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Automatically identifying chemical and drug names in scientific publications advances information access for this important class of entities in a variety of biomedical disciplines by enabling improved retrieval and linkage to related concepts. While current methods for tagging chemical entities were developed for the article title and abstract, their performance in the full article text is substantially lower. However, the full text frequently contains more detailed chemical information, such as the properties of chemical compounds, their biological effects and interactions with diseases, genes and other chemicals. We therefore present the NLM-Chem corpus, a full-text resource to support the development and evaluation of automated chemical entity taggers. The NLM-Chem corpus consists of 150 full-text articles, doubly annotated by ten expert NLM indexers, with ~5000 unique chemical name annotations, mapped to ~2000 MeSH identifiers. We also describe a substantially improved chemical entity tagger, with automated annotations for all of PubMed and PMC freely accessible through the PubTator web-based interface and API. The NLM-Chem corpus is freely available.

A valuable approach to chemical safety assessment is the use of read-across chemicals to provide safety data to support the assessment of structurally similar chemicals. An inventory of over 6000 discrete organic chemicals used as fragrance materials in consumer products has been clustered into chemical class-based groups for efficient search of read-across sources. We developed a robust, tiered system for chemical classification based on (1) organic functional group, (2) structural similarity and reactivity features of the hydrocarbon skeletons, (3) predicted or experimentally verified Phase I and Phase II metabolism, and (4) expert pruning to consider these variables in the context of specific toxicity end points. The systematic combination of these data yielded clusters, which may be visualized as a top-down hierarchical clustering tree. In this tree, chemical classes are formed at the highest level according to organic functional groups. Each subsequent subcluster stemming from classes in this hierarchy of the cluster is a chemical cluster defined by common organic functional groups and close similarity in the hydrocarbon skeleton. By examining the available experimental data for a toxicological endpoint within each cluster, users can better identify potential read-across chemicals to support safety assessments.

The European medicinal leech has been used for medicinal purposes for millennia, and continues to be used today in modern hospital settings. Its utility is granted by the extremely potent anticoagulation factors that the leech secretes into the incision wound during feeding and, although a handful of studies have targeted certain anticoagulants, the full range of anticoagulation factors expressed by this species remains unknown. Here, we present the first draft genome of the European medicinal leech, Hirudo medicinalis, and estimate that we have sequenced between 79-94% of the full genome. Leveraging these data, we searched for anticoagulation factors across the genome of H. medicinalis. Following orthology determination through a series of BLAST searches, as well as phylogenetic analyses, we estimate that fully 15 different known anticoagulation factors are utilized by the species, and that 17 other proteins that have been linked to antihemostasis are also present in the genome. We underscore the utility of the draft genome for comparative studies of leeches and discuss our results in an evolutionary context.

Kendrick mass defect (KMD) analysis is widely used for helping the detection and identification of chemically related compounds based on exact mass measurements. We report here the use of KMD as a criterion for filtering complex mass spectrometry data set. The method allow automated, easy and efficient data processing, enabling the reconstruction of 2D distributions of families of homologous compounds from MSI images. We show that KMD filtering, based on in-house software, is suitable and robust for high resolution (full width at half-maximum, fwhm, at m / z 410 of 20 000) and very high-resolution (fwhm, at m / z 410 of 160 000) MSI data. This method has been successfully applied to two different types of samples, bacteria cocultures, and brain tissue sections.

The Amino acid Derivative Reactivity Assay (ADRA) is an in chemico alternative to animal testing for the prediction of skin sensitization potential. Although co-elution of test chemicals and nucleophilic reagents during HPLC analysis is sometimes problematic when using the Direct Peptide Reactivity Assay (DPRA), it rarely occurs when using ADRA. Nevertheless, the application of either of these tests to multi-constituent substances requires nucleophilic reagents capable of selective detection. With this issue in mind, the authors developed an ADRA fluorescence detection method (ADRA-FL), which utilizes the natural fluorescence of ADRA nucleophilic reagents. In this study, we demonstrate the efficacy of ADRA-FL by testing 82 test chemicals used in the development of both DPRA and the conventional ADRA (ADRA-UV) as well as establish a threshold value for distinguishing sensitizers and non-sensitizers. Our results show that not only are depletion values obtained using ADRA-FL virtually identical to those obtained using ADRA-UV, the threshold value for either test is 4.9%. Additionally, in order to demonstrate the applicability of ADRA-FL to multi-constituent substances, we prepared test samples that consisted of a set of 10 non-sensitizers combined with one of 10 different sensitizers and tested each using ADRA-FL. The test results were concordant with those obtained using ADRA-UV. Also, because ADRA-FL chromatograms showed a significant decrease in multiple peaks as well as extremely stable baselines, we conclude that ADRA-FL is a highly selective and highly accurate mans of quantifying nucleophilic reagents that is applicable to a wide variety of chemical substances.

Considering the classification of compounds as a nonlinear problem, the use of kernel methods is a good choice. Graph kernels provide a nice framework combining machine learning methods with graph theory, whereas the essence of graph kernels is to compare the substructures of two graphs, how to extract the substructures is a question. In this paper, we propose a novel graph kernel based on matrix named the local block kernel, which can compare the similarity of partial substructures that contain any number of vertexes. The paper finally tests the efficacy of this novel graph kernel in comparison with a number of published mainstream methods and results with two datasets: NCI1 and NCI109 for the convenience of comparison.

Yarrow (Achillea millefolium L., Asteraceae) is an important medicinal plant used worldwide for its medicinal properties such as the analgesic, antioxidant and anti-inflammatory ones. The aim of this study was to evaluate the growth and production of photosynthetic pigments and of volatile constituents of Achillea millefolium L. under different irrigation depths. The treatments were the application of 55, 110, 220, 440 and 880 mm of water for a period of 110 days. Data were submitted to polynomial regression analysis at 5% probability, while the volatile constituents were analyzed by standard deviation. Different irrigation depths provided quadratic growth responses being the highest dry matter production at the depth of 440 mm. The contents of chlorophyll a, b, total and carotenoids were higher at the lower depth tested (55 mm). The major volatile compounds identified were sabinene, 1,8-cineol, borneol and β-caryophyllene. Increased water availability reduced the complexity of the volatile fraction of essential oil. Thus, it is recommended that the species be cultivated at 440 mm irrigation depth to have a higher production of dry matter and lower variation in the volatile profile of the essential oil.

