Prediction of aquatic toxicity mode of action using linear discriminant and random forest models.

The ability to determine the mode of action (MOA) for a diverse group of chemicals is a critical part of ecological risk assessment and chemical regulation. However, existing MOA assignment approaches in ecotoxicology have been limited to a relatively few MOAs, have high uncertainty, or rely on professional judgment. In this study, machine based learning algorithms (linear discriminant analysis and random forest) were used to develop models for assigning aquatic toxicity MOA. These methods were selected since they have been shown to be able to correlate diverse data sets and provide an indication of the most important descriptors. A data set of MOA assignments for 924 chemicals was developed using a combination of high confidence assignments, international consensus classifications, ASTER (ASessment Tools for the Evaluation of Risk) predictions, and weight of evidence professional judgment based an assessment of structure and literature information. The overall data set was randomly divided into a training set (75%) and a validation set (25%) and then used to develop linear discriminant analysis (LDA) and random forest (RF) MOA assignment models. The LDA and RF models had high internal concordance and specificity and were able to produce overall prediction accuracies ranging from 84.5 to 87.7% for the validation set. These results demonstrate that computational chemistry approaches can be used to determine the acute toxicity MOAs across a large range of structures and mechanisms.

Estimation of environment-related properties of chemicals for design of sustainable processes: development of group-contribution+ (GC+) property models and uncertainty analysis.

The aim of this work is to develop group-contribution(+) (GC(+)) method (combined group-contribution (GC) method and atom connectivity index (CI) method) based property models to provide reliable estimations of environment-related properties of organic chemicals together with uncertainties of estimated property values. For this purpose, a systematic methodology for property modeling and uncertainty analysis is used. The methodology includes a parameter estimation step to determine parameters of property models and an uncertainty analysis step to establish statistical information about the quality of parameter estimation, such as the parameter covariance, the standard errors in predicted properties, and the confidence intervals. For parameter estimation, large data sets of experimentally measured property values of a wide range of chemicals (hydrocarbons, oxygenated chemicals, nitrogenated chemicals, poly functional chemicals, etc.) taken from the database of the US Environmental Protection Agency (EPA) and from the database of USEtox is used. For property modeling and uncertainty analysis, the Marrero and Gani GC method and atom connectivity index method have been considered. In total, 22 environment-related properties, which include the fathead minnow 96-h LC(50), Daphnia magna 48-h LC(50), oral rat LD(50), aqueous solubility, bioconcentration factor, permissible exposure limit (OSHA-TWA), photochemical oxidation potential, global warming potential, ozone depletion potential, acidification potential, emission to urban air (carcinogenic and noncarcinogenic), emission to continental rural air (carcinogenic and noncarcinogenic), emission to continental fresh water (carcinogenic and noncarcinogenic), emission to continental seawater (carcinogenic and noncarcinogenic), emission to continental natural soil (carcinogenic and noncarcinogenic), and emission to continental agricultural soil (carcinogenic and noncarcinogenic) have been modeled and analyzed. The application of the developed property models for the estimation of environment-related properties and uncertainties of the estimated property values is highlighted through an illustrative example. The developed property models provide reliable estimates of environment-related properties needed to perform process synthesis, design, and analysis of sustainable chemical processes and allow one to evaluate the effect of uncertainties of estimated property values on the calculated performance of processes giving useful insights into quality and reliability of the design of sustainable processes.

Does rational selection of training and test sets improve the outcome of QSAR modeling?

Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable.

Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set.

The estimation of accuracy and applicability of QSAR and QSPR models for biological and physicochemical properties represents a critical problem. The developed parameter of "distance to model" (DM) is defined as a metric of similarity between the training and test set compounds that have been subjected to QSAR/QSPR modeling. In our previous work, we demonstrated the utility and optimal performance of DM metrics that have been based on the standard deviation within an ensemble of QSAR models. The current study applies such analysis to 30 QSAR models for the Ames mutagenicity data set that were previously reported within the 2009 QSAR challenge. We demonstrate that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs. The presented approach identifies 30-60% of compounds having an accuracy of prediction similar to the interlaboratory accuracy of the Ames test, which is estimated to be 90%. Thus, the in silico predictions can be used to halve the cost of experimental measurements by providing a similar prediction accuracy. The developed model has been made publicly available at http://ochem.eu/models/1 .

Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure.

Few quantitative structure-activity relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity end points. In this study, a comprehensive data set of 7385 compounds with their most conservative lethal dose (LD(50)) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. To enable fair comparison between the predictive power of models generated in this study versus a commercial toxicity predictor, TOPKAT (Toxicity Prediction by Komputer Assisted Technology), a modeling subset of the entire data set was selected that included all 3472 compounds used in TOPKAT's training set. The remaining 3913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. QSAR models of five different types were developed for the modeling set. The prediction accuracy for the external validation set was estimated by determination coefficient R(2) of linear regression between actual and predicted LD(50) values. The use of the applicability domain threshold implemented in most models generally improved the external prediction accuracy but expectedly led to the decrease in chemical space coverage; depending on the applicability domain threshold, R(2) ranged from 0.24 to 0.70. Ultimately, several consensus models were developed by averaging the predicted LD(50) for every compound using all five models. The consensus models afforded higher prediction accuracy for the external validation data set with the higher coverage as compared to individual constituent models. The validated consensus LD(50) models developed in this study can be used as reliable computational predictors of in vivo acute toxicity.

