Showing papers in "Sar and Qsar in Environmental Research in 1993"
TL;DR: The log-log relationship between the bioconcentration tendency of organic chemicals in fish and the n -octanol/water partition coefficients breaks down for very hydrophobic compounds, so the use of parabolic and bilinear models allows this problem to be overcome.
Abstract: The log-log relationship between the bioconcentration tendency of organic chemicals in fish and the n−octanol/water partition coefficients breaks down for very hydrophobic compounds. The use of parabolic and bilinear models allows this problem to be overcome. The QSAR equation log BCF = 0.910 log P - 1.975 log (6.8 10−7 P + 1) - 0.786 (n = 154; r = 0.950; s = 0.347; F = 463.51) was found to be a good predictor of bioconcentration in fish.
126 citations
TL;DR: It is observed that the dependence of toxicity on log P in different isoelectrophilic windows decreased as reactivity increased, which is consistent with toxicity models where competing nucleophilic interaction sites are distributed along the transport route of the chemicals.
Abstract: The toxicity of chemicals is orthogonal with individual molecular descriptors used to quantify hydrophobicity and soft electrophilicity when considering large data sets. Estimating the toxicity of reactive chemicals requires descriptors of both passive transport and the stereoelectronic interaction, which are largely independent processes. QSARs using either log P or an electronic parameter alone are only significant for sets of chemicals that represent special, albeit some important, cases in QSAR. Chemicals were clustered according to their reactivity as soft electrophiles by defining isoelectrophilic windows along the toxicity response surface. Within these narrow windows of reactivity, the variation of toxicity was explained by the variation of log P. We observed that the dependence of toxicity on log P in different isoelectrophilic windows decreased as reactivity increased. The data are consistent with toxicity models where competing nucleophilic interaction sites are distributed along the t...
63 citations
TL;DR: In this article, it was shown that the entropy of melting, ΔSm, is related to symmetry by ΔSm = 13.5−R In[sgrave], and that molecular symmetry plays an important role in the determination of melting it is also important in determining vapor pressure and ideal solubility.
Abstract: The molecular rotational symmetry number, [sgrave], is defined and evaluated for a wide range of rigid organic compounds. Molecular symmetry is an important determinant of a variety of properties of crystalline substances. It is shown that the entropy of melting, ΔSm, is related to symmetry by ΔSm = 13.5–R In[sgrave]. Since molecular symmetry plays an important role in the determination of the entropy of melting it is also important in determining vapor pressure and ideal solubility, both of which depend upon entropy of melting.
37 citations
TL;DR: In this article, the authors use QSAR to partition coefficients between soil and plants, air and plants and between animal diet and lipid tissues as case studies for characterizing the precision of ITF estimations.
Abstract: Dose assessments based on multimedia exposure models require intermedia-transfer factors (ITFs) as input. These factors relate contaminant concentrations in an environmental medium (the source medium such as air. water, or soil for which measurements are available) to the exposure medium (personal air, tap water, and food products) with which humans have contact. In this paper, I use QSAR as applied to partition coefficients between soil and plants, air and plants, and between animal diet and lipid tissues as case studies for characterizing the precision of ITF estimations. These partition coefficients form the basis of total dose models required for realistic exposure/risk assessments. The quantitative relationship between ITFs and chemical properties are developed along with the estimation errors associated with these relationships. Many of these correlations have large estimation errors that limit their reliability as applied in risk assessment. I examine factors that contribute to these estim...
36 citations
TL;DR: Comparison with discriminant factor analysis showed that a backpropagation NN was more appropriate to model the field half-lives of pesticides.
Abstract: The field half-lives of 110 pesticides were modelled using a backpropagation neural network (NN). The molecules were described by means of the frequency of 17 structural fragments. Before training the NN, different scaling transformations were assayed. Best results were obtained with correspondence factor analysis which also allowed a reduction of dimensionality. The training and testing sets of the NN analysis gave 95.5% and 84.6% of good classifications, respectively. Comparison with discriminant factor analysis showed that a backpropagation NN was more appropriate to model the field half-lives of pesticides.
