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In this work the effects of NaYF4:Yb, Er (NYE) structure on the enhanced red upconversion luminescence (UC) was investigated. -NYE nanocrystals (NCs) and -NYE NCs were fabricated by a high temperature decomposition reaction method. The prepared NCs were characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), and photoluminescence (PL) spectroscopy. The results show that the red UC luminescence of -NYE NCs is significantly enhanced compared with that of -NYE. Furthermore, a possible energy transfer mechanism was proposed on the basis of our experimental results.

The occurrence of alachlor in natural drinking water sources raises concerns due to adverse health effects. Advanced oxidation with zero valent iron activating persulfate (ZVI/PS) can degrade alachlor with great potential. But no information was reported about effect of ZVI/PS preoxidation on the generation of disinfection byproducts (DBPs) during the downstream chlorination of alachlor. The objective was to investigate DBP formation through chlorination of alachlor preoxidized by ZVI/PS. The results found that ZVI/PS preoxidation enhanced the DBP formation, compared with original system without any treatment. The longer the preoxidation time of ZVI/PS during the experiment, the more DBPs that were generated. During 48.0 h chlorination time, the produced DBPs increased to a stable value. Nitrate posed almost no impact on the DBP formation in the ZVI/PS system, while chloride significantly decreased DBP formation, which was attributed to the reduction of trichloromethane. During the chlorination process, alachlor could be further oxidized by remaining PS and free chlorine collectively. ZVI/PS had little effect on the DBP formation in the real water matrix. However, it could reduce carbonaceous DBP formation and increase nitrogenous DBP formation when the real water matrix contained 100 μg/L alachlor. The acute toxicity of alachlor solution could be reduced by the ZVI/PS system, while after chlorination the acute toxicity was largely increased due to the formation of DBPs. With increase of PS concentration, the DBP formation decreased in the ZVI/PS preoxidation system and increased in the system with PS alone. In conclusion, application of ZVI/PS in actual water containing alachlor declined DBP generation by augmenting PS concentration which should consider standard limited value of sulfate. Further investigation is needed for high bromide water.

The evaluation and prediction of mixture toxicity is a hot topic of toxicology and environmental sciences. The Concentration Addition (CA) and Independent action (IA) models are the primary reference models. CA is suitable for predicting the toxicity of the mixture where the components have same mode of action (MOA) and the IA for toxicity of mixture where components have different MOA. Nonetheless, it is very difficult to obtain the information about the MOA of chemicals. Therefore, it is important to explore a feasible and efficient method to identify MOA. In this study, to reveal the mechanisms of toxic action of ionic liquids (ILs) to firefly luciferase and further build the prediction model for mixture toxicity, luciferase is treated as the biomolecular receptor and four 1-alkyl- 3-methyl-imidzole chloride ILs ([Cnmim]Cl, ILn, n =2, 6, 8, 12) are treated as the ligands. The binding modes of ligands (ionic liquids) and receptor (luciferase) are identified by molecular docking and molecular dynamics simulations. Molecular dynamics simulations revealed that the imidazolium ring of [C2mim] is bound at the bottom of the luciferin (D-LH2) pocket which is surrounded by Arg218, Phe247 Thr251, Leu286, Arg337, Gly339 and Ile351 and the alkyl-chain extends from the bottom of the pocket to the entrance. The [C6mim] is bound at the pocket which is surrounded by Phe247, Thr251, Gly315, Gly339, Leu342, Ala348 and Ile351. In contrast, the imidazolium ring of [C8mim] and [C12mim] is bound at the entrance of the D-LH2 pocket and the alkyl-chain inserts into the bottom of the D-LH2 pocket, surrounded by Gly200, Arg218, His245, Phe247, Thr251, Gly316, Arg337, Thr343, Leu526 and Thr527. According to the information of the binding site, binding pose, hydrogen bonds and hydrophobic contacts, we can determine whether the ILs have similar binding pattern (BP). The results show that [C2mim]Cl belongs to BP1 and [C6mim]Cl belongs to BP2. [C8mim]Cl and [C12mim]Cl belong to BP3. We used the firefly luciferase-microplate toxicity analysis to determine the luminescence inhibition effect of the four ILs on luciferase and direct equipartition ray design (EquRay) to design 18 binary mixture rays. We evaluated the toxicities of the mixtures by CA and IA. The results show that the mixture toxicities of [C2mim]Cl-[C8mim]Cl and [C2mim]Cl-[C12mim]Cl where components have dissimilar BP can be predicted by IA. The mixture toxicities of [C2mim]Cl-[C6mim]Cl and [C8mim]Cl-[C12mim]Cl where components have similar BP could be predicted by CA. This study demonstrates that CA and IA are suitable for predicting the toxicity of mixtures according to the different BPs of the individual ILs to luciferase.

