PyBact

PyBact is a software written in Python for bacterial identification. The code simulates the predefined behavior of bacterial species by generating a simulated data set based on the frequency table of biochemical tests from diagnostic microbiology textbook. The generated data was used for predictive model construction by machine learning approaches and results indicated that the classifiers could accurately predict its respective bacterial class with accuracy in excess of 99 %

Machine learning approaches for discerning intercorrelation of hematological parameters and glucose level for identification of diabetes mellitus

The aim of this study is to explore the relationship between hematological parameters and glycemic status in the establishment of quantitative population-health relationship (QPHR) model for identifying individuals with or without diabetes mellitus (DM). Methods: A cross-sectional investigation of 190 participants residing in Nakhon Pathom, Thailand in January-March, 2013 was used in this study. Individuals were classified into 3 groups based on their blood glucose levels (normal, Pre-DM and DM). Hematological (white blood cell (WBC), red blood cell (RBC), hemoglobin (Hb) and hematocrite (Hct)) and glucose parameters were used as input variables while the glycemic status was used as output variable. Support vector machine (SVM) and artificial neural network (ANN) are machine learning approaches that were employed for identifying the glycemic status while association analysis (AA) was utilized in discovery of health parameters that frequently occur together. Results: Relationship amongst hematological parameters and glucose level indicated that the glycemic status (normal, Pre-DM and DM) was well correlated with WBC, RBC, Hb and Hct. SVM and ANN achieved accuracy of more than 98 % in classifying the glycemic status. Furthermore, AA analysis provided association rules for defining individuals with or without DM. Interestingly, rules for the Pre-DM group are associated with high levels of WBC, RBC,Hb and Hct. Conclusion: This study presents the utilization of machine learning approaches for identification of DM status as well as in the discovery of frequently occurring parameters. Such predictive models provided high classification accuracy as well as pertinent rules in defining DM

In silico design for synthesis of molecularly imprinted microspheres specific towards bisphenol A by precipitation polymerization

Bisphenol A (BPA), a ubiquitous chemical used in industries, has attracted great attention due to its widespread leakage to the environment and foodstuff. This has spurred great interest in the preparation of synthetic polymers capable of selectively sequestering BPA. In this study, theoretical calculation was utilized to confirm the selection of suitable functional monomer capable of strong interaction with BPA. It was found that 4-vinylpyridine was the optimal functional monomer as demonstrated in the literature. A series of molecularly imprinted polymers (MIPs) were prepared by varying the functional monomer, cross-linker, and porogen. After template removal, rebinding with the original template molecule was carried out in acetonitrile, acetonitrile/water (50/50 - 20/80, v/v). 4-vinylpyridine co-polymerized with ethylene glycol dimethacrylate (4VPY-co-EDMA) and 4-vinylpyridine co-polymerized with trimethylolpropane trimethacrylate (4VPY-co-TRIM) were found to exhibit good binding performance towards bisphenol A in acetonitrile. However, only 4VPY-co-EDMA was able to maintain its imprinting effect in acetonitrile/water (50/50 v/v) whereas 4VPY-co-TRIM totally lost its imprinting effect

Prediction of selectivity index of pentachlorophenol-imprinted polymers

A data set comprising of the selectivity index of pentachlorophenol-imprinted polymers against 53 pentachlorophenol and related compounds was obtained from the excellent work of Baggiani et al. Molecular descriptors of the phenol compounds were calculated with E-DRAGON to obtain a total of 1,666 descriptors spanning 20 categories of molecular properties. Multivariate analysis of the data set was performed using multiple linear regression, partial least squares regression, and principal component regression. Partial least squares regression was found to deliver an excellent predictive model and was chosen for further investigation. The descriptor dimension was reduced by the combined use of partial least squares and Unsupervised Forward Selection algorithm. The obtained Quantitative Structure-Property Relationship (QSPR) model based on the smaller subset of the molecular descriptors displayed substantial gain in predictive ability when compared to the model of Baggiani et al. Such QSPR model can help in the computational design of MIPs with predefined selectivity toward template molecules of interest

QSAR study of anti-prion activity of 2-aminothiazoles

2-aminothiazoles is a class of compounds capable of treating life-threatening prion diseases. QSAR studies on a set of forty-seven 2-aminothiazole derivatives possessing anti-prion activity were performed using multivariate analysis, which comprised of multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM). The results indicated that MLR afforded reasonable performance with a correlation coefficient (r) and root mean squared error (RMSE) of 0.9073 and 0.2977, respectively, as obtained from leave-oneout cross-validation (LOO-CV). More sophisticated learning methods such as SVM provided models with the highest accuracy with r and RMSE of 0.9471 and 0.2264, respectively, while ANN gave reasonable performance with r and RMSE of 0.9023 and 0.3043, respectively, as obtained LOO-CV calculations. Descriptor analysis from the regression coefficients of the MLR model suggested that compounds should be asymmetrical molecule with low propensity to form hydrogen bonds and high frequency of N content at topological distance 02 in order to provide good activities. Insights from QSAR studies is anticipated to be useful in the design of novel derivatives based on the 2-aminothiazole scaffold as potent therapeutic agents against prion diseases