A hierarchical clustering methodology for the estimation of toxicity.

ABSTRACT A quantitative structure-activity relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural similarity is defined in terms of 2-D physicochemical descriptors (such as connectivity and E-state indices). A genetic algorithm-based technique is used to generate statistically valid QSAR models for each cluster (using the pool of descriptors described above). The toxicity for a given query compound is estimated using the weighted average of the predictions from the closest cluster from each step in the hierarchical clustering assuming that the compound is within the domain of applicability of the cluster. The hierarchical clustering methodology was tested using a Tetrahymena pyriformis acute toxicity data set containing 644 chemicals in the training set and with two prediction sets containing 339 and 110 chemicals. The results from the hierarchical clustering methodology were compared to the results from several different QSAR methodologies.

Precursors to dinitrogen reduction: structures and reactivity of trans-[Fe(DMeOPrPE)2(eta(2)-H2)H]+ and trans-[Fe(DMeOPrPE)2(N2)H]+.

trans-[Fe(DMeOPrPE)(2)(H(2))H](+) and trans-[Fe(DMeOPrPE)(2)(N(2))H](+) (DMeOPrPE = 1,2-bis(dimethoxypropylphosphino)ethane) were synthesized and their structures determined by X-ray crystallography. These complexes are important species in a dinitrogen reduction scheme involving protonation of an iron(0) dinitrogen complex to produce ammonia. The rates of substitution of the coordinated H(2) and N(2) molecules with acetonitrile were monitored in a variety of organic solvents. The coordinated N(2) substituted approximately 6 times faster than H(2), but surprisingly the solvent had little effect on the observed rates. The results suggest that the H(2) molecule in trans-[Fe(DMeOPrPE)(2)(H(2))H](+) does not participate in hydrogen bonding to the bulk solvent, as was previously observed in the analogous Ru complex. The deprotonation of trans-[Fe(DMeOPrPE)(2)(N(2))H](+) to yield Fe(DMeOPrPE)(2)N(2) was investigated in the presence of a variety of anions, and it was found that the anion facilitates the reaction through an ion-pairing interaction in which the anion removes electron density from the hydride ligand.

Prediction of growth and biotransformation rates of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) in the presence of barium.

The biotransformation of explosives has been investigated by many researchers. Bioremediation of soil and water contaminated with hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) is becoming the method of choice for clean-up of a variety of sites. In this study, we investigated biotransformation of RDX in the presence of barium. Ba is a metal commonly found in combination with RDX at sites requiring remediation. RDX was biotransformed by both a consortium of bacteria and an isolate from the consortium under anoxic conditions using a rich medium. However, Ba inhibited cell growth under both aerobic and anoxic conditions and slowed biotransformation rates by 40%. RDX and Ba inhibited growth of the isolate more than growth of the consortium. An additive inhibition model is proposed that accurately predicts the reduced growth rates observed.

Addition of Cl2C: to (-)-O-menthyl acrylate under sonication-phase-transfer catalysis. Efficient synthesis of (+)- and (-)-(2-chlorocyclopropyl)methanol.

[reaction: see text] Dichlorocyclopropanation of (-)-O-menthyl acrylate under conditions of phase-transfer catalysis (CHCl3, KOH, tetramethylammonium bromide), with sonication, gives excellent yields (85-94%) of the corresponding dichlorocyclopropanecarboxylate ester compared to thermal conditions (90 degrees C, 56%). No diastereoselectivity was observed, but one isomer was isolated pure by fractional crystallization. The measured kinetic isotope effect (initial rate (CHCl3)/rate (CDCl3) approximately 1.7) suggests deprotonation of CHCl3 as the rate-limiting step.

Life cycle assessment of gasoline blending options.

A life cycle assessment has been done to compare the potential environmental impacts of various gasoline blends that meet octane and vapor pressure specifications. The main blending components of alkylate, cracked gasoline, and reformate have different octane and vapor pressure values as well as different potential environmental impacts. Because the octane and vapor pressure values are nonlinearly related to impacts, the results of this study show that some blends are better for the environment than others. To determine blending component compositions, simulations of a reformer were done at various operating conditions. The reformate products of these simulations had a wide range of octane values and potential environmental impacts. Results of the study indicate that for low-octane gasoline (95 Research Octane Number), lower reformer temperatures and pressures generally decrease the potential environmental impacts. However, different results are obtained for high-octane gasoline (98 RON), where increasing reformer temperatures and pressures increase the reformate octane values faster than the potential environmental impacts. The higher octane values for reformate allow blends to have less reformate, and therefore high-octane gasoline can have lower potential environmental impacts when the reformer is operated at higher temperatures and pressures. In the blends studied, reformate and cracked gasoline have the highest total impacts, of which photochemical ozone creation is the largest contributor (assuming all impact categories are equally weighted). Alkylate has a much lower total potential environmental impact but does have higher impact values for human toxicity by ingestion, aquatic toxicity, terrestrial toxicity, and acidification. Therefore, depending on environmental priorities, different gasoline blends and operating conditions should be chosen to meet octane and vapor pressure specifications.