In this work, -NaLuF4:Yb, Er (NLF) nanocomposites (NCs) and -NLF NCs with diameter about ∼13 nm were fabricated by a high temperature decomposition reaction method. The effects of NLF structure on the enhanced red upconversion luminescence performance were investigated. Under 980 nm excitation from a laser diode, the -NLF emitted dominant red UC emission. Furthermore, the possible energy transfer mechanism was proposed on the basis of our experimental results.

In this work a multi-objective quantitative structure-property relationship (QSPR) analysis approach was reported based on the study on three partition properties of 50 aromatic sulfur-containing carboxylates. Here multi-objectives (properties) were taken as a vector for QSPR modeling. The quantitative correlations for partition properties were developed using a genetic algorithm-based variable-selection approach with quantum chemical descriptors derived from AMl-based calculations. With the QSPR models, the aqueous solubility, octanol/water partition coefficients and reversed-phase HPLC capacity factors of sulfur-containing compounds were estimated and predicted. Using GA-based multivariate linear regression with cross-validation procedure, a set of the most promising descriptors was selected from a pool of 28 quantum chemical semi-empirical descriptors, including steric and electronic types, to integrally build QSPR models. The selected molecular descriptors included the net charges on carboxyl group (Qoc), the 2nd power of net charges on nitrogen atoms (Q2N), the net atomic charge on the sulfur atoms (Qs), the van der Waals volume of molecule (V), the most positive net atomic charge on hydrogen atoms (QH) and the measure of polarity and polarizability (π), which were main factors affecting the distribution processes of the compounds under study. The statistically best QSPR models of six descriptors were simultaneously obtained by GA-based linear regression analysis. With the selected descriptors and the QSPR equations, mechanisms of partition action of the Sulfur-containing carboxylates were able to be investigated and interpreted.

Selective inhibition of cyclooxygenase-2 (COX-2) might avoid the side effects of current available nonsteroidal antiinflammatory drugs while retaining their therapeutic efficacy. A novel variable selection and modeling method based on prediction is developed to construct the quantitative structure-activity relationships (QSAR) between the molecular electronegativity distance vector (MEDV) based on 13 atomic types and the biological activities of a set of selective cyclooxygenase-2 inhibitory molecules, 3,4-diarylcycloxazolones (DAA) plus indomethacin, naproxen, and celecoxib. Using multiple linear regression, a 5-variable linear model is developed with the calibrated correlation coefficient of 0.9271 and root mean square error of 0.17 in modeling stage and the validated correlation coefficient of 0.9030 and root mean square error of 0.20 in leave-one-out validation step, respectively. To further test the predictive ability of the model, 20 DAA compounds are picked up to construct a training set which is used to build a QSAR model and then the model is employed to predict the biological activities of the balance compounds. The predicted correlation coefficient and root mean square error are 0.9332 and 0.19, respectively.

In China, water pollution by pesticide mixtures has constituted a serious environmental problem due to potential toxicity and bioaccumulation. But few pesticide combinations have exactly similar and dissimilar mechanisms of action. For this purpose, in tests with the freshwater luminescent bacterium (Vibrio qinghaiensis sp.-Q67), ten pesticides, including three herbicides and seven insecticides, were selected as test substances. Concentration response analysis was performed for ten individual substances, and for mixtures containing all ten substances in twelve different concentration ratios (based on UDCR and EECR methods). The observed mixture toxicity was compared with predictions by the two models, concentration addition (CA) and independent action (IA). The toxicity of the tested mixtures showed a good agreement with those predicted by the concept of CA except four UDCR mixtures: UD10-2, UD10-4, UD10-8 and UD10-10. To examine the influence of imidacloprid in the four UDCR mixtures (UD10-2, UD10-4, UD10-8, UD10-10), it was removed from the ten-pesticide mixtures and the remaining nine chemicals were combined at the same relative proportions based on the UDCR method (UD9-2, UD9-4, UD9-8, UD9-10). There was not significant departure from CA for the scattered points with the nine remaining pesticides omitting imidacloprid. Thus, imidacloprid may significantly influence the other pesticides due to its properties.

