as follows: two natural classes (Citrus limetta, Citrus aurantifolia), including 17 samples, and three reconstructed classes, including 84 samples (made from Spanish concentrate, Chinese concentrate, and concentrate containing adulteration compounds). The lime juice samples were freeze-dried and analyzed using FT-IR spectroscopy. At first, principal component analysis (PCA) was applied for clustering, but the samples were not thoroughly clustered with respect to their original groups in score plots. To enhance the classification rates, different chemometric algorithms including variable importance in projection (VIP), partial least square-discriminant analysis (PLS-DA), and counter propagation artificial neural networks (CPANN) were used. The best discriminatory wavenumbers related to each class were selected using the VIP-PLS-DA algorithm. Then, the CPANN algorithm was used as a nonlinear mapping tool for classification of the samples based on their original groups. The lime juice samples were correctly designated to their original groups in CPANN maps and the overall accuracy of the model reached up to 0.96 and 0.87 for the training and validation procedures. This level of accuracy indicated the FT-IR spectroscopy coupled with VIP-PLS-DA and CPANN methods can be used successfully for detection of authenticity of lime juice samples.

Probing heat and oxidation induced conformational changes of molecular chaperone artemin by excitation-emission fluorescence spectroscopy.

Artemin is a potent molecular chaperone, which protects Artemia embryos undergoing encystment against extreme environmental stresses. In the present work, we have examined the structural changes of artemin from A. urmiana upon exposure to oxidant and heat, by using CD measurements as well as excitation-emission fluorescence spectroscopy as a powerful tool for monitoring the conformational transitions and molecular interactions in proteins. We have also provided here the first document on reporting the three dimensional fluorescence spectra of a protein using ANS. Totally, the fluorescence results indicated that the microenvironments of tyrosine and tryptophan residues and the hydrophobic pockets as well as the polypeptide backbone or secondary structure of the chaperone were influenced in responses to heat and H2O2 in different degrees. Moreover, the native state of artemin did not induce a considerable exposure of the internal non-polar groups to the solvent. Besides, the excitation-emission spectra of heated artemin by ANS revealed new emission peaks at 430-450 nm when it was excited at 330 nm, which suggests probable exposure of new binding sites for hydrophobic or electrostatic interactions of the protein with ANS. The protein also showed a greater conformational sensitivity to the temperature fluctuations compared to oxidation. Here, we presented some evidence in support of the relation between artemin and its stress dependent activation in vitro and in vivo. This study can expect that the EEM fluorescence spectroscopy could provide a promising tool to study conformational transitions of proteins.

Synthesis, crystal structure, insecticidal activities, molecular docking and QSAR studies of some new phospho guanidines and phospho pyrazines as cholinesterase inhibitors.

Novel phospho guanidine and phospho pyrazine derivatives were synthesized and characterized by 31P, 13C, 1HNMR and IR spectroscopy to obtain novel and human-safe insecticides. Compound 35 [(C4H4N2NH)2P(O)(C6H6)] was investigated by X-ray crystallography. The inhibitory effects of synthesized compounds were evaluated on human and insect acetylcholinesterase (AChE) using in vitro Ellman method. A few of these compounds, which had low human toxicity, were selected for assessing the killing effects (in vivo) on the elm leaf beetle (X.luteola). The in vitro and in vivo results indicated that compounds bearing both phosphoryl groups and aromatic systems were found to possess a good selectivity for the inhibition of insect AChE over human AChE; up to 550-fold selectivity was achieved for compound 19. Docking studies were performed to explain reasons for the selective behavior of AChE inhibitors. Additionally, the quantitative structure-activity relationship (QSAR) and density functional theory (DFT) results of AChEs demonstrated that the size, shape, dipole moment, and ability to form hydrogen bond played the main role in both models. In addition, the aromatic π - π interactions and charge of the amide nitrogen had a major effect on insecticidal activity of the compounds. The present research can be helpful to gain a better understanding of the interactions between the insect AChE and its inhibitors and introduces compounds which are capable of becoming human-safe insecticides.

Synthesis, crystal structure and biological evaluation of new phosphoramide derivatives as urease inhibitors using docking, QSAR and kinetic studies.