Frameworks for chemical regulation are based on the science at the time they were written. Today some regulations are being applied to a much broader spectrum of chemicals than we had knowledge of when the regulations were written. This entails a risk that the regulations are being used outside of their chemical application domain. This question is explored using the POP screening assessment in the Stockholm convention, which was developed 20 years ago. Using perfluorinated alkyl acids (PFAAs) as an example, it is shown that the assessment can lead to false negative conclusions. A second case study using octamethylcyclotetrasiloxane (D4) illustrates that there is also a risk of false positives. The risk for false negative classification of PFAAs is due to the inclusion of a screening criterion - bioaccumulation - that is not a requirement for adverse effects of chemicals in remote regions. For D4 the risk of false positive classification stems from the four screening criteria (persistence, bioaccumulation, long-range transport, and adverse effects) applying to different environmental media/compartments. The major lesson is that applying the POP screening procedure to the broad spectrum of chemicals in modern commerce will require that we rely less on the individual screening criteria and more on the comparison of estimated exposure and the thresholds for effects stipulated in Annex D, paragraph 2 of the convention. Models have an important role to play in this context and should become more strongly integrated into the POP screening process.

The hazard of chemicals in the environment is inherently related to the molecular structure and derives simultaneously from various chemical properties/activities/reactivities. Models based on Quantitative Structure Activity Relationships (QSARs) are useful to screen, rank and prioritize chemicals that may have an adverse impact on humans and the environment. This paper reviews a selection of QSAR models (based on theoretical molecular descriptors) developed for cumulative multivariate endpoints, which were derived by mathematical combination of multiple effects and properties. The cumulative end-points provide an integrated holistic point of view to address environmentally relevant properties of chemicals.

Relationships of toxicities from intravenous (i.v.), intraperitoneal (i.p.), subcutaneous (s.c.) and intragastric (i.g.) exposure routes to mice were investigated. Regression analysis showed that the toxicities from i.v. route is strongly correlated with i.p. and s.c. routes, but poorly with i.g. route. Close toxicities from different routes for some compounds indicate that distribution rate is the determining step and dictates chemical concentration at the target site(s). On the other hand, the absorption rate is the determining step for many compounds, which lead to different toxicities between exposure routes. The classified compounds characterized as having either absorption or distribution rate determining step were based upon the comparison of toxicities from the different routes. We found that some aliphatic acids and benzoic acids have lower toxicity values from i.g. route compared to an i.v. route because of poor absorption. Many compounds show low toxic effects from i.g. route than those from other routes because of the first-pass metabolism in the gastrointestinal tract, resulting in the poor relationship for toxicities between i.g. and i.v. or other routes. Stepwise regression analysis showed that physicochemical properties of a compound, such as molecular volume, polarizability and hydrophobicity, significantly affect adsorption rate, which leads to different toxicities based upon exposure routes. Comparison of the toxicities between mice and rats indicate that toxic effect and the toxicokinetic processes in mice are very similar to that in rats. A universal correlation equation has been developed for the toxicities between rats and mice from different exposure routes, which can be applied to predict toxicities across species.

Many substances in workplace do not have occupational exposure limits. The threshold of toxicological concern (TTC) principle is part of the hierarchy of approaches useful in occupational health risk assessment. The aim of this study was to derive occupational TTCs (OTTCs) reflecting the airborne concentrations below which no significant risk to workers would be anticipated. A reference dataset consisting of the 8-h threshold limit values-Time-Weighted Average for 280 organic substances was compiled. Each substance was classified into low (class I), intermediate (class II), or high (class III) hazard categories as per Cramer rules. For each chemical, n-octanol:water partition coefficient and vapor pressure along with the molecular weight were used to predict the blood:air partition coefficient. The blood:air partition coefficient along with data on water solubility and ventilation rate allowed the prediction of pulmonary retention factor and absorbed dose in workers. For each Cramer class, the distribution of the predicted doses was analyzed to identify the various percentile values corresponding to the OTTC. Accordingly, for Cramer classes I-III, the OTTCs derived in this study correspond to 0.15, 0.0085, and 0.006 mmol/d, respectively, at the 10th percentile level, while these values were 1.5, 0.09 and 0.03 mmol/d at the 25th percentile level. The proposed OTTCs are not meant to replace the traditional occupational exposure limits, but can be used in data-poor situations along with exposure estimates to support screening level risk assessment and prioritization., (© The Author 2017. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

The contaminants that have the greatest chances of appearing in drinking water are those that are mobile enough in the aquatic environment to enter drinking water sources and persistent enough to survive treatment processes. Herein a screening procedure to rank neutral, ionizable and ionic organic compounds for being persistent and mobile organic compounds (PMOCs) is presented and applied to the list of industrial substances registered under the EU REACH legislation as of December 2014. This comprised 5155 identifiable, unique organic structures. The minimum cut-off criteria considered for PMOC classification herein are a freshwater half-life >40 days, which is consistent with the REACH definition of freshwater persistency, and a log D oc < 4.5 between pH 4-10 (where D oc is the organic carbon-water distribution coefficient). Experimental data were given the highest priority, followed by data from an array of available quantitative structure-activity relationships (QSARs), and as a third resort, an original Iterative Fragment Selection (IFS) QSAR. In total, 52% of the unique REACH structures made the minimum criteria to be considered a PMOC, and 21% achieved the highest PMOC ranking (half-life > 40 days, log D oc < 1.0 between pH 4-10). Only 9% of neutral substances received the highest PMOC ranking, compared to 30% of ionizable compounds and 44% of ionic compounds. Predicted hydrolysis products for all REACH parents (contributing 5043 additional structures) were found to have higher PMOC rankings than their parents, due to increased mobility but not persistence. The fewest experimental data available were for ionic compounds; therefore, their ranking is more uncertain than neutral and ionizable compounds. The most sensitive parameter for the PMOC ranking was freshwater persistency, which was also the parameter that QSARs performed the most poorly at predicting. Several prioritized drinking water contaminants in the EU and USA, and other contaminants of concern, were identified as PMOCs. This identification and ranking procedure for PMOCs can be part of a strategy to better identify contaminants that pose a threat to drinking water sources.

For many years there has been growing demand for gaseous reference materials, which is connected with development in many fields of science and technology. As a result, new methodological and instrumental solutions appear that can be used for this purpose. Appropriate quality assurance/quality control (QA/QC) must be used to make sure that measurement data are a reliable source of information. Reference materials are a significant element of such systems. In the case of gas samples, such materials are generally called reference gas mixtures. This article presents the application and classification of reference gas mixtures, which are a specific type of reference materials, and the methods for obtaining them are described. Construction solutions of devices for the production of reference gas mixtures are detailed, and a description of a prototype device for dynamic production of reference gas mixtures containing aroma compounds is presented.