This work aims at establishing a simple fluorescent probe for the determination of dissolved oxygen. It is found that iron(II) ions activate oxygen to produce reactive species being capable of oxidizing non-fluorescent coumarin to fluorescent 7-hydroxycoumarin. However, this process is not effective because the yield of the reactive species is very low in the presence of simple iron(II) salts alone. The addition of organic ligands such as oxalate results in the formation of complexes between iron(II) ions, which leads to considerable increase in the yield of reactive species (such as hydroxyl radicals) and then increase in the fluorescence intensity of 7-hydroxycoumarin to a significant level. It has been observed that in the mixture solution of iron(II) ions, ligand, coumarin, and dissolved oxygen, there is an excellent linear response between the fluorescence and dissolved oxygen. Therefore, a new spectrofluorimetric method has been proposed for the determination of dissolved oxygen by using catalytic activation of O 2 by iron(II) chelates. Under optimized conditions, a linear correlation ( r = 0.995) has been observed between the fluorescence intensity of 7-hydroxycoumarin at 456 nm and the concentration of dissolved oxygen over the range of 0.96–9.22 mg L −1 . The limit of detection for dissolved oxygen at a signal-to-noise ratio of 3 has been estimated to be 0.35 mg L −1 . The proposed method has been applied to determine the concentration of dissolved oxygen in practical water samples with results as satisfactory as that obtained by the standard iodometric method.

Computer-based quantitative structure-activity relationship (QSAR) model has been becoming a powerful tool in understanding the structural requirements for chemicals to bind the estrogen receptor (ER), designing drugs for human estrogen replacement therapy, and identifying potential estrogenic endocrine disruptors. In this study, a simple yet powerful neural network technique, generalized regression neural network (GRNN) was used to develop a QSAR model based on 131 structurally diverse estrogens (training set). Only nine descriptors calculated solely from the molecular structures of compounds selected by objective and subjective feature selections were used as inputs of the GRNN model. The predictive power of the built model was found to be comparable to that of the more traditional techniques but requiring significantly easy implementation and a shorter computation-time. The obtained result indicates that the proposed GRNN model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogenic activity of organic compounds.

Since the complexity and structural diversity of man-made compounds are considered, quantitative structure-activity relationships (QSARs)-based fast screening approaches are urgently needed for the assessment of the potential risk of endocrine disrupting chemicals (EDCs). The artificial neural networks (ANN) are capable of recognizing highly nonlinear relationships, so it will have a bright application prospect in building high-quality QSAR models. As a popular supervised training algorithm in ANN, back-propagation (BP) converges slowly and immerses in vibration frequently. In this paper, a research strategy that BP neural network was improved by conjugate gradient (CG) algorithm with a variable selection method based on genetic algorithm was applied to investigate the QSAR of EDCs. This resulted in a robust and highly predictive ANN model with R 2 of 0.845 for the training set, q 2 pred of 0.81 and root-mean-square error (RMSE) of 0.688 for the test set. The result shows that our method can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.

The molecular electronegativity-distance vector (MEDV) is employed to describe the chemical structure of bisphenol A analogs and their correlated estrogen activities. The result shows that the constructed models have good predictability and indicates substructures that may influence estrogen activities of chemicals.

Molecular structures of polychlorinated naphthalenes were numerically described with a simple but efficient encoding method. Correspondingly a set of structural parameters were obtained for these compounds and linearly correlated with their gas chromatography retention indexes. A quantitative structure-retention relationship Model (M1) was developed by using multiple linear regression (MLR) with correlation coefficient R = 0.9880 between the numeric structural codes and the gas chromatography retention indexes of 62 polychlorinated naphthalenes. If the "leave-one-out" cross-validation procedure was employed to construct QSPR model for all samples, the second model M2 with the correlation coefficient being R = 0.9839 was generated. The structural codes of polychlorinated naphthalenes were tested with MLR for estimation and prediction of the GC RI by models M1 and M2, and the results obtained were satisfactory.

The use of numerous descriptors that are indicative of molecular structure and topology is becoming more common in quantitative structure-activity relationship (QSAR). How to choose the adequate descriptors for QSAR studies is important but difficult because there are no absolute rules to govern this choice. A variety of variable selection techniques including stepwise, partial least squares/principal component analysis (PLS/PCA), neural network, and evolutionary algorithm such as genetic algorithm have been applied to this common problem. All-subsets regression (ASR) is capable of finding out the best variable subset from among a large pool. In this paper, a novel variable selection and modeling method based on the prediction, for short VSMP, has been developed. Here two controllable parameters, the interrelation coefficient between the pairs of the independent variables (r(int)) and the correlation coefficient (q(2)) obtained using the leave-one-out (LOO) cross-validation technique, are introduced into the ASR to improve its performances. This technique differs from the other variable selection procedures related to the ASR by two main features: (1) The search of various optimal subset search is controlled by the statistic q(2) or root-mean-square error (RMSEP) in the LOO cross-validation step rather than the correlation coefficient obtained in the modeling step (r(2)). (2) The searching speed of all optimal subsets is expedited by the statistic r(int) together with q(2). A comparison of the results of the VSMP applied to the Selwood data set (n = 31 compounds, m = 53 descriptors) with those obtained from alternative algorithms shows the good performance of the technique.

