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This article aims to provide a comprehensive critical, yet readable, review of general interest to the chemistry community on molecular similarity as applied to chemical informatics and predictive modeling with a special focus on read-across (RA) and read-across structure–activity relationships (RASAR). Molecular similarity-based computational tools, such as quantitative structure–activity relationships (QSARs) and RA, are routinely used to fill the data gaps for a wide range of properties including toxicity endpoints for regulatory purposes. This review will explore the background of RA starting from how structural information has been used through to how other similarity contexts such as physicochemical, absorption, distribution, metabolism, and elimination (ADME) properties, and biological aspects are being characterized. More recent developments of RA's integration with QSAR have resulted in the emergence of novel models such as ToxRead, generalized read-across (GenRA), and quantitative RASAR (q-RASAR). Conventional QSAR techniques have been excluded from this review except where necessary for context. [ABSTRACT FROM AUTHOR]

The availability of experimental nanotoxicity data is in general limited which warrants both the use of in silico methods for data gap filling and exploring novel methods for effective modeling. Read-Across Structure-Activity Relationship (RASAR) is an emerging cheminformatic approach that combines the usefulness of a QSAR model and similarity-based Read-Across predictions. In this work, we have generated simple, interpretable, and transferable quantitative-RASAR (q-RASAR) models which can efficiently predict the cytotoxicity of TiO2-based multi-component nanoparticles. A data set of 29 TiO2-based nanoparticles with specific amounts of noble metal precursors was rationally divided into training and test sets, and the Read-Across-based predictions for the test set were generated. The optimized hyperparameters and the similarity approach, which yield the best predictions, were used to calculate the similarity and error-based RASAR descriptors. A data fusion of the RASAR descriptors with the chemical descriptors was done followed by the best subset feature selection. The final set of selected descriptors was used to develop the q-RASAR models, which were validated using the stringent OECD criteria. Finally, a random forest model was also developed with the selected descriptors, which could efficiently predict the cytotoxicity of TiO2-based multi-component nanoparticles superseding previously reported models in the prediction quality thus showing the merits of the q-RASAR approach. To further evaluate the usefulness of the approach, we have applied the q-RASAR approach also to a second cytotoxicity data set of 34 heterogeneous TiO2-based nanoparticles which further confirmed the enhancement of external prediction quality of QSAR models after incorporation of RASAR descriptors. [ABSTRACT FROM AUTHOR]

Mutagenicity is considered an important endpoint from the regulatory, environmental and medical points of view. Due to the wide number of compounds that may be of concern and the enormous expenses (in terms of time, money, and animals) associated with rodent mutagenicity bioassays, this endpoint is a major target for the development of alternative approaches for screening and prediction. The majority of old-aged expert systems and quantitative structure-activity relationship (QSAR) models may show reduced performance over time for their application on newer chemical candidates; thus, researchers constantly try to improve the modeling strategies. In our report, we initially performed traditional classification-based linear discriminant analysis (LDA) QSAR modeling using the benchmark Ames dataset of diverse chemicals (6512 compounds) to recognize the relationship between the molecules and their potential mutagenic behavior. The classical LDA QSAR model is developed from a selected set of 2D descriptors. The LDA QSAR model was developed by using a total of 31 descriptors identified from the analysis of the most discriminating features. Additionally, we have used similarity-derived features obtained from the read-across (RA) to develop an RA-based QSAR model. The developed RA-based LDA QSAR model has better predictivity, transferability, and interpretability compared to the LDA QSAR model, and it uses a very small number of descriptors compared to the classical QSAR model. Different machine learning (ML) models were also developed using the descriptors appearing in the read-across-based LDA QSAR model for comparative studies. We have checked the prediction quality of 216 true external set compounds using the novel similarity-derived RA model. The performance of the OECD toolbox is also compared with the RA-derived LDA QSAR model for a true external set. The current study aimed to explore the significance of the read-across-based algorithm and its application to the most current experimental mutagenicity data to complement already available expert systems. [Display omitted] • A read-across-derived LDA model is developed for the mutagenic properties of chemicals. • The model was extensively validated. • The read-across-based LDA QSAR model has better predictivity than the LDA QSAR model. • This study offers a new and powerful algorithm for filling the data gap on mutagenicity. • The proposed method offers an effective tool to support the risk assessment for mutagenicity. [ABSTRACT FROM AUTHOR]