The present publication describes an integrative grouping concept to derive threshold values for inhalation exposure. The classification scheme starts with differences in toxicological potency and develops criteria to group compounds into two potency classes, namely toxic (T-group) or low toxic (L-group). The TTC concept for inhalation exposure is based on the TTC RepDose data set, consisting of 296 organic compounds with 608 repeated-dose inhalation studies. Initially, 21 structural features (SFs) were identified as being characteristic for compounds of either high or low NOEC values (Schüürmann et al., 2016). In subsequent analyses these SF groups were further refined by taking into account structural homogeneity, type of toxicological effect observed, differences in absorption, metabolism and mechanism of action (MoA), to better define their structural and toxicological boundaries. Differentiation of a local or systemic mode of action did not improve the classification scheme. Finally, 28 groups were discriminated: 19 T-groups and 9 L-groups. Clearly distinct thresholds were derived for the T- and L-toxicity groups, being 2 × 10(-5) ppm (2 μg/person/day) and 0.05 ppm (4260 μg/person/day), respectively. The derived thresholds and the classification are compared to the initial mainly structure driven grouping (Schüürmann et al., 2016) and to the Cramer classification., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Chemical flocculants are generally used in drinking water and wastewater treatment due to their efficacy and cost effectiveness. However, the question of their toxicity to human health and environmental pollution has been a major concern. In this article, we review the application of some chemical flocculants utilized in water treatment, and bioflocculants as a potential alternative to these chemical flocculants. To the best of our knowledge, there is no report in the literature that provides an up-to-date review of the relevant literature on both chemical flocculants and bioflocculants in one paper. As a result, this review paper comprehensively discussed the various chemical flocculants used in water treatment, including their advantages and disadvantages. It also gave insights into bioflocculants production, challenges, various factors influencing their flocculating efficiency and their industrial applications, as well as future research directions including improvement of bioflocculants yields and flocculating activity, and production of cation-independent bioflocculants. The molecular biology and synthesis of bioflocculants are also discussed., (© 2016 The Authors. MicrobiologyOpen published by John Wiley & Sons Ltd.)

A large number of organic pollutants that cause damage to the ecosystem and threaten human health are transported to wastewater treatment plants (WWTPs). The problems regarding water pollution in Latin America have been well documented, and there is no evidence of substantive efforts to change the situation. In the present work, two methods to study wastewater samples are employed: non-targeted 1D ((13)C and (1)H) and 2D NMR spectroscopic analysis to characterize the largest possible number of compounds from urban wastewater and analysis by HPLC-(UV/MS)-SPE-ASS-NMR to detect non-specific recalcitrant organic compounds in treated wastewater without the use of common standards. The set of data is composed of several compounds with the concentration ranging considerably with treatment and seasonality. An anomalous discharge, the influence of stormwater on the wastewater composition and the presence of recalcitrant compounds (linear alkylbenzene sulfonate surfactant homologs) in the effluent were further identified. The seasonal variations and abnormality in the composition of organic compounds in sewage indicated that the procedure that was employed can be useful in the identification of the pollution source and to enhance the effectiveness of WWTPs in designing preventive action to protect the equipment and preserve the environment., (Copyright © 2014 John Wiley & Sons, Ltd.)

The toxicity data of 2624 chemicals to fish, Daphniamagna, Tetrahymenapyriformis and Vibriofischeri were used to investigate the effects of species sensitivity and bioconcentration on excess toxicity. The results showed that 47 chemical classes were identified as having the same modes of action (MOAs) to all four species, but more than half of the classes were identified as having different MOAs. Difference in chemical MOAs is one of the reasons resulting in the difference in toxic effect to these four species. Other important reasons are the difference in sensitivity and bioconcentration of species. Among the four species, V. fischeri has the most compounds identified as reactive MOA. This may be due to some compounds can be easily absorbed into the bacteria, react with the DNA or proteins, disrupt the normal function of the cell and exhibit significantly greater toxicity to the bacteria. On the other hand, the skin and lipid content of aqueous organisms can strongly inhibit the bio-uptake for some reactive compounds, resulting in a less toxic effect than expected. D. magna is the most sensitive species and T. pyriformis is the least sensitive species of the four species. For a comparison of interspecies toxicity, we need to use the same reference threshold of excess toxicity. However, some reactive compounds may be identified as baseline or less inert compounds for low sensitive species from the threshold developed from high sensitive species. The difference in the discrimination of excess toxicity to different species is not only because of the difference in MOAs for some compounds, but also due to the difference in sensitivity and bioconcentration., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The use of chemicals is continuously growing both in total amount as well as in a number of different substances, among which organic chemicals play a major role. Owing to the growing public awareness on the need of protecting both ecosystems and human health from the risks related to chemical pollution, an increasing attention has been drowned to risk assessment and prioritization of organic pollutants. In this context, the aims of this study were (a) to perform an environmental risk assessment for 200 organic micropollutants including both regulated and emerging contaminants (pesticides, alkylphenols, pharmaceuticals, hormones, personal care products, perflourinated compounds and various industrial organic chemicals) monitored in four rivers located in the Mediterranean side of the Iberian Peninsula, namely, the Ebro, Llobregat, Júcar and Guadalquivir rivers; and (b) to prioritize them for each of the four river basins studied, taking into account their observed concentration levels together with their ecotoxicological potential. For this purpose, a prioritization approach has been developed and a resulting ranking index (RI) associated with each compound. Ranking index is based on the measured concentrations of the chemical in each river and its ecotoxicological potential (EC50 values for algae, Daphnia sp. and fish). Ten compounds were identified as most important for the studied rivers: pesticides chlorpyriphos, chlorfenvinphos, diazinon, dichlofenthion, prochloraz, ethion carbofuran and diuron and the industrial organic chemicals nonylphenol and octylphenol that result from the biodegration of polyethoxylated alkyphenol surfactants. Also, further research into chronic toxicity of emerging contaminants is advocated., (Copyright © 2014 Elsevier B.V. All rights reserved.)

