Showing papers in "Sar and Qsar in Environmental Research in 2003"
TL;DR: A comparative study of stepwise- MLR, PLS and GA-MLR in deriving QSAR models for datasets of α1-adrenoreceptor antagonists and β3-adenoreceptor agonists has been carried out using the public domain software Dragon for computing descriptors and free Matlab codes for data modeling.
Abstract: The use of the internet has evolved in quantitative structure-activity relationship (QSAR) over the past decade with the development of web based activities like the availability of numerous public domain software tools for descriptor calculation and chemometric toolboxes. The importance of chemometrics in QSAR has accelerated in recent years for processing the enormous amount of information in form of predictive mathematical models for large datasets of molecules. With the availability of huge numbers of physicochemical and structural parameters, variable selection became crucial in deriving interpretable and predictive QSAR models. Among several approaches to address this problem, the principle component regression (PCR) and partial least squares (PLS) analyses provide highly predictive QSAR models but being more abstract, they are difficult to understand and interpret. Genetic algorithm (GA) is a stochastic method well suited to the problem of variable selection and to solve optimization problems. Consequently the hybrid approach (GA-MLR) combining GA with multiple linear regression (MLR) may be useful in derivation of highly predictive and interpretable QSAR models. In view of the above, a comparative study of stepwise-MLR, PLS and GA-MLR in deriving QSAR models for datasets of alpha1-adrenoreceptor antagonists and beta3-adrenoreceptor agonists has been carried out using the public domain software Dragon for computing descriptors and free Matlab codes for data modeling.
93 citations
TL;DR: Predicting ecotoxicity and other (quantitative) structure–activity relationships ((Q)SARs) programs will play an important role in future chemical policies, such as in the European Union and The Netherlands, to reduce animal testing and costs and to speed up the number of risk assessments for hazardous chemicals.
Abstract: ECOSAR and DEREKfW predictions for the (eco)toxicological effects of circa 70 substances were compared with experimental data for risk assessment purposes. These and other (quantitative) structure–activity relationships ((Q)SARs) programs will play an important role in future chemical policies, such as in the European Union and The Netherlands, to reduce animal testing and costs and to speed up the number of risk assessments for hazardous chemicals. The two programs, ECOSAR and DEREKfW, were selected because they are easy to use and transparent in their predictions. They predict to which chemical class a substance belongs and also predict some (eco)toxicological properties. ECOSAR categorised 87% of the chemicals correctly in chemical classes. With regard to predicting ecotoxicity, criteria were drawn up for the reliability of the QSARs provided by ECOSAR. Application of these criteria had the result that half of the regression lines from ECOSAR were considered unreliable beforehand. It turned out, howeve...
63 citations
TL;DR: The adverse effects of 158 pesticides to the Honey bee, the alfalfa leafcutting bee and the alkali bee were compared and M. rotundata appeared the most susceptible to pesticides followed by N. melanderi.
Abstract: The adverse effects of 158 pesticides to the Honey bee (Apis mellifera), the alfalfa leafcutting bee (Megachile rotundata) and the alkali bee (Nomia melanderi) were compared by means of various linear and non-linear multivariate analyses. A comparison exercise including the bumble bee (Bombus spp.) was also performed from a more restricted set of 32 pesticides. While no difference of sensitivity was found between A. mellifera and Bombus spp., M. rotundata appeared the most susceptible to pesticides followed by N. melanderi.
62 citations
TL;DR: This study demonstrates the utility of this CoMFA model for real-world use in predicting the AR binding affinities of structurally diverse chemicals over a wide RBA range.
Abstract: A large number of natural, synthetic and environmental chemicals are capable of disrupting the endocrine systems of experimental animals, wildlife and humans. These so-called endocrine disrupting chemicals (EDCs), some mimic the functions of the endogenous androgens, have become a concern to the public health. Androgens play an important role in many physiological processes, including the development and maintenance of male sexual characteristics. A common mechanism for androgen to produce both normal and adverse effects is binding to the androgen receptor (AR). In this study, we used Comparative Molecular Field Analysis (CoMFA), a three-dimensional quantitative structure–activity relationship (3D-QSAR) technique, to examine AR-ligand binding affinities. A CoMFA model with r^{2} = 0.902 and q^{2} = 0.571 was developed using a large training data set containing 146 structurally diverse natural, synthetic, and environmental chemicals with a 106-fold range of relative binding affinity (RBA). By comparing the...