Holographic QSAR model is constructed to predict relative binding affinities of estradiol derivatives with lamb uterine estrogen receptor. The method does not require the generation of three-dimensional structure of the compounds. The factors that influence the quality of QSAR model are discussed. The result indicates that the model has good predictability and yields a high q LOO 2 value of 0.897. Compared to other QSAR methods, the HQSAR technique is suitable for use in screening large database of chemicals.

The molecular holographic distance vector (MHDV) is employed to characterize the structures of 51 substituted benzenes. 29 descriptors from 91 MHDV ones have nonzero values where 3 descriptors have only one nonzero sample and 1 descriptor only two nonzero samples. A genetic algorithm is used to select an optimal combination of the variables from the remaining 25 nonzero descriptors. Then the optimal descriptors are employed to relate to the relative biodegradability using multiple linear regression method. The 6-variable linear model developed has high quality where the correlation coefficient of estimations and the root mean square error of estimations are 0.9604 and 0.280, respectively, and the correlation coefficient of predictions and the root mean square error of predictions for leave-one-out procedure are 0.9471 and 0.324, respectively.

A com bi na torial method for es ti mat ing and pre dict ing the bi o log i cal ac tiv i ties of two sets of dipeptides, a set of 48 com pounds and an other set of 58, was de vel oped. The mo lec u lar ho lo graphic dis tance vec tor (MHDV) was em ployed to char ac ter ize the struc tures of the pep tide mol e cules. Pre lim i nary se lec tion of the MHDV descriptors was per formed based on the num ber of the mol e cules hav ing non-zero MHDV val u es. The fi nal op ti mal descriptors were com pleted by a ge netic al go rithm-based vari able se lec tion pro ce dure. Then the op ti mal descriptors are used to re late to the bi o log i cal ac tiv i ties of the pep tides us i ng the mul ti ple lin ear re gres sion (MLR) method. For two pan els of dipeptides, the cor re la tion co ef fi cient of es ti m a tions (r) are re spec tively 0.9651 for 48 pep tides and 0.936 for 58 pep tides, and the cor re la tion co ef fi cient of leave-one-out pre dic tions (q) are re spec tively 0.9452 and 0.9075. Mo lec u lar struc tural char ac ter iza tion (MSC) is an im por tant and in dis pens able tech nique in mod ern drug mo lec u lar de sign and the eval u a tion of phar ma co log i cal pro files. To study the bi o log i cal ac tiv i ties of the mol e cules and de velop the quan ti ta tive struc ture-activity re la tion ships (QSAR) so as to de sign fur ther novel mol e cules and eval u ate their bi o log i cal ac tiv i ties, it is es sen tial to de scribe the chem i cal struc tures of the mol e cules. The cur rent meth ods of MSC in clude two-dimensional top o log i cal descriptors, en er getic descrip tors, quan tum me chan i cal descriptors, and three-dimensional mo lec u lar field descriptors. 1

A molecular electronegativity–distance vector (MEDV) has been proposed to describe the structure of polycyclic aromatic hydrocarbons (PAHs) and relate to their retention indices (RI) for programmed temperature SE-52 capillary-column gas chromatography. The 209 PAHs investigated contain not only one or two heteroatoms such as nitrogen, oxygen and sulfur but also one, two or more conjugated rings. Applying multiple linear regression (MLR) in combination with the cross-validation (CV) technique, a four-parameter quantitative structure–retention relationship (QSRR) of 209 PAHs is developed with the correlation coefficient ( R ) of 0.9812 and the root mean square error (RMS) of 15.533 between the estimated and experimental retention indices (RI). It was found that the errors for oxygen-containing PAHs are often positive and for sulfur-containing PAHs, negative. Thus, MEDV is modified by lengthening the CO or CO bond and by shortening the CS bond. Leaving out several outliers, a better QSRR is described with R =0.9936 and RMS=9.212 for 172 PAHs.

Quantitative structure-property relation ships (QSPR) were developed with the quantum semiempirical descriptors computed by AM1 Hamiltonian in MOPAC7.0 for phenylthio, phenylsulfinyl and phenylsulfonyl esters. Using step wise regression analysis with a cross-validation procedure, the most potent and in formative descriptors were screened out from a group of 19 quantum chemical semi-empirical descriptors, including steric and electronic types, to build QSPR models. Several equations were obtained and used to estimate and predict octanol/water partition coefficients and reversed phase high-performance liquid chromatography (HPLC) capacity factors for a series of 30 similar sulfur-containing compounds. The results indicate that molecular descriptors, including average molecular polarizability (α), energy of the lowest unoccupied molecular orbital (ELUMO), net atomic charge on carbon atoms in the carbonyl (QCO), molecular weight (MW), the most positive atomic charge on hydrogen atom (QH+), and net atomic charge on oxygen atoms in the group-NO2 (QNQ2) are the main factors affecting the octanol/water partition coefficients of the compounds under study. And quantum descriptors, covering the most negative atomic charge on an atom Q−), net atomic charge on carbon atoms in the carboxy (QCOO), molecular weight (MW), heat of formation (HF), the most positive atomic charge on a hydrogen atom (QH+), and total energy (TE), are the main factors affecting HPLC capacity factors of these compounds. Rational mechanisms for the two physico-chemical proper ties of the sulfur-containing carboxylates were discussed and interpreted.