This work aims at evaluating the relevance and versatility of liquid chromatography coupled to high resolution mass spectrometry (LC/HRMS) for performing a qualitative and comprehensive study of the human serum metabolome. To this end, three different chromatographic systems based on a reversed phase (RP), hydrophilic interaction chromatography (HILIC) and a pentafluorophenylpropyl (PFPP) stationary phase were used, with detection in both positive and negative electrospray modes. LC/HRMS platforms were first assessed for their ability to detect, retain and separate 657 metabolite standards representative of the chemical families occurring in biological fluids. More than 75% were efficiently retained in either one LC-condition and less than 5% were exclusively retained by the RP column. These three LC/HRMS systems were then evaluated for their coverage of serum metabolome. The combination of RP, HILIC and PFPP based LC/HRMS methods resulted in the annotation of about 1328 features in the negative ionization mode, and 1358 in the positive ionization mode on the basis of their accurate mass and precise retention time in at least one chromatographic condition. Less than 12% of these annotations were shared by the three LC systems, which highlights their complementarity. HILIC column ensured the greatest metabolome coverage in the negative ionization mode, whereas PFPP column was the most effective in the positive ionization mode. Altogether, 192 annotations were confirmed using our spectral database and 74 others by performing MS/MS experiments. This resulted in the formal or putative identification of 266 metabolites, among which 59 are reported for the first time in human serum., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Following legislation, the assessment of the environmental risks of 30000-100000 chemical substances is required for their registration dossiers. However, their behavior in the environment and their transfer to environmental components such as water or atmosphere are studied for only a very small proportion of the chemical in laboratory tests or monitoring studies because it is time-consuming and/or cost prohibitive. Therefore, the objective of this work was to develop a new methodology, TyPol, to classify organic compounds, and their degradation products, according to both their behavior in the environment and their molecular properties. The strategy relies on partial least squares analysis and hierarchical clustering. The calculation of molecular descriptors is based on an in silico approach, and the environmental endpoints (i.e. environmental parameters) are extracted from several available databases and literature. The classification of 215 organic compounds inputted in TyPol for this proof-of-concept study showed that the combination of some specific molecular descriptors could be related to a particular behavior in the environment. TyPol also provided an analysis of similarities (or dissimilarities) between organic compounds and their degradation products. Among the 24 degradation products that were inputted, 58% were found in the same cluster as their parents. The robustness of the method was tested and shown to be good. TyPol could help to predict the environmental behavior of a "new" compound (parent compound or degradation product) from its affiliation to one cluster, but also to select representative substances from a large data set in order to answer some specific questions regarding their behavior in the environment., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Sediment toxicity identification and evaluation (TIE) methods have been used for 20 yr to identify the causes of toxicity in sediments around the world. In the present study, the authors summarize and categorize results of 36 peer-reviewed TIE studies (67 sediments) into nonionic organic, cationic, ammonia, and "other" toxicant groups. Results are then further categorized according to whether the study was performed in freshwater or marine sediments and whether the study was performed using whole-sediment or interstitial-water TIE methods. When all studies were grouped, nonionic organic toxicants, either singly or in combination with other toxicants, were implicated in 70% of all studies. When studies were divided into interstitial-water TIE methodology compared with whole-sediment TIE methodology, results indicated that studies performed using interstitial-water TIE methods reported nonionic organic toxicity slightly more often than toxicity from cationic metals (67% compared with 49%). In contrast, studies using whole-sediment TIE methods report nonionic organic chemical toxicity, either singly or in combination with another toxicant, in 90% of all sediments tested. Cationic metals play a much smaller role in whole-sediment TIE studies-fewer than 20% of all sediments had a metals signal. The discrepancy between the 2 methods can be attributed to exposure differences. Contrary to earlier findings, ammonia generally plays only a minor role in sediment toxicity., (© 2013 SETAC. This article is a US Government work and is in the public domain in the USA.)

The kinetics of photodegradation of levofloxacin in solution on UV irradiation in the pH range 2.0-12.0 has been studied using a HPLC method. Levofloxacin undergoes first-order kinetics in the initial stages of the reaction and the apparent first-order rate constants are of the order of 0.167 to 1.807×10-3 min-1. The rate-pH profile is represented by a curve indicating the presence of cationic, dipolar and anionic species during the reaction. The singly ionized form of the molecule is non-fluorescent and is less susceptible to photodegradation. The increase in the degradation rate in the pH range 5.0-9.0 may be due to greater reactivity of the ionized species existing in that range. The rate appears to vary with a change in the degree of ionization of the species present in a particular pH range and their susceptibility to photodegradation. Above pH 9, the decrease in the rate of photodegradation may be a result of deprotonation of the piperazinyl group. The levofloxacin molecule is more stable in the pH range around 7, which is then suitable for formulation purposes. The photodegradation of levofloxacin was found to be affected by the dielectric constant and viscosity of the medium.

Knowledge of the environmental behavior of chemicals is a fundamental part of the risk assessment process. The present paper discusses various methods of ranking of a series of persistent organic pollutants (POPs) according to the persistence, bioaccumulation and toxicity (PBT) characteristics. Traditionally ranking has been done as an absolute (total) ranking applying various multicriteria data analysis methods like simple additive ranking (SAR) or various utility functions (UFs) based rankings. An attractive alternative to these ranking methodologies appears to be partial order ranking (POR). The present paper compares different ranking methods like SAR, UF and POR. Significant discrepancies between the rankings are noted and it is concluded that partial order ranking, as a method without any pre-assumptions concerning possible relation between the single parameters, appears as the most attractive ranking methodology. In addition to the initial ranking partial order methodology offers a wide variety of analytical tools to elucidate the interplay between the objects to be ranked and the ranking parameters. In the present study is included an analysis of the relative importance of the single P, B and T parameters., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Repeated dose toxicity (RDT) is one of the most important hazard endpoints in the risk assessment of chemicals. However, due to the complexity of the endpoints associated with whole body assessment, it is difficult to build up a mechanistically transparent structure-activity model. The category approach, based on mechanism information, is considered to be an effective approach for data gap filling for RDT by read-across. Therefore, a library of toxicological categories was developed using experimental RDT data for 500 chemicals and mechanistic knowledge of the effects of these chemicals on different organs. As a result, 33 categories were defined for 14 types of toxicity, such as hepatotoxicity, hemolytic anemia, etc. This category library was then incorporated in the Hazard Evaluation Support System (HESS) integrated computational platform to provide mechanistically reasonable predictions of RDT values for untested chemicals. This article describes the establishment of a category library and the associated HESS functions used to facilitate the mechanistically reasonable grouping of chemicals and their subsequent read-across.