Antibiotics are often found in the environment as pollutants. They are usually found as mixtures in the environment and may produce toxicity against different ecological species due to joint exposure in the sub-optimal range. Sometimes the degradation products of parent chemicals also interact with it and cause mixture toxicity. In this study, we have developed three different mixture-Quantitative Structure-Activity Relationship (mixture-QSAR) models for three different bacterial species (Vibrio fischeri, Escherichia coli, and Bacillus subtilis). The toxicity data were collected from a previous experimental report in the literature, which comprised binary and ternary mixtures of sulfonamides (SAs), sulfonamide potentiators (SAPs), and tetracyclines (TCs). We have also explored the interspecies modeling to find inter-correlation among the toxicity of these studied organisms and have developed quantitative structure activity-activity relationship (QSAAR) models by employing the "data fusion" quantitative read-across structure-activity–activity relationship (q-RASAAR) and partial least squares (PLS) regression algorithms. All the models are strictly validated using both internal and external validation tests as suggested in the OECD guidelines. Three different mixing rules have been used in this study for descriptor computations to incorporate the additive and interaction effects among the mixture components. To the best of our knowledge, this is the first report of interspecies mixture toxicity models which can predict the cellular toxicity of binary and ternary mixtures against any of the three above-mentioned organisms. [Display omitted] • Simple 2D-QSAR models have been developed for the mixture toxicity against V. fischeri, E. coli, and B. subtilis. • Three mixing rules have been applied to compute the optimum mixture descriptors. • The q-RASAR algorithm is found as an efficient strategy to develop interspecies QSAAR (q-RASAAR) models. • Mixture toxicity against E. coli is additive, whereas that against V. fischeri and B. subtilis involves interaction terms. • The mixture toxicity patterns against V. fischeri and B. subtilis show similarities unlike the E. coli toxicity. [ABSTRACT FROM AUTHOR]

Abstract Over the past few years, the ecotoxicological hazard potential of agrochemicals has received much attention in the industries and regulatory agencies. In the current work, we have developed quantitative structure-activity relationship (QSAR) models for Daphnia magna toxicities of different classes of agrochemicals (fungicides, herbicides, insecticides and microbiocides) individually as well as for the combined set with the application of Organization for Economic Co-operation and Development (OECD) recommended guidelines. The models for the individual data sets as well as for the combined set were generated employing only simple and interpretable two-dimensional descriptors, and subsequently strictly validated using test set compounds. The validated individual models were used to generate consensus models, with the objective to improve the prediction quality and reduced prediction errors. All the individual models of different classes of agrochemicals as well as the global set of agrochemicals showed encouraging statistical quality and prediction ability. The general observations from the derived models suggest that the toxicity increases with lipophilicity and decreases with polarity. The generated models of different classes of agrochemicals and also for the combined set should be applicable for data gap filling for new or untested agrochemical compounds falling within the applicability domain of the developed models. Graphical abstract Image 1 Highlights • We propose simple QSAR models for toxicity prediction of agrochemicals against D. magna. • 2D descriptors with definite physicochemical meaning were employed for model development. • The models were validated by considering various internal as well as external parameters. • All the models suggest the impact of lipophilicity and polarity in governing agrochemical toxicity. • The models can be utilized in data gap filling, regulatory screening and for prediction of new or untested agrochemicals. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Toxicity of 144 endocrine disruptor chemicals (EDCs) against 14 different species has been modelled. • This is the first report of modelling which covers so many different endpoints for the toxicity of EDCs. • Interspecies toxicity modelling has also been reported in cases where sufficient common data are available. • All models have been validated using stringent internal and external validation tools. Abstract This study reports highly robust externally predictive quantitative structure-toxicity relationship (QSTR) and interspecies quantitative structure-toxicity-toxicity (i-QSTTR) models developed using toxicity data of endocrine disruptor chemicals (EDCs) towards 14 different species falling in four different trophic levels. Genetic algorithm followed by Partial Least Squares (PLS) regression was used in model development following the strict OECD guidelines. The models were developed using 2D descriptors having definite physicochemical meaning and validated by several internationally accepted validation metrics. The scope of predictions was defined by estimating applicability domain of the models. Presence of halogens, sulfur and phosphorus in the molecules greatly influenced the toxicity of EDCs as suggested by continuous repetition of 2D atom pair descriptors. Lipophilic contributions as calculated by logP terms (mainly ALOGP2 and XlogP) were the second most important feature controlling the EDC hazards. Hydrophilic moiety such as functionalities like esters, aliphatic ethers, branching and higher oxygen content reduced the EDC toxicity. Interspecies models were employed in data gap filling following the hierarchy of different species. The reliability of predictions was calculated by the "prediction reliability indicator" tool. [ABSTRACT FROM AUTHOR]