The relative polarizability effect of alkyl substituent can be conveniently obtained by calculating the so-called Polarizability Effect Index (PEI) value, proposed firstly in our laboratories, PEI = Σ (a i /N i 2 ), where a i is the polarizability of primary units CH3, CH 2 -, CH C-, and the N i is the carbon atom number from reaction site to the primary unit. By using the PEl and atomic charge in a molecule, the ionization potential of alkyl halide, the enthalpy of reaction for the gas-phase addition of a proton to an alcohol or ether (proton affinity), the gas-phase basicity of alcohols or ethers (proton affinity), and gas-phase acidity of alcohols can be quantitatively modeled by the two-parameter expression equation.

Based on the distance between atoms and the electro-negativity of each atom, a new set of descriptors called the molecular electronegativity edge vector (VMEE) being applied to de scribe the molecular structure of peptide analogues, is proposed only from the primary structure of peptides. Here several quantitative structure activity relationship models are proposed on the biological activity of 58 angiotensin converting enzyme (ACE) inhibitors and of 47 bitter tasting dipeptides by multiple linear regression method. The results show that the novel molecular electronegativity edge vector (VMEE) has both excellent structural selectivity and good activity estimation. Besides, this novel molecular electronegative edge vector, be cause it is easy to calculate, will be useful in structure characterization and activity prediction of biological molecules.

A wavelet neural network (WNN) is employed to create a quantitative structure-retention index relationship, which correlates the novel molecular distance edge vector (MDEV)-consisting of ten elements to Gas Chromatographic retention indexes (RI G C ) of Alkanes. The RI G C has been calculated by the WNN from the molecular topological descriptors of examined alkanes. In this work, the RI G C estimated and predicted by conventional neural networks (say back propagation neural networks, BP) has also been provided. The excellent predicted results with a correlation of 0.9996 and standard deviation of 5.0598 suggest that the WNN technique is a powerful tool in QSAR/QSPR modeling and superior to the BP neural networks.

A novel molecular holographic distance vector (MHDV) is proposed to characterize the structures of the peptide molecules and employed to relate to the biological activities of the peptides by means of principal component regression (PCR) method. For two pan els of dipeptides, the correlation coefficient (R) between the estimated and the observed activities are respectively 0.9370 and 0.9585 and the R obtained by cross-validation method are respectively 0.8676 and 0.9295, which is the best result to date for the two sets of dipeptides. The novel MHDV descriptor only depends on distance matrix and various atomic types of non-hydrogen atoms in a molecule and requires no 3D structural information, so, it is a very simple and easy to use descriptor.

A set of novel graph theoretical parameters, called the atomic distance–edge vector (ADEV), was developed in our laboratories. A regression equation linking the carbon-13 chemical shift (CS) to an ADEV containing 16 descriptor variables of various chemically non-equivalent carbon atoms in a molecule was obtained using multiple linear regression (MLR). The regression model was used to predict the carbon-13 nuclear magnetic resonance (13C NMR) spectra of unknown alkanes. It was found that the estimated CS values were in good agreement with the experimental CS values. Copyright © 2001 John Wiley & Sons, Ltd.

A new method of quantitative structure-retention relationship (QSRR) is proposed for estimating and predicting gas chromatographic retention indices of alkanes by using a novel molecular distance-edge vector, called μ vector, containing 10 elements. The QSRR model (Ml), between the μ vector and chromatographic retention indices of 64 alkanes, was developed by using multiple linear regression (MLR) with the correlation coefficient being R = 0.9992 and the root mean square (RMS) error between the estimated and measured retention indices being RMS = 5.938. In order to explain the equation stability and prediction abilities of the M1 model, it is essential to perform a cross-validation (CV) procedure. Satisfactory CV results have been obtained by using one external predicted sample every time with the average correlation coefficient being R = 0.9988 and average RMS = 7.128. If 21 compounds, about one third drawn from all 64 alkanes, construct an external prediction set and the 43 remaining construct an internal calibration set, the second QSRR model (M2) can be created by using calibration set data with statistics being R = 0.9993 and RMS = 5.796. The chromatographic retention indices of 21 compounds in the external testing set can be predicted by the M2 model and good prediction results are obtained with R = 0.9988 and RMS = 6.508.