60 citations
TL;DR: Based on a structural formulae of compounds presented as SDF or MOL-files, computer program PASS predicts 900 pharmacological effects, mechanism of action, and specific toxicity of chemical compounds, with an average accuracy of prediction in leave-one-out cross-validation.
Abstract: The majority of biologically active compounds have both pharmacotherapeutic and side/toxic actions. To estimate general efficacy and safety of the molecules under study, their biological potential should be thoroughly evaluated. In an early stage of study, only information about structural formulae was available and was used as an input for computational prediction. Based on a structural formulae of compounds presented as SDF or MOL-files, computer program PASS predicts 900 pharmacological effects, mechanism of action, and specific toxicity. An average accuracy of prediction in leave-one-out cross-validation is about 85%. For evaluating new compounds, scientific community may use PASS via the Internet for free at URL: http://www.ibmh.msk.su/PASS. In the first 18 months of PASS Inet's use, approximately 1000 researchers from 60 countries have obtained predicted biological activity spectra for about 23,000 different chemical compounds. More than 64 million PASS predictions for almost 250,000 compounds from ...
58 citations
TL;DR: A simple model for uptake of neutral organic contaminants into fruits considers xylem and phloem transport to fruits through the stem and finds that polar chemicals are transferred efficiently into fruits, but empirical data to verify these predictions are lacking.
Abstract: Apples and other fruits are frequently cultivated in gardens and are part of our daily diet. Uptake of pollutants into apples may therefore contribute to the human daily intake of toxic substances. In current risk assessment of polluted soils, regressions or models are in use, which were not intended to be used for tree fruits. A simple model for uptake of neutral organic contaminants into fruits is developed. It considers xylem and phloem transport to fruits through the stem. The mass balance is solved for the steady-state, and an example calculation is given. The Fruit Tree Model is compared to the empirical equation of Travis and Arms (T&A), and to results from fruits, collected in contaminated areas. For polar compounds, both T&A and the Fruit Tree Model predict bioconcentration factors fruit to soil (BCF, wet weight based) of > 1. No empirical data are available to support this prediction. For very lipophilic compounds (1ogK(ow) > 5), T&A overestimates the uptake. The conclusion from the Fruit Tree Model is that the transfer of lipophilic compounds into fruits is not relevant. This was also found by an empirical study with PCDD/F. According to the Fruit Tree Model, polar chemicals are transferred efficiently into fruits, but empirical data to verify these predictions are lacking.
54 citations
TL;DR: Drawing on the idea of permeability "enhancers" and "inhibitors", it is hypothesized that the solvents needed to orientate themselves in front of the stratum corneum layer first before penetrating through the skin.
Abstract: The permeability of a series of 12 commercial solvents through living human skin were studied by using a topological sub-structural approach (TOPS-MODE). We first analyzed the influence of several physicochemical parameters used in describing the skin permeability of the solvents. No single significant relationship was found between any of these physicochemical properties and the permeability of the solvents. A QSAR model using TOPS-MODE descriptors was obtained and validated. This model accounted for more than 95% of the variance in the experimental permeability of these solvents. Using the derived model, the structural factors responsible for the permeability of this series of solvents through living human skin were identified. Methyl groups bonded to heteroatoms or to CH 2 groups resulted in the greatest contributions to skin permeability and these groups were considered to be "permeability enhancers". In contrast, groups of the type X=O (X=S, C) were found to be "permeability inhibitors" because they ...
53 citations
TL;DR: The results indicate that from a known chemical universe, in this case 385 derivatives, robust QSARs of equal quality may be developed from a small number of diverse compounds, validated by a representative test set.
Abstract: The aim of this investigation was to develop a strategy for the formulation of a valid ecotoxicological-based QSAR while, at the same time, minimizing the required number of toxicological data points. Two chemical selection approaches-distance-based optimality and K Nearest Neighbor (KNN), were used to examine the impact of the number of compounds used in the training and testing phases of QSAR development (i.e. diversity and representivity, respectively) on the predictivity (i.e. external validation) of the QSAR. Regression-based QSARs for the ectotoxic potency for population growth impairment of aromatic compounds (benzenes) to the aquatic ciliate Tetrahymena pyriformis were developed based on descriptors for chemical hydrophobicity and electrophilicity. A ratio of one compound in the training set to three in the test set was applied. The results indicate that from a known chemical universe, in this case 385 derivatives, robust QSARs of equal quality may be developed from a small number of diverse compounds, validated by a representative test set. As a conservative recommendation it is suggested that there should be a minimum of 10 observations for each variable in a QSAR.