In an attempt to achieve a new class of phosphoramide inhibitors with high potency and resistance to the hydrolysis process against urease enzyme, we synthesized a series of bisphosphoramide derivatives (01-43) and characterized them by various spectroscopic techniques. The crystal structures of compounds 22 and 26 were investigated using X-ray crystallography. The inhibitory activities of the compounds were evaluated against the jack bean urease and were compared to monophosphoramide derivatives and other known standard inhibitors. The compounds containing aromatic amines and their substituted derivatives exhibited very high inhibitory activity in the range of IC50 = 3.4-1.91 × 10-10 nM compared with monophosphoramides, thiourea, and acetohydroxamic acid. It was also found that derivatives with PO functional groups have higher anti-urease activity than those with PS functional groups. Kinetics and docking studies were carried out to explore the binding mechanism that showed these compounds follow a mixed-type mechanism and, due to their extended structures, can cover the entire binding pocket of the enzyme, reducing the formation of the enzyme-substrate complex. The quantitative structure-activity relationship (QSAR) analysis also revealed that the interaction between the enzyme and inhibitor is significantly influenced by aromatic rings and PO functional groups. Collectively, the data obtained from experimental and theoretical studies indicated that these compounds can be developed as appropriate candidates for urease inhibitors in this field.

Identification of molecular features necessary for selective inhibition of B cell lymphoma proteins using machine learning techniques.

Selective inhibition of Bcl-2 and Bcl-xL proteins due to their dual inhibition toxicity plays an important role in treatment of cancer and chemotherapy effectiveness; therefore, in the last decade, discovery of selective inhibitors for Bcl-2 and Bcl-xL proteins has become a significant and important research topic. The present contribution paves the way for characterization of molecular features which induce selectivity for inhibition of Bcl-2 and Bcl-xL. In this line, a total of 1534 molecules related to inhibition of Bcl-2 and Bcl-xL proteins were collected from Binding Database. A diverse set of molecular descriptors was calculated for each molecule, and the best subset of descriptors were selected using variable importance in projection (VIP) approach. The molecules were classified according to their therapeutic targets (Bcl-2/Bcl-xL) and activities. Partial least square-discriminate analysis (PLS-DA) and supervised Kohonen network (SKN) models were utilized to relate the molecular structures of chemicals to their activities and selectivities. According to the VIP-selected descriptors physicochemical properties, such as polarity number, number of branches, size and cyclicity of the molecule, flexibility, functional counts and constitutional descriptors, all affect the activities of Bcl-2 and Bcl-xL inhibitors. The performances of PLS-DA and SKN methods were evaluated based on statistical parameters derived from the confusion matrices. The models were validated using tenfold cross-validation and an external test set. The best statistical results were obtained by implementing the SKN model. The classification rates range from 93.5 to 79.1% for the training and validation procedure for the optimized SKN models. The high values of the obtained classification rates demonstrate that the information provided in this work would be useful to design new drugs with selective inhibitory activities toward Bcl-2 or Bcl-xL proteins for more effective treatment of cancer.

Metabolic Fingerprinting of Seminal Plasma from Non-obstructive Azoospermia Patients: Positive Versus Negative Sperm Retrieval.

BACKGROUND: Non-obstructive azoospermia (NOA) occurs in approximately 10% of infertile men. Retrieval of the spermatozoa from the testicle of NOA patients is an invasive approach. Seminal plasma is an excellent source for exploring to find the biomarkers for presence of spermatozoa in testicular tissue. The present discovery phase study aimed to use metabolic fingerprinting to detect spermatogenesis from seminal plasma in NOA patients as a non-invasive method. METHODS: In this study, 20 men with NOA were identified based on histological analysis who had their first testicular biopsy in 2015 at Avicenna Fertility Center, Tehran, Iran. They were divided into two groups, a positive testicular sperm extraction (TESE(+)) and a negative testicular sperm extraction (TESE(-)). Seminal plasma of NOA patients was collected before they underwent testicular sperm extraction (TESE) operation. The metabolomic fingerprinting was evaluated by Raman spectrometer. Principal component analysis (PCA) and an unsupervised statistical method, was used to detect outliers and find the structure of the data. The PCA was analyzed by MATLAB software. RESULTS: Metabolic fingerprinting of seminal plasma from NOA showed that TESE (+) versus TESE(-) patients were classified by PCA. Furthermore, a possible subdivision of TESE(-) group was observed. Additionally, TESE(-) patients were in extreme oxidative imbalance compared to TESE(+) patients. CONCLUSION: Metabolic fingerprinting of seminal plasma can be considered as a breakthrough, an easy and cheap method for prediction presence of spermatogenesis in NOA.