The oxygen atom in the hydroxyl group OH of an alcohol molecule is considered a pseudo-carbon atom, like a carbon atom in alkanes, for our present case of extension from alkanes to alcohols. The ratio of electronegativity of oxygen (ENO) to that of carbon (ENC) is defined as the relative electronegativity, REN = ENO/ENC, of a oxygen atom under the condition of REN being 1 for a carbon atom; similarly, the value of bond length for the bond C−O (BLO) over that of the bond C−C (BLC) is regarded as the relative bond length, RBL = BLO/BLC, of the bond C−O with RBL being equal to 1 for C−C. The molecular distance−edge vector, i.e., vector μ, for alkanes can be extended to describe the molecular structure of alcohols instead of alkanes. A quantitative structure−property relationship (QSPR) equation can be modeled by using multiple linear regression (MLR). The first model, M1, between the modified μ vector and boiling points of 106 alcohols is created with correlation coefficient being R = 0.9951 and root-mean-sq...

Models that estimate and predict the normal boiling point (NBP) of alkanes based on a molecular distance-edge (MDE) vector, λ, have been developed by using multiple linear regression (MLR) methods. The structures of the examined compounds are selectively described by an MDE vector structure descriptor, a novel molecular distance-edge vector recently developed in our laboratory. MLR was used to develop a linear model containing ten variables with a high precision root mean squares error (RMS = 4.985K) and a good correlation with the correlation coefficient (R = 0.9948). In addition, a predictive model has been developed by using 125 isomers in alkanes as the training set, and its performance was certified by employing 25 alkanes chosen randomly as the test set from a total of 150 alkane compounds; excellent predicted results were obtained with the RMS and R values found between the calculated value and observed NBP being RMS = 4.486K and R = 0.9945.

Vibrio fischeri is a marine luminous bacterium that has been widely used in toxicity bioassays, while Vibrio qinghaiensis sp.-Q67 is a newly found freshwater species which is more suitable for the tests on freshwater samples. However, there is a sensitive difference between these two species due to the media, chemical modes of action and the tested species. It remains unclear how these factors induce toxicity changes in luminous bioassays. Therefore, by using molecular docking between reactive chemicals and the target proteins of Vibrio fischeri and Vibrio qinghaiensis sp.-Q67 respectively, the sensitive difference was explored from the angle of amino acid residues that involved in the interactions. Mutation of amino acid residues was performed to investigate the role of these amino acids in the interactions and the most important amino acid residues in toxicity effect were found. The results suggested tat the most important amino acid residues in toxicity effect would affect the binding affinity between chemicals and target proteins of Vibrio fischeri and Vibrio qinghaiensis sp.-Q67, and then induce distinct toxic effect on them. As there are fewer toxicity data for freshwater Vibrio qinghaiensis sp.-Q67 than for Vibrio fischeri, this study helps to take advantage of the plentiful toxicity data of Vibrio fischeri to predict toxicities of freshwater samples.

The MEDV-13, molecular electronegativity distance vector based on 13 atomic types, has at best 91 descriptors. It is impossible to indirectly use multiple linear regression (MLR) to derive a quanti...

Only from the primary structures of peptides, a new set of descriptors called the molecular electronegativity edge-distance vector (VMED) was proposed and applied to describing and characterizing the molecular structures of oligopeptides and polypeptides, based on the electronegativity of each atom or electronic charge index (ECI) of atomic clusters and the bonding distance between atom-pairs. Here, the molecular structures of antigenic polypeptides were well expressed in order to propose the automated technique for the computerized identification of helper T lymphocyte (Th) epitopes. Furthermore, a modified MED vector was proposed from the primary structures of polypeptides, based on the ECI and the relative bonding distance of the fundamental skeleton groups. The side-chains of each amino acid were here treated as a pseudo-atom. The developed VMED was easy to calculate and able to work. Some quantitative model was established for 28 immunogenic or antigenic polypeptides (AGPP) with 14 (1-14) A(d) and 14 other restricted activities assigned as "1"(+) and "0"(-), respectively. The latter comprised 6 A(b)(15-20), 3 A(k)(21-23), 2 E(k)(24-26), 2 H-2(k)(27 and 28) restricted sequences. Good results were obtained with 90% correct classification (only 2 wrong ones for 20 training samples) and 100% correct prediction (none wrong for 8 testing samples); while contrastively 100% correct classification (none wrong for 20 training samples) and 88% correct classification (1 wrong for 8 testing samples). Both stochastic samplings and cross validations were performed to demonstrate good performance. The described method may also be suitable for estimation and prediction of classes I and II for major histocompatibility antigen (MHC) epitope of human. It will be useful in immune identification and recognition of proteins and genes and in the design and development of subunit vaccines. Several quantitative structure activity relationship (QSAR) models were developed for various oligopeptides and polypeptides including 58 dipeptides and 31 pentapeptides with angiotensin converting enzyme (ACE) inhibition by multiple linear regression (MLR) method. In order to explain the ability to characterize molecular structure of polypeptides, a molecular modeling investigation on QSAR was performed for functional prediction of polypeptide sequences with antigenic activity and heptapeptide sequences with tachykinin activity through quantitative sequence-activity models (QSAMs) by the molecular electronegativity edge-distance vector (VMED). The results showed that VMED exhibited both excellent structural selectivity and good activity prediction. Moreover, the results showed that VMED behaved quite well for both QSAR and QSAM of poly-and oligopeptides, which exhibited both good estimation ability and prediction power, equal to or better than those reported in the previous references. Finally, a preliminary conclusion was drawn: both classical and modified MED vectors were very useful structural descriptors. Some suggestions were proposed for further studies on QSAR/QSAM of proteins in various fields.