33 citations
TL;DR: AQUAFAC, a new group contribution method for estimating aqueous activity coefficients, has been applied to a large set of organic compounds as mentioned in this paper, which introduces 27 new group values for hydrocarbon, halogen, and non-hydrogen bond donating oxygen groups.
Abstract: AQUAFAC, a new group contribution method for estimating aqueous activity coefficients, has been applied to a large set of organic compounds. The current work introduces 27 new group values for hydrocarbon, halogen, and non-hydrogen bond donating oxygen groups. Group values (q-values) have been derived from a data set of 621 compounds representing over 1700 individual solubility values. No correction factors were used in generating the current group values. AQUAFAC was found to give acceptable results when applied to some environmentally important compounds.
32 citations
TL;DR: Results suggest, at least for reversible physical mechanisms, that volume fraction analyses are significant in determining the mechanism of toxic action of a chemical.
Abstract: The relative toxicity of selected industrial organic chemicals was secured from the literature for the static 48-h Tetrahymena pyriformis 50% population growth impairment and the flow-through 96-h Pimephales promelas 50% mortality endpoints. Chemicals were selected to represent the nonpolar narcosis (aliphatic alcohols and aliphatic ketones) and polar narcosis (anilines and phenols) mechanisms of toxic action. molar volume (MV) and 1-octanol/water partition coefficient (log Kow) data were generated for each chemical. High-quality, log Kow dependent quantitative structure-activity relationships were observed for each chemical class and mechanism of action for both endpoints. The volume fraction (Vf) for each chemical in the target phase was determined from the toxicant concentration in the water (toxicity data), the MV, and the target/water partition coefficient (Ktw) with Ktw considered equal to Kow (1-a). Analyses of target sites, by way of "a" revealed that "a" was constant for a mechanism of action regardless of chemical class, but distinct for a given test system. Mean Vt was constant for each mechanism of action regardless of chemical class or test system. These results suggest, at least for reversible physical mechanisms, that volume fraction analyses are significant in determining the mechanism of toxic action of a chemical.
29 citations
TL;DR: Partially recurrent neural networks with different topologies are applied for secondary structure prediction of proteins, which predicts the secondary structures alpha-helix, beta-sheet, and "coil" for each amino acid.
Abstract: Partially recurrent neural networks with different topologies are applied for secondary structure prediction of proteins. The state of some activations in the network is available after a pattern presentation via feedback connections as additional input during the processing of the next pattern in a sequence. A reference data set containing 91 proteins in the training set and 15 non-homologous proteins in the test set is used for training and testing a network with a modified, hierarchical Elman architecture. The network predicts the secondary structures α-helix, β-sheet, and “coil” for each amino acid. The percentage of correctly classified amino acids is 67.83% on the training set and 63.98% on the test set. The best performance of a three-layer feedforward network is 62.7% on the same test set. A cascaded network, where the outputs of the recurrent network are processed by a second net with 13 × 3 inputs, four hidden and three output units has a predictive performance of 64.49%. The best corre...
21 citations
TL;DR: In this paper, simple techniques to predict the melting point and boiling point of non-hydrogen bonding rigid molecules have been developed using additive constitutive properties e.g., molecular fragments and a non-additive non-constitutive property, molecular symmetry.
Abstract: Simple techniques to predict the melting point (T m ) and boiling point (T b ) of non-hydrogen bonding rigid molecules have been developed. The compounds used include halogen, methyl, cyano, and nitro derivatives of benzene and polycyclic compounds. Melting point prediction uses additive constitutive properties e.g., molecular fragments and a non-additive non-constitutive property, molecular symmetry. Boiling point estimation employs only additive constitutive properties. It was found that symmetry affects the entropy of melting which in turn affects the melting point.
20 citations
TL;DR: Artificial neural networks can be used for the direct QSAR analysis of percent effect biological data, thus avoiding the bias introduced by arbitrarily chosen classes and the loss of information due to prior classification.