45 citations
TL;DR: The diagnostic method presented here is based on a probabilistic approach that exploits highly discriminative profile Hidden Markov Models, excised from low entropy regions of multiple sequence alignments, to derive potent family signatures.
Abstract: G-protein coupled receptors (GPCRs) constitute a broad class of cell-surface receptors, including several functionally distinct families, that play a key role in cellular signalling and regulation of basic physiological processes. GPCRs are the focus of a significant amount of current pharmaceutical research since they interact with more than 50% of prescription drugs, whereas they still comprise the best potential targets for drug design. Taking into account the excess of data derived by genome sequencing projects, the use of computational tools for automated characterization of novel GPCRs is imperative. Typical computational strategies for identifying and classifying GPCRs involve sequence similarity searches (e.g. BLAST) coupled with pattern database analysis (e.g. PROSITE, BLOCKS). The diagnostic method presented here is based on a probabilistic approach that exploits highly discriminative profile Hidden Markov Models, excised from low entropy regions of multiple sequence alignments, to derive potent...
41 citations
TL;DR: Based on quantum chemical descriptors, by the use of partial least squares regression, quantitative structure-property relationship models for subcooled liquid vapor pressures ( P L ) of polybrominated diphenyl ether (PBDE) congeners were developed and can be used for estimating P L of other PBDE congeners.
Abstract: Based on quantum chemical descriptors, by the use of partial least squares regression, quantitative structure-property relationship models for subcooled liquid vapor pressures ( P L ) of polybrominated diphenyl ether (PBDE) congeners were developed. The Q cum 2 value of the optimal model obtained is as high as 0.993, indicating a good predictive ability and robustness of the model. Although disagreements were observed between the predicted log P L values and log P L values of validation set, the model obtained can still be used for estimating P L of other PBDE congeners, considering the fact that accurate P L values for compounds with low volatility are extremely difficult to determine experimentally. Intermolecular dispersive interactions play a leading role in governing the values of P L , followed by electrostatic, dipole-dipole and dipole-induced dipole interactions. Intermolecular dispersive interactions also govern the values of enthalpies of vaporization.
41 citations
TL;DR: New specific measures (vertex accessibility, accessible connectedness, and adjusted average distance) are introduced based on assessment of the reduced accessibility of nodes in directed networks.
Abstract: Recently, there was an increased interest towards network approach to biology and environmental sciences. Networks are believed to be the key to the understanding of the work of biological machine in cells, organs, organisms, and ecosystems. While complexity of undirected networks has been recently analyzed, the assessment of complexity in directed networks has specificity that has not been explored so far. The present paper aims to address the existing gap by discussing the applicability of the available complexity descriptors. New specific measures (vertex accessibility, accessible connectedness, and adjusted average distance) are introduced based on assessment of the reduced accessibility of nodes in directed networks.
TL;DR: The system allows various molecular modeling and molecular processing tasks, including the calculation of molecular and substituent properties, property-based virtual screening, visualization of molecules, bioisosteric design, diversity analysis, and support of combinatorial chemistry.
Abstract: Web-based tools offer many advantages for processing chemical information, most notably ease of use and high interactivity. Therefore more and more pharmaceutical companies are using web technology to deliver sophisticated molecular processing tools directly to the desks of their chemists, to assist them in the process of designing and developing new drugs. In this paper, the web-based cheminformatics system developed at Novartis and currently used by more than thousand users is described. The system allows various molecular modeling and molecular processing tasks, including the calculation of molecular and substituent properties, property-based virtual screening, visualization of molecules, bioisosteric design, diversity analysis, and support of combinatorial chemistry. The methodology to calculate various molecular properties relevant to drug design is described, including the prediction of intestinal absorption, blood–brain barrier penetration, efflux, and water solubility. Information about the web te...
TL;DR: A model was developed that was capable of making a prediction regardless the mechanism of toxic action, using Partial Least Squares analysis for the prediction of the acute toxicity of aliphatic chemicals to the ciliate Tetrahymena pyriformis.