The structural and thermodynamic properties of 76 polychlorinated naphthalenes (PCNs) were fully computed at B3LYP/6-31G* level. Both structural and thermodynamic parameters of PCNs obtained were consequently taken as theoretical descriptors and correlated with their gas chromatographic retention indices (RI), so as to develop the relevant quantitative structure-retention relationship (QSRR) regression model (model I) with r2 of 0.9957, which possesses high correlation, high predictive power and clear physical interpretations. Secondly, another linear QSRR model (model II) was achieved by employing the number and position of chlorine substitution as descriptors, of which r2 was 0.9967, and also the main factors affecting the retention time of PCNs were investigated.

Polychlorinated biphenyls (PCBs) congeners with various degrees of chlorination and substitution patterns are among the most widespread and persistent man-made organic pollutants. They are toxic, lipophilic and tend to be bioaccumulated. The knowledge of the physico-chemical properties is very useful to explain the environmental behavior of PCBs and to perform an exposure assessment. In this paper, we have used a new molecular representation, the molecular hologram, to generate quantitative structure-property relationship models to predict the physico-chemical properties of biphenyl and all of its chlorinated congeners. The investigated properties include 1-octanol/water partition coefficient (logK(ow)), aqueous solubility (-logS(w)), aqueous activity coefficient (-logY(w)), Total molecular surface area, Henry's law constant (logH). The results show that this new quantitative structure-activity relationship approach presents highly predictive models for important physico-chemical properties of PCBs.

The MEDV-13, molecular electronegativity distance vector based on 13 atomic types, has at best 91 descriptors. It is impossible to indirectly use multiple linear regression (MLR) to derive a quantitative structure-activity relationship (QSAR) model. Although principal component regression (PCR) or partial least-squares regression (PLSR) can be employed to develop a latent QSAR model, it is still difficult how to determine the principal components (PCs) and depict the physical meaning of the PCs. So, a genetic algorithm (GA) is first employed to select an optimal subset of the descriptors from original MEDV-13 descriptor set. Then MLR is utilized to build a QSAR model between the optimal subset and the biological activities of three sets of compounds. For 31 benchmark steroids, a 5-descriptor QSAR model (M1) between the corticosteroid-binding globulin (CBG) binding affinity of the steroids and 5-descriptor subset is developed. The root-mean-square error of estimations (RMSEE) and the correlation coefficient of estimations (r) between the CBG binding affinity (BA) observed and the BA estimated by M1 are 0.422 and 0.9182, respectively. The root-mean-square error of predictions (RMSEP) and the correlation coefficient of predictions (q) between the BA observed and the BA predicted by leave-one-out cross validations are 0.504 and 0.8818, respectively. For 58 dipeptides inhibiting angiotensin-converting enzyme (ACE), a 5-variable QSAR model (M2) between the pIC(50) of peptides and 5-descriptor subset is derived. The M2 has a high quality with RMSEE = 0.339 and r = 0.9398 and RMSEP = 0.370 and q = 0.9280. For 16 indomethacin amides and esters (ImAE) inhibiting cyclooxygenase-2 (COX-2), a 6-variable QSAR model (M3) with RMSEE = 0.079 and r = 0.9839 and RMSEP = 0.151 and q = 0.9413 is built.

Chemical structures of polychlorinated dibenzodioxin (PCDD) congeners are described by a novel molecular distance edge vector (VMDE), developed in our laboratory, that consists of the modified molecular distance edge parameters based on the identical group as a pseudo-atom instead of a traditional atom. Quantitative structure retention relationships (QSRRs) between the new VMDE parameters and the gas chromatographic retention behavior of PCDDs are then generated by a multiple linear regression method for nonpolar, moderately polar, and polar stationary phases. All QSRR models with a high correlation (R0.99) are developed for nonpolar, moderately polar, and polar columns (DB-5, SP-2100, SE-54, and OV-1701). Cross validation with the leave-one-out procedure is performed, and satisfactory results are obtained with high correlation. The obtained results show that the new VMDE vector is adapted to characterize the chemical structure and model the retention behavior of PCDDs on various polar stationary phases.