Motor Oil Classification Using Color Histograms and Pattern Recognition Techniques.

Motor oil classification is important for quality control and the identification of oil adulteration. In this work, we propose a simple, rapid, inexpensive and nondestructive approach based on image analysis and pattern recognition techniques for the classification of nine different types of motor oils according to their corresponding color histograms. For this, we applied color histogram in different color spaces such as red green blue (RGB), grayscale, and hue saturation intensity (HSI) in order to extract features that can help with the classification procedure. These color histograms and their combinations were used as input for model development and then were statistically evaluated by using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM) techniques. Here, two common solutions for solving a multiclass classification problem were applied: (1) transformation to binary classification problem using a one-against-all (OAA) approach and (2) extension from binary classifiers to a single globally optimized multilabel classification model. In the OAA strategy, LDA, QDA, and SVM reached up to 97% in terms of accuracy, sensitivity, and specificity for both the training and test sets. In extension from binary case, despite good performances by the SVM classification model, QDA and LDA provided better results up to 92% for RGB-grayscale-HSI color histograms and up to 93% for the HSI color map, respectively. In order to reduce the numbers of independent variables for modeling, a principle component analysis algorithm was used. Our results suggest that the proposed method is promising for the identification and classification of different types of motor oils.

The molecular cues for the biological effects of ionizing radiation dose and post-irradiation time on human breast cancer SKBR3 cell line: A Raman spectroscopy study.

Radiotherapy is one of the main modalities of cancer treatment. The utility of Raman spectroscopy (RS) for detecting the distinct radiobiological responses in human cancer cells is currently under investigation. RS holds great promises to provide good opportunities for personalizing radiotherapy treatments. Here, we report the effects of the radiation dose and post-irradiation time on the molecular changes in the human breast cancer SKBR3 cells, using RS. The SKBR3 cells were irradiated by gamma radiation with different doses of 0, 1, 2, 4, and 6 Gy. The Raman signals were acquired 24 and 48 h after the gamma radiation. The collected Raman spectra were analyzed by different statistical methods such as principal component analysis, linear discriminant analysis, and genetic algorithm. A thorough analysis of the obtained Raman signals revealed that 2 Gy of gamma radiation induces remarkable molecular and structural changes in the SKBR3 cells. We found that the wavenumbers in the range of 1000-1400 cm-1 in Raman spectra are selective for discriminating between the effects of the different doses of irradiation. The results also revealed that longer post-irradiation time leads to the relaxation of the cells to their initial state. The molecular changes that occurred in the 2Gy samples were mostly reversible. On the other hand, the exposure to doses higher than 4Gy induced serious irreversible changes, mainly seen in 2700-2800 cm-1 in Raman spectra. The classification models developed in this study would help to predict the radiation-based molecular changes induced in the cancer cells by only using RS. Also, this designed framework may facilitate the process of biodosimetry.

Untargeted metabolomic profiling of seminal plasma in nonobstructive azoospermia men: A noninvasive detection of spermatogenesis.