Synthesis and computational investigation of molecularly imprinted nanospheres for selective recognition of alpha-tocopherol succinate

Molecularly imprinted polymers (MIPs) are macromolecular matrices that can mimic the functional properties of antibodies, receptors and enzymes while possessing higher durability. As such, these polymers are interesting materials for applications in biomimetic sensor, drug synthesis, drug delivery and separation. In this study, we prepared MIPs and molecularly imprinted nanospheres (MINs) as receptors with specific recognition properties toward tocopherol succinate (TPS) in comparison to tocopherol (TP) and tocopherol nicotinate (TPN). MIPs were synthesized using methacrylic acid (MAA) as functional monomer, ethylene glycol dimethacrylate (EGDMA) as crosslinking agent and dichloromethane or acetronitrile as porogenic solvent under thermal-induced polymerization condition. Results indicated that imprinted polymers of TPS-MIP, TP-MIP and TPN-MIP all bound specifically to their template molecules at 2 folds greater than the non-imprinted polymers. The calculated binding capacity of all MIP was approximately 2 mg per gram of polymer when using the optimal rebinding solvent EtOH:H2O (3:2, v/v). Furthermore, the MINs toward TPS and TP were prepared by precipitation polymerization that yielded particles that are 200-400 nm in size. The binding capacities of MINs to their templates were greater than that of the non-imprinted nanospheres when using the optimal rebinding solvent EtOH:H2O (4:1, v/v). Computer simulation was performed to provide mechanistic insights on the binding modalities of template-monomer complexes. In conclusion, we had successful prepared MIPs and MINs for binding specifically to TP and TPS. Such MIPs and MINs have great potential for industrial and medical applications, particularly for the selective separation of TP and TPS

Elucidating the Structure-Activity Relationships of the Vasorelaxation and Antioxidation Properties of Thionicotinic Acid Derivatives

Nicotinic acid, known as vitamin B3, is an effective lipid lowering drug and intense cutaneous vasodilator. This study reports the effect of 2-(1-adamantylthio)nicotinic acid (6) and its amide 7 and nitrile analog 8 on phenylephrine-induced contraction of rat thoracic aorta as well as antioxidative activity. It was found that the tested thionicotinic acid analogs 6-8 exerted maximal vasorelaxation in a dose-dependent manner, but their effects were less than acetylcholine (ACh)-induced nitric oxide (NO) vasorelaxation. The vasorelaxations were reduced, apparently, in both NG-nitro-L-arginine methyl ester (L-NAME) and indomethacin (INDO). Synergistic effects were observed in the presence of L-NAME plus INDO, leading to loss of vasorelaxation of both the ACh and the tested nicotinic acids. Complete loss of the vasorelaxation was noted under removal of endothelial cells. This infers that the vasorelaxations are mediated partially by endothelium-induced NO and prostacyclin. The thionicotinic acid analogs all exhibited antioxidant properties in both 2,2-diphenyl-1-picrylhydrazyl (DPPH) and superoxide dismutase (SOD) assays. Significantly, the thionicotinic acid 6 is the most potent vasorelaxant with ED50 of 21.3 nM and is the most potent antioxidant (as discerned from DPPH assay). Molecular modeling was also used to provide mechanistic insights into the vasorelaxant and antioxidative activities. The findings reveal that the thionicotinic acid analogs are a novel class of vasorelaxant and antioxidant compounds which have potential to be further developed as promising therapeutics

A practical overview of quantitative structure-activity relationship

Quantitative structure-activity relationship (QSAR) modeling pertains to the construction of predictive models of biological activities as a function of structural and molecular information of a compound library. The concept of QSAR has typically been used for drug discovery and development and has gained wide applicability for correlating molecular information with not only biological activities but also with other physicochemical properties, which has therefore been termed quantitative structure-property relationship (QSPR). Typical molecular parameters that are used to account for electronic properties, hydrophobicity, steric effects, and topology can be determined empirically through experimentation or theoretically via computational chemistry. A given compilation of data sets is then subjected to data preprocessing and data modeling through the use of statistical and/or machine learning techniques. This review aims to cover the essential concepts and techniques that are relevant for performing QSAR/QSPR studies through the use of selected examples from our previous work