Metabolomics represents an emerging issue that can aid in the diagnosis and/or prognosis of different diseases. Metabolomic study of urine is particularly interesting as it can be on the base of the developing of new faster and non-invasive methodologies. In response to this actual trend, comprehensive two-dimensional gas chromatography-time of flight mass spectrometry (GC×GC-ToFMS) combined with headspace solid phase microextraction (HS-SPME) is applied, for the first time to our knowledge, to the untargeted and comprehensive study of the volatile composition of human urine. From a total of ca. 700 compounds detected per sample, 294 were tentatively identified and distributed over the chemical families of hydrocarbons, amines, amides, esters, ketones, aldehydes, alcohols, carboxylic acids, ethers, nitriles, halides, sulfides, thiols, terpenoids, and heterocyclic compounds. To our knowledge, this is the most complete information available so far about whole human urine volatile composition, which represents a valuable data for future advanced studies in the clinical field based on urine fingerprinting. Relevant SPME and GC×GC parameters were considered. Complex sample characterization of human urine is significantly simplified due to the structured GC×GC chromatogram that produces distinct spaces for metabolite chemical families. Furthermore, the potential of this methodology in health related applications was explored by comparing the urinary volatile profiles between smoker (high-risk population for lung cancer) vs. non-smoker adults, focusing on metabolites related to oxidative stress (aliphatic alkanes and aldehydes). In spite of the small sample numbers considered, the results suggest that the urinary volatile profiles may be useful for differentiating subjects with different physiological conditions, thus making it worth to further explore its diagnostic potential., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Bacteria produce unique volatile mixtures that could be used to identify infectious agents to the species, and possibly the strain level. However, due to the immense variety of human pathogens, and the close relatedness of some of these bacteria, the robust identification of the bacterium based on its volatile metabolome is likely to require a large number of volatile compounds for each species. We applied comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS) to the identification of the headspace volatiles of Pseudomonas aeruginosa PA14 grown for 24 h in lysogeny broth. This is the first reported use of GC×GC-TOFMS for the characterization of bacterial headspace volatiles. The analytical purity that is afforded by this chromatographic method facilitated the identification of 28 new P. aeruginosa-derived volatiles, nearly doubling the list of volatiles for this species., (Copyright © 2012 Elsevier B.V. All rights reserved.)

An analytical method for the classification of complex real-world samples was researched and developed with the use of excitation-emission fluorescence matrix (EEFM) spectroscopy, using the medicinal herbs, Rhizoma corydalis decumbentis (RCD) and Rhizoma corydalis (RC) as example samples. The data set was obtained from various authentic RCD-A and RC-A, adulterated AD, and commercial RCD-C and RC-C samples. The spectra (range: λ(ex) = 215∼395 nm and λ(em) = 290∼560 nm), arranged in two- and three-way data matrix formats, were processed using principal component analysis (PCA) and parallel factor analysis (PARAFAC) to produce two-dimensional component-by-component plots for qualitative data classification. The RCD-A and RC-A object groups were clearly discriminated, but the AD and the RCD-C as well as RC-C samples were less well separated. PARAFAC analysis produced somewhat better discrimination, and loadings plots revealed the presence of the marker compound Protopine-a strongly fluorescing substance-as well as at least two other unidentified fluorescent components. Classification performance of the common K-nearest neighbors (KNN) and linear discrimination analysis (LDA) methods was relatively poor when compared with that of the back propagation- and radial basis function-artificial neural networks (BP-ANN and RBF-ANN) models on the basis of two- and three-way formatted data. The best results were obtained with the three-way fingerprints and the RBF-ANN model. Subsequently, the quality of the commercial samples (RCD-C and RC-C) was classified on the best optimized RBF-ANN model. Thus, EEFM spectroscopy, which provides three-way measured data, is potentially a powerful analytical technique for the analysis of complex real-world substances provided the classification is performed by the RBF-ANN or similar ANN methods.

In this study the effect of organic and inorganic nanomaterials (NMs) on the structural diversity of the soil microbial community was investigated by Denaturing Gradient Gel Electrophoresis, after amplification with universal primers for the bacterial region V6-V8 of 16S rDNA. The polymers of carboxylmethyl-cellulose (CMC), of hydrophobically modified CMC (HM-CMC), and hydrophobically modified polyethylglycol (HM-PEG); the vesicles of sodium dodecyl sulphate/didodecyl dimethylammonium bromide (SDS/DDAB) and of monoolein/sodium oleate (Mo/NaO); titanium oxide (TiO(2)), titanium silicon oxide (TiSiO(4)), CdSe/ZnS quantum dots, gold nanorods, and Fe/Co magnetic fluid were the NMs tested. Soil samples were incubated, for a period of 30 days, after being spiked with NM suspensions previously characterized by Dynamic Light Scattering (DLS) or by an ultrahigh-resolution scanning electron microscope (SEM). The analysis of similarities (ANOSIM) of DGGE profiles showed that gold nanorods, TiO(2), CMC, HM-CMC, HM-PEG, and SDS/DDAB have significantly affected the structural diversity of the soil bacterial community., (Copyright © 2012 Elsevier B.V. All rights reserved.)

The present work aimed at evaluating the toxicity and genotoxicity of two organic (vesicles composed of sodium dodecyl sulphate/didodecyl dimethylammonium bromide-SDS/DDAB and of monoolein and sodium oelate-Mo/NaO) and four inorganic (titanium oxide-TiO₂, silicon titanium-TiSiO₄, Lumidot-CdSe/ZnS, and gold nanorods) nanoparticles (NP), suspended in two aqueous media (Milli Q water and American Society for Testing and Materials (ASTM) hardwater), to the bacteria Vibrio fischeri (Microtox test) and Salmonella typhimurium-his⁻ (Ames test with strains TA98 and TA100). Aiming a better understanding of these biological responses physical and chemical characterization of the studied NP suspensions was carried out. Results denoted a high aggregation state of the NP in the aqueous suspensions, with the exception of SDS/DDAB and Mo/NaO vesicles, and of nanogold suspended in Milli Q water. This higher aggregation was consistent with the low values of zeta potential, revealing the instability of the suspensions. Regarding toxicity data, except for nano TiO₂, the tested NP significantly inhibited bioluminescence of V. fischeri. Genotoxic effects were only induced by SDS/DDAB and TiO₂ for the strain TA98. A wide range of toxicity responses was observed for the six tested NP, differing by more than 5 orders of magnitude, and suggesting different modes of action of the tested NP.

Advocates of "green products" argue that promoting these products can protect the environment, workers, and public health. Biobased metalworking fluids (MWFs) are among the products promoted as "green products." The main question is, what constitutes a green product? To answer this question, the authors compared and contrasted the health and safety aspects of biobased and petroleum-based MWFs in terms of their additives. These two product categories of MWFs derived from various feedstocks were investigated through interviews and literature review. Three classes of biobased MWFs and four classes of petroleum-based MWFs were identified and compared. The little information available on the individual constituents for biobased MWFs indicates that they had biocides and preservatives, corrosion inhibitors, extreme pressure, and antiwear components, which are also common additives in petroleum-based MWFs. Precautionary approaches should be taken when promoting biobased MWFs as "green products" until individual components are evaluated for their health and safety impacts.