Male factor infertility is involved in almost half of all infertile couples. Lack of the ejaculated sperm owing to testicular malfunction has been reported in 6-10% of infertile men, a condition named nonobstructive azoospermia (NOA). In this study, we investigated untargeted metabolomic profiling of the seminal plasma in NOA men using gas chromatography-mass spectrometry and advance chemometrics. In this regard, the seminal plasma fluids of 11 NOA men with TESE-negative, nine NOA men with TESE-positive and 10 fertile healthy men (as a control group) were collected. Quadratic discriminate analysis (QDA) technique was implemented on total ion chromatograms (TICs) for identification of discriminatory retention times. We developed multivariate classification models using the QDA technique. Our results revealed that the developed QDA models could predict the classes of samples using their TIC data. The receiver operating characteristic curves for these models were >0.88. After recognition of discriminatory retention time's asymmetric penalized least square, evolving factor analysis, correlation optimized warping and alternating least squares strategies were applied for preprocessing and deconvolution of the overlapped chromatographic peaks. We could identify 36 discriminatory metabolites. These metabolites may be considered discriminatory biomarkers for different groups in NOA.

Estimating complicated baselines in analytical signals using the iterative training of Bayesian regularized artificial neural networks.

The present work deals with the development of a new baseline correction method based on the comparative learning capabilities of artificial neural networks. The developed method uses the Bayes probability theorem for prevention of the occurrence of the over-fitting and finding a generalized baseline. The developed method has been applied on simulated and real metabolomic gas-chromatography (GC) and Raman data sets. The results revealed that the proposed method can be used to handle different types of baselines with cave, convex, curvelinear, triangular and sinusoidal patterns. For further evaluation of the performances of this method, it has been compared with benchmarking baseline correction methods such as corner-cutting (CC), morphological weighted penalized least squares (MPLS), adaptive iteratively-reweighted penalized least squares (airPLS) and iterative polynomial fitting (iPF). In order to compare the methods, the projected difference resolution (PDR) criterion has been calculated for the data before and after the baseline correction procedure. The calculated values of PDR after the baseline correction using iBRANN, airPLS, MPLS, iPF and CC algorithms for the GC metabolomic data were 4.18, 3.64, 3.88, 1.88 and 3.08, respectively. The obtained results in this work demonstrated that the developed iterative Bayesian regularized neural network (iBRANN) method in this work thoroughly detects the baselines and is superior over the CC, MPLS, airPLS and iPF techniques. A graphical user interface has been developed for the suggested algorithm and can be used for easy implementation of the iBRANN algorithm for the correction of different chromatography, NMR and Raman data sets.

Concentration profiling of minerals in iliac crest bone tissue of opium addicted humans using inductively coupled plasma and discriminant analysis techniques.

Opium addiction is one of the main health problems in developing countries and induces serious defects on the human body. In this work, the concentrations of 32 minerals including alkaline, heavy and toxic metals have been determined in the iliac crest bone tissue of 22 opium addicted individuals using inductively coupled plasma-optical emission spectroscopy (ICP-OES). The bone tissues of 30 humans with no physiological and metabolomic diseases were used as the control group. For subsequent analyses, the linear and quadratic discriminant analysis techniques have been used for classification of the data into "addicted" and "non-addicted" groups. Moreover, the counter-propagation artificial neural network (CPANN) has been used for clustering of the data. The results revealed that the CPANN is a robust model and thoroughly classifies the data. The area under the curve for the receiver operating characteristic curve for this model was more than 0.91. Investigation of the results revealed that the opium consumption causes a deficiency in the level of Calcium, Phosphate, Potassium and Sodium in iliac crest bone tissue. Moreover, this type of addiction induces an increment in the level of toxic and heavy metals such as Co, Cr, Mo and Ni in iliac crest tissue. The correlation analysis revealed that there were no significant dependencies between the age of the samples and the mineral content of their iliac crest, in this study. The results of this work suggest that the opium addicted individuals need thorough and restricted dietary and medical care programs after recovery phases, in order to have healthy bones.

Assessment of the orthogonality in two-dimensional separation systems using criteria defined by the maximal information coefficient.