Abstract: The aim of this study was to evaluate a multivariate statistical model, utilising Partial Least Squares (PLS) analysis, for the prediction of the acute toxicity of aliphatic chemicals to the ciliate Tetrahymena pyriformis. A model was developed that was capable of making a prediction regardless the mechanism of toxic action. The toxicity of 476 compounds, possessing different mechanisms of toxic action was considered. A set of 74 descriptors, including the octanol-water partition coefficient, molecular-orbital descriptors, geometrical, topological and connectivity indices, was generated. A three-component, eight-descriptor PLS model was developed. It was validated by a Y-permutation test and by simulation of external prediction for complementary subsets. A comparison with existing class or mechanism-based models, derived on the same data set, was made.
TL;DR: The mutagenicity data for various aromatic and heteroaromatic amines, a data set extensively studied by other quantitative structure–activity relationship (QSAR)-authors, have been modeled by a wide set of theoretical molecular descriptors using linear multivariate regression and genetic algorithm–variable subset selection.
Abstract: In the present research the mutagenicity data (Ames tests TA98 and TA100) for various aromatic and heteroaromatic amines, a data set extensively studied by other quantitative structure–activity relationship (QSAR)-authors, have been modeled by a wide set of theoretical molecular descriptors using linear multivariate regression (MLR) and genetic algorithm–variable subset selection (GA–VSS). The models have been calculated on a subset of compounds selected by a D-optimal experimental design. Moreover, they have been validated by both internal and external validation procedures showing satisfactory predictive performance. The models proposed here can be useful in predicting data and setting a testing priority for those compounds for which experimental data are not available or are not yet synthesized.
TL;DR: B batch screening of 2484 HPV chemicals to predict their mutagenicity in Salmonella typhimurium (Ames test) offers hope that rapid and inexpensive computational methods can aid in prioritizing the testing of HPV chemicals, save time and animals and help to avoid needless expense.
Abstract: Computational screening is suggested as a way to set priorities for further testing of high production volume (HPV) chemicals for mutagenicity and other toxic endpoints. Results are presented for batch screening of 2484 HPV chemicals to predict their mutagenicity in Salmonella typhimurium (Ames test). The chemicals were tested against 15 databases for Salmonella strains TA100, TA1535, TA1537, TA97 and TA98, both with metabolic activation (using rat liver and hamster liver S9 mix test) and without metabolic activation. Of the 2484 chemicals, 1868 are predicted to be completely nonmutagenic in all of the 15 data modules and 39 chemicals were found to contain structural fragments outside the knowledge of the expert system and therefore suggested for further evaluation. The remaining 616 chemicals were found to contain different biophores (structural alerts) believed to be linked to mutagenicity. The chemicals were ranked in descending order according to their predicted mutagenic potential and the first 100 c...
TL;DR: Quality of the validation statistics supports the claim that the QSAR model may be used for estimation of pLC 50 values for similar molecules, and detailed structure interpretation is given for the descriptors in the model.
Abstract: Topological structure methods are used to model fish toxicity against three classes of organic chemicals The models were obtained independent of 3D structure information Further, no mechanism of partitioning was assumed, thus avoiding the problems associated with selection of partitioning system for computation of log P QSAR models were developed for a set of 92 compounds, including phenols, anilines and substituted aromatic hydrocarbons, yielding excellent statistics: r 2 =087, s =025 and q 2 =085 leave-one-out (LOO), that are better than those reported in the literature The model is based on molecular connectivity valence chi-1 index [ 1 h v ], the atom type E-State indices for chlorine [ S T (-Cl)] and for ether oxygen [ S T (-O-)], and the maximum hydrogen E-State atom value in a molecule [H max ] Each of the subgroups was also separately well modeled The model for the full set is validated through use of external validation test sets and ten-fold cross-validation (repeated three times) The
TL;DR: The decision tree developmental SAR models exhibited modest prediction accuracy and bagging tended to enhance the accuracy of prediction, and the model ensemble approach reduced the variability of prediction measures compared to the single model approach.
Abstract: Humans are exposed to thousands of environmental chemicals for which no developmental toxicity information is available. Structure-activity relationships (SARs) are models that could be used to efficiently predict the biological activity of potential developmental toxicants. However, at this time, no adequate SAR models of developmental toxicity are available for risk assessment. In the present study, a new developmental database was compiled by combining toxicity information from the Teratogen Information System (TERIS) and the Food and Drug Administration (FDA) guidelines. We implemented a decision tree modeling procedure, using Classification and Regression Tree software and a model ensemble approach termed bagging. We then assessed the empirical distributions of the prediction accuracy measures of the single and ensemble-based models, achieved by repeating our modeling experiment many times by repeated random partitioning of the working database. The decision tree developmental SAR models exhibited mo...