Providing a confidence measure associated with the substance(s) identified in an unknown mixture by a spectral search technique is critical for non-expert users of devices and techniques based on spectroscopy. In this work, a technique for estimating probabilities associated with substances identified by spectral searching is described. In the proposed approach, a mixture analysis algorithm processes the spectrum of an unknown sample using a spectral library to generate a list of substances that may be present in the sample. The partial correlation of each of the substances in the list is then computed. The estimation of the probability is accomplished through a generalized linear model that converts the partial correlation values to a probability measure for each of the mixture components. The statistical properties of partial correlation allow probability estimation irrespective of whether a substance is present in a pure form or within a mixture. The technique was evaluated using both simulated and real Raman spectra of multi-component mixtures, and adequate performance was demonstrated., (© 2012 Society for Applied Spectroscopy)

Soil organic carbon is of heterogeneity in components. The active components are sensitive to agricultural management, while the inert components play an important role in carbon fixation. Soil organic carbon fractionation mainly includes physical, chemical, and biological fractionations. Physical fractionation is to separate the organic carbon into active and inert components based on the density, particle size, and its spatial distribution; chemical fractionation is to separate the organic carbon into various components based on the solubility, hydrolizability, and chemical reactivity of organic carbon in a variety of extracting agents. In chemical fractionation, the dissolved organic carbon is bio-available, including organic acids, phenols, and carbohydrates, and the acid-hydrolyzed organic carbon can be divided into active and inert organic carbons. Simulated enzymatic oxidation by using KMnO4 can separate organic carbon into active and non-active carbon. Biological fractionation can differentiate microbial biomass carbon and potential mineralizable carbon. Under different farmland management practices, the chemical composition and pool capacity of soil organic carbon fractions will have different variations, giving different effects on soil quality. To identify the qualitative or quantitative relationships between soil organic carbon components and carbon deposition, we should strengthen the standardization study of various fractionation methods, explore the integrated application of different fractionation methods, and sum up the most appropriate organic carbon fractionation method or the appropriate combined fractionation methods for different farmland management practices.

We have estimated degradation half-lives of both brominated and chlorinated dibenzo-p-dioxins (PBDDs and PCDDs), furans (PBDFs and PCDFs), biphenyls (PBBs and PCBs), naphthalenes (PBNs and PCNs), diphenyl ethers (PBDEs and PCDEs) as well as selected unsubstituted polycyclic aromatic hydrocarbons (PAHs) in air, surface water, surface soil, and sediments (in total of 1,431 compounds in four compartments). Next, we compared the persistence between chloro- (relatively well-studied) and bromo- (less studied) analogs. The predictions have been performed based on the quantitative structure-property relationship (QSPR) scheme with use of k-nearest neighbors (kNN) classifier and the semi-quantitative system of persistence classes. The classification models utilized principal components derived from the principal component analysis of a set of 24 constitutional and quantum mechanical descriptors as input variables. Accuracies of classification (based on an external validation) were 86, 85, 87, and 75% for air, surface water, surface soil, and sediments, respectively. The persistence of all chlorinated species increased with increasing halogenation degree. In the case of brominated organic pollutants (Br-OPs), the trend was the same for air and sediments. However, we noticed that the opposite trend for persistence in surface water and soil. The results suggest that, due to high photoreactivity of C-Br chemical bonds, photolytic processes occurring in surface water and soil are able to play significant role in transforming and removing Br-OPs from these compartments. This contribution is the first attempt of classifying together Br-OPs and Cl-OPs according to their persistence, in particular, environmental compartments.

With the increasing amount of data made available in the chemical field, there is a strong need for systems capable of comparing and classifying chemical compounds in an efficient and effective way. The best approaches existing today are based on the structure-activity relationship premise, which states that biological activity of a molecule is strongly related to its structural or physicochemical properties. This work presents a novel approach to the automatic classification of chemical compounds by integrating semantic similarity with existing structural comparison methods. Our approach was assessed based on the Matthews Correlation Coefficient for the prediction, and achieved values of 0.810 when used as a prediction of blood-brain barrier permeability, 0.694 for P-glycoprotein substrate, and 0.673 for estrogen receptor binding activity. These results expose a significant improvement over the currently existing methods, whose best performances were 0.628, 0.591, and 0.647 respectively. It was demonstrated that the integration of semantic similarity is a feasible and effective way to improve existing chemical compound classification systems. Among other possible uses, this tool helps the study of the evolution of metabolic pathways, the study of the correlation of metabolic networks with properties of those networks, or the improvement of ontologies that represent chemical information.

This paper briefly outlines the United Nations Globally Harmonized System of Classification and Labelling of Chemicals (GHS) and what is meant by GHS implementation in terms of policy-related information for decision-makers plus a framework of actions to enable such policies to be effectively undertaken. The paper discusses the need for simple indicators to measure progress with GHS implementation. Two groups of indicators have been described that comprehensively reflect basic components of GHS implementation. The first group was presented as aggregated indicators or indices, for national capability evaluation. Indicators to evaluate management performance linked to stated objectives were presented as the second group. Information from an Organisation for Economic Co-operation and Development (OECD) published questionnaire on GHS implementation and a questionnaire by the Intergovernmental Forum on Chemical Safety (IFCS) on chemical safety that included GHS have provided the basic information for applying the management capability indicators approach. Through the use of information in these two questionnaires, country-relevant and regional progress with GHS implementation was illustrated. Despite the GHS implementation target of 2008 set by the Johannesburg Plan of Implementation, very few countries have reported they have achieved this target. The simple management indicators proposed in this paper are based on adoption of a questionnaire and a numerical scoring system that could be used to chart progress towards GHS implementation. It was concluded, therefore, that the use of indicators as described in this paper does provide a quick survey of the overall situation in a country and facilitates comparison between countries across regions in terms of GHS implementation. Furthermore such results may indicate which countries require additional assistance with GHS implementation thus strengthening policies and actions across all sectors and involving governments, business, trade, and civil society., (Copyright 2010 Elsevier Ltd. All rights reserved.)

Herein, we describe a new dataset of kinetic aqueous solubilities determined by nephelometry for 711 druglike compounds. The solubilities are reported in twelve classes ranging from <2 microg mL(-1) to >250 microg mL(-1). The measurements were designed to provide the appropriate data for applications in the early phases of drug discovery. Three class classification models (insoluble, moderately soluble, soluble) were built using the random forest algorithm and their performance for this dataset was analyzed.