A novel method based on the maximal information coefficient (MIC) is developed to assess the orthogonality of comprehensive two-dimensional separation systems. The proposed method is based on a modification of Marriott's method, which was previously reported in 2013. Marriott's method relies on the calculation of two separate parameters. The first term is Cpert which defines the peak coverage percent in separation space, and the second is Cpeaks which corresponds to 2-D distribution correlation of the peaks. Marriott's method for estimating the values of Cpeaks is based on the calculation of the coefficient of determination (R(2)) between the retention indices of the peaks. Herein, we present some examples where R(2) is an inefficient way to estimate the values of Cpeaks. The results in this work illustrate that when there are either functional or non-functional local dependencies between the distributions of the peaks, R(2) values fail to thoroughly estimate the values of Cpeaks. We proposed using the MIC instead of R(2) to estimate the values of Cpeaks for orthogonality calculations. Simulations of comprehensive two-dimensional gas chromatograms were performed using the Abraham solvation parameter model in order to generate examples for orthogonality assessment. The results indicate that the suggested modifications in this work correct the shortcomings of Marriott's model, and the proposed equation accurately measures the column dependencies in 2-D separation systems.

CS-MINER: A Tool for Association Mining in Binding-Database.

This paper introduces the algorithms, implementation strategies, features, and applications of CS-MINER, a tool for visualization and analysis of drug-like chemical space. The CS-MINER is the abstract abbreviation for Chemical Space Miner and correlates the medicinal target space and chemical space, in a systematic way. The database in this software consists of a large collection of drug-like molecules. To prepare this database, a large number of molecules for 110 important biological targets were collected from Binding-DB. A total of 1497 physicochemical properties were calculated for each molecule. The CS-MINER uses the discriminant analysis techniques for tracing the collected data and finally separates the molecules based on their therapeutic targets and activities. The developed multivariate classifiers can be used for ligand-based virtual screening of more than 0.5 million random molecules of PubChem and ZINC databases. In order to validate the models, selected subspaces in CS-MINER were compared with DrugBank molecules. At the end of the analysis, the software provides an interactive environment for visualization of the selected chemical subspaces in the form of 2- and 3-dimensional plots. In general, CS-MINER is a tool for comparing the relative position of active biosimilar molecules in chemical space and is freely available at www.csminer.com.

1H NMR based metabolic profiling in Crohn's disease by random forest methodology.

The present study was designed to search for metabolic biomarkers and their correlation with serum zinc in Crohn's disease patients. Crohn's disease (CD) is a form of inflammatory bowel disease that may affect any part of the gastrointestinal tract and can be difficult to diagnose using the clinical tests. Thus, introduction of a novel diagnostic method would be a major step towards CD treatment. Proton nuclear magnetic resonance spectroscopy ((1)H NMR) was employed for metabolic profiling to find out which metabolites in the serum have meaningful significance in the diagnosis of CD. CD and healthy subjects were correctly classified using random forest methodology. The classification model for the external test set showed a 94% correct classification of CD and healthy subjects. The present study suggests Valine and Isoleucine as differentiating metabolites for CD diagnosis. These metabolites can be used for screening of risky samples at the early stages of CD diagnoses. Moreover, a robust random forest regression model with good prediction outcomes was developed for correlating serum zinc level and metabolite concentrations. The regression model showed the correlation (R(2)) and root mean square error values of 0.83 and 6.44, respectively. This model suggests valuable clues for understanding the mechanism of zinc deficiency in CD patients.

Integrated One-Against-One Classifiers as Tools for Virtual Screening of Compound Databases: A Case Study with CNS Inhibitors.

A total of 21 833 inhibitors of the central nervous system (CNS) were collected from Binding-database and analyzed using discriminant analysis (DA) techniques. A combination of genetic algorithm and quadratic discriminant analysis (GA-QDA) was proposed as a tool for the classification of molecules based on their therapeutic targets and activities. The results indicated that the one-against-one (OAO) QDA classifiers correctly separate the molecules based on their therapeutic targets and are comparable with support vector machines. These classifiers help in charting the chemical space of the CNS inhibitors and finding specific subspaces occupied by particular classes of molecules. As a next step, the classification models were used as virtual filters for screening of random subsets of PUBCHEM and ZINC databases. The calculated enrichment factors together with the area under curve values of receiver operating characteristic curves showed that these classifiers are good candidates to speed up the early stages of drug discovery projects. The "relative distances" of the center of active classes of biosimilar molecules calculated by OAO classifiers were used as indices for sorting the compound databases. The results revealed that, the multiclass classification models in this work circumvent the definition inactive sets for virtual screening and are useful for compound retrieval analysis in Chemoinformatics.