Abstract: Artificial neural networks (ANN) can be used for the direct QSAR analysis of percent effect biological data, thus avoiding the bias introduced by arbitrarily chosen classes and the loss of information due to prior classification. For two data sets the ANN results are compared with those obtained by adaptive least squares and nonlinear regression analyses. In comparison with the other methods the neural network shows higher predictive power and does not require an explicit equation relating the observed effect to physico-chemical descriptors.
19 citations
TL;DR: The aim of this paper was to explore the usefulness of a backpropagation neural network (BNN) to estimate the biodegradability of benzene derivatives and compared to the BIODEG probability program.
Abstract: The aim of this paper was to explore the usefulness of a backpropagation neural network (BNN) to estimate the biodegradability of benzene derivatives. 127 chemicals selected from the BIODEG data bank (Syracuse Research Corporation, 1992) were described by means of 20 structural descriptors taking into account the nature and position of the substituents on the benzene ring. Three classes of biodegradability were selected and modelled from the BNN. A 20/5/3 BNN (α = 0.8 and η = 0.5) correctly classified 92% (104/113) of the training and 86% (12/14) of the testing sets. The results were compared to those produced by the BIODEG probability program (Syracuse Research Corporation, Version 2.13).
TL;DR: In this paper, a hierarchical separation of influences by means of orthogonal descriptors in structure-property studies is emphasized, where the role of mass is removed, and the influence of other structural factors is more readily discerned.
Abstract: The dominant role of molecular mass in determining many physical properties of substances often masks significant variations of these same properties with molecular shape. Here attention is drawn to the important influence of molecular shape on molecular properties. The interdependences of the heats of vaporization, boiling points, molar volumes, molar refractions, critical temperatures, critical pressures and surface tensions of alkanes in general, and of the octanes in particular, are used to illustrate the different roles of molecular mass and shape in influencing bulk properties. The advantages of using a hierarchical separation of influences by means of orthogonal descriptors in structure-property studies are emphasized. The high interrelatedness of many physical properties in alkanes does not extend to isomeric variations, but rather reflects the dominant influence of mass on these properties. When the role of mass is removed, the influence of other structural factors is more readily discerned.
TL;DR: This work proposes, by analogy to the travelling salesman problem, a new method taking advantage of the capability of Hopfield-like neural networks to carry out combinatorial optimization of an objective function, which can also suggest partial solutions having one or two atoms less than the given pattern.
Abstract: The three-dimensional (3D)-pattern search problem can be summarized as finding, in a molecule, the subset of atoms that have the most similar spatial arrangement as those of a given 3D pattern. For this NP-complete combinatorial optimization problem we propose, by analogy to the travelling salesman problem, a new method taking advantage of the capability of Hopfield-like neural networks to carry out combinatorial optimization of an objective function. This objective function is built from the sum of the differences of interatomic distances in the pattern and the molecule. Here we present the implementation we have found of the 3D-pattern search problem on Hopfield-like neural networks. Initial tests indicate that this approach not only successfully retrieves a given pattern, but can also suggest partial solutions having one or two atoms less than the given pattern, an interesting feature in the case of local conformational flexibility of the molecule. The distributed representation of the problem...
TL;DR: A neural network was applied to a large, structurally heterogeneous data set of mutagens and non-mutagens to investigate structure-property relationships to demonstrate how the network was classifying the data.
Abstract: A neural network was applied to a large, structurally heterogeneous data set of mutagens and nonmutagens to investigate structure-property relationships. Substructural data comprising a total of 1280 fragments were used as inputs. The training of the back-propagation networks was directed by an algorithm which selected an optimal subset of fragments in order to maximize their discriminating power, and a good predictive network. The system comprised three programs: the first used a keyfile of 100 fragments to generate training and test files, the second was the network itself and a procedure for ranking the effectiveness of these fragments and the third randomly replaced the lowest fragments. This cycle was then repeated. After running on a 386/33 PC several networks produced approximately 11% failures in the test set and 6% in the training set. By simplifying the output of the hidden layer it was possible to describe the hidden layer states in terms of clusters of mutagens and non-mutagens. Some ...