In present study the Industrial chemical map was created, and investigated. Molecular descriptors were calculated for 56072 organic substances from the European inventory of existing commercial chemical substances (EINECS). The resulting multivariate dataset was subjected to principal component analysis (PCA), giving five principal components, mainly reflecting size, hydrophobicity, flexibility, halogenation and electronical properties. It is these five PCs that form the basis of the map of organic, industrial chemicals, the Industrial chemical map. The similarities and diversity in chemical characteristics of the substances in relation to their persistence (P), bioaccumulation (B) and long-range transport potential were then examined, by superimposing five sets of entries obtained from other relevant databases onto the Industrial chemical map. These sets displayed very similar diversity patterns in the map, although with a spread in all five PC vectors. Substances listed by the United Nations Environment Program as persistent organic pollutants (UNEP POPs) were on the other hand clearly grouped with respect to each of the five PCs. Illustrating similarities and differences in chemical properties are one of the strengths of the multivariate data analysis method, and to be able to make predictions of, and investigate new chemicals. Further, the results demonstrate that non-testing methods as read-across, based on molecular similarities, can reduce the requirements to test industrial chemicals, provided that they are applied carefully, in combination with sound chemical knowledge.

For 8500 organic chemicals from the Canadian Domestic Substances List, air/water and octanol/water partition coefficients have been calculated with COSMOfrag a predictive a priori method based on quantum mechanics and statistical thermodynamics provided by COSMOlogic. The results were compared with predictions from the EPI Suite. On average a difference of 1.8 log units between both methods was observed for the predicted logK(aw) values. This has consequences for the classification in terms of atmospheric long range transport potential and bioaccumulation in the terrestrial food chain where 11% and 17% of all compounds were classified differently. Much better agreement was observed for the predicted logK(ow). Principal differences became obvious for compounds that occur as isomers or conformers. The modules of the EPI Suite are not designed to account for information on stereochemistry of molecules or E/Z isomers. Intramolecular H-bonds are only accounted for if specific correction factors exist from the calibration procedure. In contrast, COSMOfrag in combination with COSMOtherm can identify the existence of such isomers or H-bond conformers and account for these effects in the calculations.

Objective: Chemical concepts assigned multiple "Chemical Viewed Structurally" semantic types (STs) in the Unified Medical Language System (UMLS) are subject to ambiguous interpretation. The multiple assignments may denote the fact that a specific represented chemical (combination) is a conjugate, derived via a chemical reaction of chemicals of the different types, or a complex, composed of a mixture of such chemicals. The previously introduced Refined Semantic Network (RSN) is modified to properly model these varied multi-typed chemical combinations., Design: The RSN was previously introduced as an enhanced abstraction of the UMLS's concepts. It features new types, called intersection semantic types (ISTs), each of which explicitly captures a unique combination of ST assignments in one abstract unit. The ambiguous ISTs of different "Chemical Viewed Structurally" ISTs of the RSN are replaced with two varieties of new types, called conjugate types and complex types, which explicitly denote the nature of the chemical interactions. Additional semantic relationships help further refine that new portion of the RSN rooted at the ST "Chemical Viewed Structurally.", Measurements: The number of new conjugate and complex types and the amount of changes to the type assignment of chemical concepts are presented., Results: The modified RSN, consisting of 35 types and featuring 22 new conjugate and complex types, is presented. A total of 800 (about 98%) chemical concepts representing multi-typed chemical combinations from "Chemical Viewed Structurally" STs are uniquely assigned one of the new types. An additional benefit is the identification of a number of illegal ISTs and ST assignment errors, some of which are direct violations of exclusion rules defined by the UMLS Semantic Network., Conclusion: The modified RSN provides an enhanced abstract view of the UMLS's chemical content. Its array of conjugate and complex types provides a more accurate model of the variety of combinations involving chemicals viewed structurally. This framework will help streamline the process of type assignments for such chemical concepts and improve user orientation to the richness of the chemical content of the UMLS.

Aspergillus fumigatus is the most important species in Aspergillus causing infective lung diseases. This species has been reported to produce a large number of extrolites, including secondary metabolites, acids, and proteins such as hydrophobins and extracellular enzymes. At least 226 potentially bioactive secondary metabolites have been reported from A. fumigatus that can be ordered into 24 biosynthetic families. Of these families we have detected representatives from the following families of secondary metabolites: fumigatins, fumigaclavines, fumiquinazolines, trypacidin and monomethylsulochrin, fumagillins, gliotoxins, pseurotins, chloroanthraquinones, fumitremorgins, verruculogen, helvolic acids, and pyripyropenes by HPLC with diode array detection and mass spectrometric detection. There is still doubt whether A. fumigatus can produce tryptoquivalins, but all isolates produce the related fumiquinazolines. We also tentatively detected sphingofungins in A. fumigatus Af293 and in an isolate of A. lentulus. The sphingofungins may have a similar role as the toxic fumonisins, found in A. niger. A further number of mycotoxins, including ochratoxin A, and other secondary metabolites have been reported from A. fumigatus, but in those cases either the fungus or its metabolite appear to be misidentified.

Purpose: Development of externally predictive Quantitative Structure-Activity Relationship (QSAR) models for Blood-Brain Barrier (BBB) permeability., Methods: Combinatorial QSAR analysis was carried out for a set of 159 compounds with known BBB permeability data. All six possible combinations of three collections of descriptors derived from two-dimensional representations of molecules as chemical graphs and two QSAR methodologies have been explored. Descriptors were calculated by MolconnZ, MOE, and Dragon software. QSAR methodologies included k-Nearest Neighbors and Support Vector Machine approaches. All models have been rigorously validated using both internal and external validation methods., Results: The consensus prediction for the external evaluation set afforded high predictive power (R2 = 0.80 for 10 compounds within the applicability domain after excluding one activity outlier). Classification accuracies for two additional external datasets, including 99 drugs and 267 organic compounds, classified as permeable (BBB+) or non-permeable (BBB-) were 82.5% and 59.0%, respectively. The use of a fairly conservative model applicability domain increased the prediction accuracy to 100% and 83%, respectively (while naturally reducing the dataset coverage to 60% and 43%, respectively). Important descriptors that affect BBB permeability are discussed., Conclusion: Models developed in these studies can be used to estimate the BBB permeability of drug candidates at early stages of drug development.