TL;DR: The design of relatively simple fingerprints for the identification of molecules having similar biological activity and recognition of remote similarity relationships is investigated, and systematic evaluation of fingerprint performance in VS test calculations demonstrates that these new prototypes perform better than previously generated MFPs.
Abstract: Binary fingerprint representations of molecular structure and properties are convenient computational tools for similarity searching in compound databases and virtual screening (VS). We are investigating the design of relatively simple fingerprints for the identification of molecules having similar biological activity and recognition of remote similarity relationships. Since our designs are considerably shorter than other fingerprints used in VS, we have previously termed them "mini-fingerprints" (MFPs). A key aspect of the design strategy is the identification of suitable molecular descriptors. Whereas our initial fingerprint designs have relied on descriptor combinations that performed well in compound classification according to biological activity, second generation MFPs encode combinations of descriptors with high information content in large compound databases and high frequency of occurrence in drug-like molecules. Thus, the design of these new fingerprints does not depend on the analysis of specif...
TL;DR: Applications of some of the approaches being taken to address quantitative structure–activity relationships needs are demonstrated for the prediction of chemical mutagenicity, where considerations of both molecular flexibility and metabolic activation improved the QSAR predictability and interpretations.
Abstract: Major scientific hurdles in the acceptance of quantitative structure–activity relationships (QSAR) for regulatory purposes have been identified. First, when quantifying important features of chemical structure complexities of molecular structure have often been ignored. More mechanistic modelling of chemical structure should proceed on two fronts: by developing a more in-depth understanding and representation of the multiple states possible for a single chemical by achieving greater rigor in understanding of conformational flexibility of chemicals; and, by considering families of activated metabolites that are derived in biological systems from an initial chemical substrate. Second, QSAR research is severely limited by the lack of systematic databases for important risk assessment endpoints, and despite many decades of research, the ability to cluster reactive chemicals by common toxicity pathways is in its infancy. Finally, computational tools are lacking for defining where a specific QSAR is applicable ...
TL;DR: A taxonomy of environmental models is presented in which it is suggested that rather than develop a single comprehensive model, the aim should be to establish a set of coordinated and consistent models treating evaluative and real environmental systems at a variety of scales from local to global and including food web models, organism-specific models and human exposure and pharmacokinetic models.
Abstract: A general review is presented of the roles of QSARs and mass balance models as tools for assessing the environmental fate and effects of chemicals of commerce It is argued that all such chemicals must be assessed using a consistent and transparent methodology that uses chemical property data derived from QSARs, or experimental determinations when possible and applies evaluative or region-specific environmental models These data and models enable an assessment to be made of the key chemical features of persistence, bioaccumulation, potential for long-range transport and toxicity The other key feature is quantity used or discharged to the environment A taxonomy of environmental models is presented in which it is suggested that rather than develop a single comprehensive model, the aim should be to establish a set of coordinated and consistent models treating evaluative and real environmental systems at a variety of scales from local to global and including food web models, organism-specific models and human exposure and pharmacokinetic models The concentrations derived from these models can then be compared with levels judged to be of toxic significance A brief account is given of perceived QSAR needs in terms of partitioning, reactivity, transport and toxicity data to support these models
TL;DR: It is demonstrated that, unlike the lowest unoccupied molecular orbital energy, E LUMO , which was previously used as a descriptor, the electron affinity can be systematically improved by application of higher levels of theory, and the reciprocal of E LumO, which is more consistent with frontier molecular orbital (FMO) theory, improves the correlations with in vitro toxicity data.
Abstract: In order to improve Quantitative Structure-Activity Relationships (QSARs) for halogenated aliphatics (HA) and to better understand the biophysical mechanism of toxic response to these ubiquitous chemicals, we employ improved quantum-mechanical descriptors to account for HA electrophilicity. We demonstrate that, unlike the lowest unoccupied molecular orbital energy, E LUMO , which was previously used as a descriptor, the electron affinity can be systematically improved by application of higher levels of theory. We also show that employing the reciprocal of E LUMO , which is more consistent with frontier molecular orbital (FMO) theory, improves the correlations with in vitro toxicity data. We offer explanations based on FMO theory for a result from our previous work, in which the LUMO energies of HA anions correlated surprisingly well with in vitro toxicity data. Additional descriptors are also suggested and interpreted in terms of the accepted biophysical mechanism of toxic response to HAs and new QSARs ar...