TL;DR: Two types of neural networks were used to establish relationships between chemical structure and musk odour of 79 nitrobenzenic compounds, and a dual two-way network was built to mimic the symmetry of the problem.
Abstract: Two types of neural networks were used to establish relationships between chemical structure and musk odour of 79 nitrobenzenic compounds. Substituents on the five free sites of the benzene ring (one position was always occupied by a t-butyl group) were described using three volume descriptors and three electronegativity descriptors. Musk odour was coded by a binary variable. First a classical network with two hidden layers containing six and three neurons was used. This network gave a better classification (94%) than that obtained by linear discriminant analysis (81%). The odour was then predicted using a leave-ten-out procedure, with 77% of correct prediction for the whole sample. Then a dual two-way network was built to mimic the symmetry of the problem (two sides on a molecule, two muskophore patterns). This network recognized both patterns already known to chemists and gave 99% of correct classifications by taking into account substitution in all positions. As a side benefit of the modified network structure it was possible to evaluate the influence of each of 19 substituents in each of the five possible positions.
TL;DR: A new method for predicting the impact sensitivity of explosive molecules is presented, which makes use of a network of formal neurons and shows a slight advantage to the neural network method.
Abstract: A new method for predicting the impact sensitivity of explosive molecules is presented. This method makes use of a network of formal neurons. The experiment uses 124 molecules belonging to different families. The molecular descriptors taken into account are the molecule's oxygen balance and the enumeration of certain groups. The results obtained are satisfactory: 80% of the molecules are correctly classed on a scale of four sensitivities. Comparison with a multivariate linear regression analysis gives a slight advantage to the neural network method.
TL;DR: Quantitative structure-biodegradability relationships (QSBRs) were established for a set of various organic compounds using autocorrelation components as molecular descriptors using auto-electricity, electronegativity, hydrogen bonding donor and acceptor ability and lipophilicity.
Abstract: Quantitative structure-biodegradability relationships (QSBRs) were established for a set of various organic compounds using autocorrelation components as molecular descriptors. The molecules were described by their size (van der Waals volume), electronegativity, hydrogen bonding donor and acceptor ability and lipophilicity (log P). In addition to the established models for alcohols, ketones, and aromatics, we have elaborated a model for both alcohols and ketones (5-day BOD = 0.06 V 0 + 1.067 log P - 0.356 (log P)2; n = 29, r = 0.958, s = 0.44, F = 145.6) and another for all the compounds (5-day BOD = 0.065 V 0 + 0.748 log P - 0.316 (log P)2; n = 43, r = 0.906, s = 0.575, F = 91.2).
TL;DR: This research showed that MEP maps provide a signature that distinguishes between active and inactive compounds.
Abstract: The present study was performed on a group of 27 derivatives of phenylsuccinimides, of which only 12 were active against maximal electrical shock in spite of the structural similarities of these compounds. The work consisted of four main parts: 1. crystallographic investigations of a subset of chosen compounds; 2. conformational analysis of characteristic molecules from the investigated series, performed by means of molecular mechanics calculations; 3. molecular orbital optimization of all the molecules using the MNDO method starting with conformations obtained in 2; 4. molecular electrostatic potential (MEP) analysis which was performed on the semiempirical (MNDO) and ab initio levels. This research showed that MEP maps provide a signature that distinguishes between active and inactive compounds. There are MEP minima close to the two carbonyl oxygens of the imide ring, and although the magnitude of the difference between the two minima is approximately constant, the sign of the difference provid...
TL;DR: The authors proposed "descriptor mapping" in the QSAR analysis, which visualizes the nonlinear dependencies between structural parameters in the linear multiple regression analysis.