The local lymph node assay (LLNA) is used to assess the contact hypersensitivity potential of compounds. In the standard assay, mice are treated topically with test compound to the dorsum of both ears on Days 1-3. The induction of a hypersensitivity response is assessed on Day 6 by injecting [(3)H]-thymidine into a tail vein and measuring thymidine incorporation into DNA of lymph node cells draining the ears. The ex vivo LLNA is conducted similarly except lymphocyte proliferation is assessed after in vitro incubation of lymph node cells with [(3)H]-thymidine, which significantly reduces the amount of radioactive waste. The current study tested the use of this approach for hazard assessment of contact hypersensitivity and to estimate allergenic potency. Female BALB/c mice were treated on Days 1-3 with two nonsensitizers (4' -methoxyacetophenone, diethyl phthalate), three weak sensitizers (hydroxycitronellal, eugenol, citral), one weak-to-moderate sensitizer (hexylcinnamic aldehyde), two moderate sensitizers (isoeugenol, phenyl benzoate), and one strong sensitizer (dinitrochlorobenzene). On Day 6, isolated lymph node cells were incubated overnight with [(3)H]-thymidine and thymidine incorporation was measured by liquid scintillation spectrophotometry. The ex vivo LLNA accurately distinguished the contact sensitizers from the nonsensitizing chemicals, and correctly ranked the relative potency of the compounds tested. The EC3 values, i.e., the effective concentration of test substance needed to induce a stimulation index of 3, were as follows: 4' -methoxyacetophenone (> 50%), diethyl phthalate (> 50%), hydroxycitronellal (20.4%), eugenol (13.6%), citral (8.9%), isoeugenol (3.8%), hexylcinnamic aldehyde (2.7%), phenyl benzoate (2%), and dinitrochlorobenzene (0.02%). In addition, low inter-animal and inter-experiment variability was seen with 25% hexyl-cinnamic aldehyde (assay positive control). The results of the ex vivo LLNA in the current study were consistent with published reports using the standard LLNA and provided further evidence that supports the use of this alternative approach to assess the skin sensitization potential of test compounds.

The formation of secondary organic aerosols (SOA) has been investigated intensively during the last two decades in numerous field and laboratory studies and a general understanding exists about the major particle-phase products. However, recent studies show that several new product classes, such as esters, peroxides or organosulfates, also have to be considered in order to understand the detailed chemical mechanisms leading to SOA as well as to predict the aerosol mass loadings. For the identification and quantification of these three compound classes as well as for carboxylic SOA compounds, liquid chromatography (LC)/mass spectrometry (MS) is the most appropriate analytical method. In this article we try to summarize briefly the work that has been done for the determination of SOA-related carboxylic acids and we present new LC/tandem MS results on the characterization of esters, peroxides and organosulfates. In contrast to earlier work, the mass-spectrometric characterization of the individual compounds is always based on the comparison with authentic reference compounds.

In this work we introduce a graph theoretical method to compare MEPs, which is independent of molecular alignment. It is based on the edit distance of weighted rooted trees, which encode the geometrical and topological information of Negative Molecular Isopotential Surfaces. A meaningful chemical classification of a set of 46 molecules with different functional groups was achieved. Structure--activity relationships for the corticosteroid binding affinity (CBG) of 31 steroids by means of hierarchical clustering resulted in a clear partitioning in high, intermediate, and low activity groups, whereas the results from quantitative structure--activity relationships, obtained from a partial least-squares analysis, showed comparable or better cross-validated correlation coefficients than the ones reported for previous methods based solely in the MEP.

Measurements of retention factors by gas chromatography on up to 10 complementary stationary phases at up to 5 temperatures for each stationary phase and liquid-liquid partition coefficients in three biphasic organic solvent systems (n-hexane-acetonitrile, n-heptane-N,N-dimethylformamide and n-heptane-2,2,2-trifluoroethanol) were used to estimate solute descriptors for 54 organosilicon compounds for use in the solvation parameter model. Many of the E descriptor values (electron lone pair interactions) are negative for simple siloxanes and silanes indicating that these compound bind electron lone pairs more tightly than n-alkanes. Silanes and siloxanes with alkyl groups have near zero dipolarity/polarizability (S descriptor). The S descriptor is only modest for simple phenylsilanes, silazanes, silanols, orthosilicates, and alkoxides. All organosilicon compounds with silicon-oxygen bonds are reasonably strong hydrogen-bond bases (B descriptor) but only the silanol group is a reasonably strong hydrogen-bond acid (A descriptor). Silanes (SiH) and silazanes (SiNHSi) are weak hydrogen-bond acids. Cavity formation and dispersion interactions (V or L descriptor) are often the main component of solvation models for siloxanes and silanes that have simple alkyl and aromatic substituents. A number of physicochemical properties (vapor pressure, aqueous solubility, biphasic partition coefficients, sorption coefficients, etc.) for linear and cyclic dimethylsiloxanes can be reliably predicted from their descriptors in established models for organic compounds.

A new method is presented to predict which donors and acceptors form hydrogen bonds in a crystal structure, based on the statistical analysis of hydrogen bonds in the Cambridge Structural Database (CSD). The method is named the logit hydrogen-bonding propensity (LHP) model. The approach has a potential application in identifying both likely and unusual hydrogen bonding, which can help to rationalize stable and metastable crystalline forms, of relevance to drug development in the pharmaceutical industry. Whilst polymorph prediction techniques are widely used, the LHP model is knowledge-based and is not restricted by the computational issues of polymorph prediction, and as such may form a valuable precursor to polymorph screening. Model construction applies logistic regression, using training data obtained with a new survey method based on the CSD system. The survey categorizes the hydrogen bonds and extracts model parameter values using descriptive structural and chemical properties from three-dimensional organic crystal structures. LHP predictions from a fitted model are made using two-dimensional observables alone. In the initial cases analysed, the model is highly accurate, achieving approximately 90% correct classification of both observed hydrogen bonds and non-interacting donor-acceptor pairs. Extensive statistical validation shows the LHP model to be robust across a range of small-molecule organic crystal structures.

Unlabelled: ChemDB is a chemical database containing nearly 5M commercially available small molecules, important for use as synthetic building blocks, probes in systems biology and as leads for the discovery of drugs and other useful compounds. The data is publicly available over the web for download and for targeted searches using a variety of powerful methods. The chemical data includes predicted or experimentally determined physicochemical properties, such as 3D structure, melting temperature and solubility. Recent developments include optimization of chemical structure (and substructure) retrieval algorithms, enabling full database searches in less than a second. A text-based search engine allows efficient searching of compounds based on over 65M annotations from over 150 vendors. When searching for chemicals by name, fuzzy text matching capabilities yield productive results even when the correct spelling of a chemical name is unknown, taking advantage of both systematic and common names. Finally, built in reaction models enable searches through virtual chemical space, consisting of hypothetical products readily synthesizable from the building blocks in ChemDB., Availability: ChemDB and Supplementary Materials are available at http://cdb.ics.uci.edu., Supplementary Information: Supplementary data are available at Bioinformatics online.