TL;DR: Three 3D-QSAR models including two partial correlation models (one for each family of HEPT and TIBO) and a mixed model gathering two families were constructed showed better prediction ability, which could help to insight the interaction between NNRTIs and HIVRT, and to design new anti-HIV NN RTIs inhibitors.
Abstract: The intermolecular interaction between two types of non nucleoside reverse transcriptase inhibitors (NNRTIs), HEPT and TIBO, and HIV reverse transcriptase receptor (HIVRT) was investigated. The result of docking study showed that two types of NNRTIs presented similar interaction mechanism with HIVRT. The most active compound of every type of inhibitors could form one hydrogen bond with the residue Lys101 and has hydrophobic interaction with residues Tyr181, Tyr188 and Tyr318, etc. Three 3D-QSAR models including two partial correlation models (one for each family of HEPT and TIBO) and a mixed model gathering two families were constructed. Comparative study of these models indicated that the mixed model offered the strongest prediction ability. For this model, the cross-validated q2 values were 0.720 and 0.675, non-cross-validated r2 values were 0.940 and 0.920 for CoMFA and CoMSIA, respectively. It has been validated by using a test set of 27 inhibitors. Compared with previously reported works, our model showed better prediction ability. It could help us to insight the interaction between NNRTIs and HIVRT, and to design new anti-HIV NNRTIs inhibitors.
TL;DR: A quantitative structure–activity relationship (QSAR) for P-gp-associated ATPase activity for a diverse set of 22 drugs is developed, and it is found that such activity is related to substrate molecular size and polarity.
Abstract: Multidrug resistance is brought about largely by membrane transport proteins such as P-glycoprotein (P-gp). We have developed a quantitative structure–activity relationship (QSAR) for P-gp-associated ATPase activity for a diverse set of 22 drugs, and found that such activity is related to substrate molecular size and polarity. We have also developed a QSAR for drug efflux from the blood–brain barrier of another diverse set of 22 drugs, and found that such efflux is a function of drug size and polarisability. Thirdly, we have carried out a QSAR analysis of the ability of 157 phenothiazines and related drugs to reverse multidrug resistance. We were unable to obtain a good QSAR for the whole data-set, but when we divided the data-set into sub-sets of closely related structures, a series of good correlations was obtained, most of which incorporated descriptors that model molecular size and polarity/polarisability. In no instance did we find any evidence that hydrogen bonding or hydrophobicity play a part in m...
TL;DR: QSAR studies showed that polarizability, polarity and presence of five-membered rings in molecules have a positive influence on local anesthetic activity, while contributions of aromatic CH and singly bonded nitrogen are negative.
Abstract: On the basis of computer prediction of biological activity by PASS and toxicity by DEREK, the most prospective 18 alkylaminoacyl derivatives of 3-amino-benzo-[d]-isothiazole were selected. Their local anesthetic action was assessed using an in vitro preparation of the isolated peroneal nerve of the frog. The local anesthetics action of the compounds was assessed according to the time required for each compound to reduce the amplitude of the evoked compound action potential (CAP). Lidocaine was used as the control compound. The results show that the tested compounds can be divided into three groups: (a) compounds with action similar to lidocaine, (b) compounds with action lower than lidocaine and (c) compounds which block completely the evoked CAP, but after the compound was removed and replaced with normal saline showed no recovery of the potential at all. QSAR studies showed that polarizability, polarity and presence of five-membered rings in molecules have a positive influence on local anesthetic activi...
TL;DR: A three-dimentional quantitative structure–activity relationship (3D-QSAR) model for the acute toxicity log EC50 of 56 phenylsulfonyl carboxylates on Vibrio fischeri indicates the significance of the correlation of the steric and electrostatic fields with the biological activities.
Abstract: From the Comparative Molecular Field Analysis (CoMFA) method, the paper describes a three-dimentional quantitative structure-activity relationship (3D-QSAR) model for the acute toxicity logEC50 (15min-EC50 in micromol l(-1)) of 56 phenylsulfonyl carboxylates on Vibrio fischeri. The achievement of a high leave-one-out (LOO) cross-validated correlation coefficient q2 of 0.790 with four optimum components indicates the significance of the correlation of the steric and electrostatic fields with the biological activities. The key features in the CoMFA contour maps are critical to trace the important properties and gain insight into the toxic mechanism of the tested phenylsulfonyl carboxylates.