Several quantitative structure-property relationship (QSPR) models between 15 basic physical properties or thermodynamic functions of alkanes and their molecular electronegative distance vectors (MEDV) are developed. For six of the properties-boiling point (BP), density (D) at 25 degrees C, refraction index (RI) at 25 degrees C, critical temperature (CT), critical pressure (CP), and surface tension (ST) at 20 degrees C-logarithmic models are found to give better results than conventional (linear) models since the values of these properties all tend to a limit with increasing carbon chain length. All models are created using multiple linear regression (MLR). Conventional models are proposed for the remaining nine physical properties or thermodynamic functions: molar volume (MV) at 20 degrees C, molar refraction (MR) at 20 degrees C, heat capacity (HC) at 300 K, enthalpy (E) at 300 K, heats of vaporization (HV) at 25 degrees C, heat of atomization (HA) at 25 degrees C, standard heat of formation (HF) at 25 degrees C, heat of formation in liquid (HFL) at 25 degrees C, and heat of formation in gas (HFG) at 25 degrees C.

Molecular imprinted thin films (MIFs) of TiO2 were prepared with a liquid phase deposition (LPD) method, and characterized by FT-IR spectroscopy, UV-visible solid-state reflection spectroscopy, X-ray diffraction, and scanning electron microscopy. Among different approaches of removing the template in the preparation of MIFs, a calcination treatment was the best to produce more 3D “molecular footprint” cavities of the template on the MIF, which promoted further the photocatalytic activity of the MIF in comparison with the films pre-treated by extraction or photodegradation. Compared with the non-imprinted TiO2 film (NIF), the MIF enhanced the photodegradation of the target pollutants by increasing the adsorption of the target pollutants on the surface of the MIF. From the Langmuir–Hinshelwood model, the value of the apparent reaction rate constant on the MIF was obtained, which was much larger than that on the NIF. The equilibrium adsorption constant on the MIF was more than 7 times that on the NIF. Because of this high affinity, the MIF exhibited special molecular recognition ability, leading to selective adsorption and photodegradation of the target pollutant. Moreover, the MIF was confirmed to have good stability during long-time photocatalysis.

Pentachlorophenol (PCP) is a typical highly-toxic pollutant, and its direct photolysis and conventional photocatalysis may produce more toxic by-products such as dibenzodioxins. It is urgently needed to develop a photocatalytic process able to remove PCP without the generation of highly toxic by-products. To achieve this, enzyme-like molecular-imprinted photocatalysts were prepared by using structural analogues of PCP as pseudo templates. It was found that 2,4-dinitrophenol (DNP) was the best template among the tested analogues. The molecular imprinted polymer (MIP) coated P25 TiO2 photocatalyst DNP–P25 prepared with DNP as the template greatly accelerated the photocatalytic degradation of PCP and depressed the generation of toxic intermediates. It was confirmed that the amino groups at the footprint cavities provided a well-defined micro reaction environment, which made the benzene ring of the adsorbed PCP be better exposed to photo-generated reactive OH radicals, leading to easier cleavage of the benzene ring. Both the intermediate analysis and toxicity evaluation confirmed that the MIP-coated TiO2 can make the photocatalytic degradation a safe and green approach of removing PCP.

Experimental and epidemiological studies suggest that some man-made and naturally occurring chemicals related to the environment have the potential to interrupt normal functioning of the endocrine systems of humans and wildlife. These chemicals, termed EDCs (Endocrine disrupting Chemicals), pose serious threats to the reproductive capability of humans and wildlife. Because of the structural diversity and various types, development of structure-based rapid screening methodologies is important and necessary for the assessment of the environmental pollutants. In this paper molecular hologram based QSAR models were developed with the combinatory application of partial least square (PLS) regression for a large diverse set of 105 environmental estrogens. Quantitatively predictive models were developed based on only molecular structures, which can be used for the accurate prediction of estrogenicity to rapidly screen potential environmental endocrine disrupting chemicals.

Nitroaromatics are typical toxic organic pollutants and are ubiquitous in environment with diverse structures. They are widely used in many industries and formed during many natural and anthropogenic processes. Most of these pollutants are potentially carcinogenic and the assessment and prediction of the mutagenicity of nitroaromatics are of great interest. In this paper the structure-mutagenicity relationships of 219 nitroaromatics are investigated by molecular orbital theory based classic structure-activity relationships and comparative molecular field analysis (CoMFA). A comparison is undertaken in respect of the interpretation of mechanism and predictive ability for these two categories of QSAR approaches and highly predictive QSAR models have been developed.