Abstract: In addition to its outstanding abilities in both classification and fitting, the neural network can also accurately predict the values of the untrained region. To rationalize this ability of prediction, the authors mathematically discussed the valid region of prediction. Based on such a background, the authors proposed “descriptor mapping” in the QSAR analysis, which visualizes the nonlinear dependencies between structural parameters. A variable of the linear multiple regression analysis in the QSAR study is supposed to be linear to the biological intensity and is independent of other variables. Analysis by the descriptor mapping method discloses the reality.
TL;DR: In this article, a topological method for molecule volume prediction based on local fragments centered on all carbons of aliphatic alkanes (C6-C11, C12) is proposed and evaluated.
Abstract: Topological methods for molecule volume prediction based on local fragments centered on all carbons of aliphatic alkanes (C6-C11, C12) are proposed and evaluated. The LOGIC method dealing with local to global information construction is based on local environment (FRELB) partition of information, both structural and physical, and the use of these FRELs to correlate the molal volumes V. Different topological vectorial sets of various depths, dealing with atom and bond FRELs, are used to optimize the contributions V D of a set of prime FRELs constituting the reference set structured in a hyperstructure linking 70 FRELB. The accuracy of determining fragment volumic contributions is traced by the decreasing number of hits resulting in the modeling of the C6-C11 populations. The training set of the V values is tested on the 355 alkanes of the C12 population. This extrapolation leads to excellent statistical results of the LOGIC method, which is a “parent structure free” similarity correlation although...
TL;DR: Using the molecular modeling program SYBYL, a conformational analysis of epibatidine has been performed and it is apparent that the pyridine nitrogen atom is in a close position to the bridging oxygen atom of morphine.
Abstract: Using the molecular modeling program SYBYL, a conformational analysis of epibatidine has been performed. Two pairs of stable conformations due to the rotational degree of freedom for the pyridine ring have been found. These conformations were compared with morphine regarding spatial arrangements as well as electronic aspects. A very close agreement between the essential receptor positions occurring in morphine and epibatidine could be demonstrated. The protonable nitrogen atom in epibatidine is in exactly the same spatial position as in morphine, if the pyridine ring and the phenolic ring of morphine were matched to each other. Interestingly, it is also apparent that the pyridine nitrogen atom is in a close position to the bridging oxygen atom of morphine. Furthermore, the chlorine substituent fits very well with the hydroxyl group of morphine. A chemical reaction is postulated to permit epibatidine to function as an analgesic. The carbon-chlorine bond should be activated by the neighbourhood of ...
TL;DR: In this paper, a set of 114 chemicals are compared using experimental input parameters and calculations based on estimated parameters and it is shown that estimated parameters are useful for an estimation of environmental distribution provided the estimation methods are chosen carefully and experimental melting points as well as boiling points are available.
Abstract: Fugacity calculations according to Mackay and Paterson have been performed for a set of 114 chemicals. Calculations using experimental input parameters and calculations based on estimated parameters are comparatively presented. It is shown that estimated parameters are useful for an estimation of environmental distribution provided the estimation methods are chosen carefully and experimental melting points as well as boiling points are available. Estimation methods for vapour pressure and water solubility need further development.
TL;DR: In this article, the activity of a set of anilide inhibitors of the Hill reaction was modeled using the traditional Hansch approach and Comparative Molecular Field Analysis (CoMFA).
Abstract: The activity of a set of anilide inhibitors of the Hill reaction was modeled using the traditional Hansch approach and Comparative Molecular Field Analysis (CoMFA). In the “best” Hansch model the most relevant parameters were the hydrophobic constants associated to substituents at the 3- and 4-positions of the benzene ring (π3 and π4) and the B 1 Verloop's parameter describing the “minimum width” of the substituent attached to the carbonyl. Successively, a combined “Hansch–CoMFA” analysis was performed using as descriptors the steric field of the acyl substituents in conjunction with the π3 and π4 constants multiplied by proper weighting factors. The results of this latter type of analysis were significantly better than those obtained through the traditional Hansch approach. The predictive ability of the “Hansch–CoMFA” model was further tested by predicting the activity of a large number of anilides not included in the training set. The residuals of such predictions indicated that the Hansch–CoMF...