TL;DR: In this occasion, an attempt was made to highlight the particularities of the journal since its launch in 1993 by means of 21 selected descriptive parameters such as geographical origin, type of endpoint, molecular descriptor, statistical technique and so on.
Abstract: This year marks the 10th anniversary of SAR and QSAR in Environmental Research . In this occasion, an attempt was made to highlight the particularities of the journal since its launch in 1993. The 306 papers already published were encoded by means of 21 selected descriptive parameters such as geographical origin, type of endpoint, molecular descriptor, statistical technique and so on. A linear multivariate analysis was used to graphically analyze the obtained data matrix. Specific domains of research to continue to favor in the journal for the next decade were also underlined.
TL;DR: An efficient virtual and rational drug design method that combines virtual bioactive compound generation with 3D-QSAR model and docking and showed that the generated structures could form more stable complexes with receptor than the reference compound selected from experimental data.
Abstract: An efficient virtual and rational drug design method is presented It combines virtual bioactive compound generation with 3D-QSAR model and docking using this method, it is possible to generate a lot of highly diverse molecules and find vitrual active lead compounds. The method was validated by the study of a set of anti-tumor drugs. With the constraints of pharmacophore obtanined by DISCO implemented in SYBYL 6.8,97 virtual bioactive compounds were generated, and their anti-tumor activities were predicted by CoMFA. Eight structures with high activity were selected and screened by the 3D-QSAR model. The most active generated structure was further investigated by modifying its structure in order to increase the activity. A comparative docking study with telomeric receptor was carried out, and the results showed that the generated structures could form more stable complexes with receptor than the reference compound selected from experimental data. This investigation showed that the proposed method was feasible way for rational drug design with high screening effciency.
TL;DR: The toxicity data for a series of 83 benzene derivatives to the autotrophic Chlorella vulgaris were subjected to HQSAR analysis and this resulted in a model with a high predictive ability, which was validated by “leave-one-out” (LOO) cross-validation procedure and an external testing set.
Abstract: Holographic quantitative structure-activity relationship (HQSAR) is an emerging QSAR technique with the combined application of molecular hologram, which encodes the frequency of occurrence of various molecular fragment types, and the subsequent partial least squares (PLS) regression analysis. Based on molecular hologram, alignment-free QSAR models could be rapidly and easily developed with highly statistical significance and predictive ability. In this paper, the toxicity data for a series of 83 benzene derivatives to the autotrophic Chlorella vulgaris (IGC50, negative logarithmic form of 6-h 50% population growth inhibition concentration in mmol/l) were subjected to HQSAR analysis and this resulted in a model with a high predictive ability. The robustness and predictive ability of the model were validated by "leave-one-out" (LOO) cross-validation procedure and an external testing set. The influence of fragment distinction parameters and fragment size on the quality of the HQSAR model have been also discussed.
TL;DR: Freely available resources from the Internet for academic use, which can be easily computed and visualized via the Internet, are reviewed.
Abstract: Due to recent computer technology advances, shape analysis has gained importance in all domains. In drug design and proteomics, molecular surfaces (van der Waals surface, solvent accessible surface, solvent excluded surface, polar surface area, electron density surface, separating surface, etc.), buried surfaces (gap, cleft, cavity, etc.) as well as shape properties of these surfaces, can be easily computed and visualized via the Internet. Freely available resources from the Internet for academic use, are reviewed.
TL;DR: It could be deduced that the adsorption of halogenated aromatics on yellow-brown soil was not a simple partitioning process but involved complicated interactions.
Abstract: Halogenated aromatic compounds exist widely in soil and aqueous environment. The study of their transport and distribution is quite important for pollution control and risk assessment. In the present work, the adsorption coefficients of 28 halogenated benzenes, anilines and phenols on yellow-brown soil were measured with batch equilibrium method, and a prediction model was developed through the quantitative structure–property relationship (QSPR) technique. Then the obtained model was tested with Monte Carlo simulation and Jacknife methods. The results indicated that it was robust enough to estimate soil adsorption behaviors for the tested compounds. Based on the obtained model, it could be deduced that the adsorption of halogenated aromatics on yellow-brown soil was not a simple partitioning process but involved complicated interactions.