To address the nanomaterial exposure threat, it is imperative to understand how nanomaterials are recognized, internalized, and distributed within diverse cell systems. Targeting of nanomaterials to a specific cell type is generally attained through the modification of the nanoparticle (NP) surface leading to required cellular uptake. The enhanced cellular uptake to normal cells can direct to the higher interaction of NPs with subcellular organelles resulting the provocation of various signaling pathways. The successes of NPs rely on the prospect for the synthesis of functionalized NPs with necessary properties and their enhanced potential for cellular uptake for specific targeting. In the present study, we have modeled the cellular uptake of 109 surface modifiers of metal oxide nanoparticles (MNPs) for three different cell lines: HUVEC (Human endothelial cells), U937 (human macrophage cells), and PaCa2 (cancer cell lines). Along with the quantitative structure–activity relationship (QSAR) models, for the very first time we have developed and performed quantitative inter cell line uptake specificity (QICLUS) modeling to identify the physicochemical properties, as well as majorly structural fragments responsible for cellular uptake differences between two specific cell lines. The present work provides a comprehensive understanding of the cellular uptake of MNPs and the underlying structural parameters controlling the nano-cellular interactions. This phenomenon has also been analyzed from the QSAR and QICLUS models that concluded the functional groups of surface modifiers like amine, anhydride, halogen atoms, nitro group, acids have the dominating roles for the uptake of MNPs into the cell lines. Thus, the developed models may be used for designing of novel surface modifiers of MNPs of desired characteristics for proper cell–NPs interactions, as well as in the context of virtual screening aspect. Moreover, the MNP–cell interactions can give some idea about the toxicity for target-specific drug delivery treatment as higher cellular uptake is required for specific cells to treat the disease and lower uptake to the neighboring cells for lower toxicity. [ABSTRACT FROM AUTHOR]

Abstract In the present work, quantitative structure-activity relationship (QSAR) models have been developed for ecotoxicity of pharmaceuticals on four different aquatic species namely Pseudokirchneriella subcapitata , Daphnia magna , Oncorhynchus mykiss and Pimephales promelas using genetic algorithm (GA) for feature selection followed by Partial Least Squares regression technique according to the Organization for Economic Co-operation and Development (OECD) guidelines. Double cross-validation methodology was employed for selecting suitable models. Only 2D descriptors were used for capturing chemical information and model building, whereas validation of the models was performed by considering various stringent internal and external validation metrics. Interestingly, models could be developed even without using any LogP terms in contrary to the usual dependence of toxicity on lipophilicity. However, the current manuscript proposes highly robust and more predictive models employing computed logP descriptors. The applicability domain study was performed in order to set a predefined chemical zone of applicability for the obtained QSAR models, and the test compounds falling outside the domain were not taken for further analysis while making a prioritized list. An additional comparison was made with ECOSAR, an online expert system for toxicity prediction of organic pollutants, in order to prove predictability of the obtained models. The obtained robust consensus models were utilized to predict the toxicity of a large dataset of approximately 9300 drug-like molecules in order to prioritize the existing drug-like substances in accordance to their acute predicted aquatic toxicities following a scaling technique. Finally, prioritized lists of 500 most toxic chemicals obtained by respective consensus models and those predicted from ECOSAR tool have been reported. Highlights • This work proposes robust QSAR models for aquatic toxicity of pharmaceuticals. • Consensus modeling improves predictive performance of models. • Comparison of predictive performance is made with the ECOSAR tool. • Ranking and prioritization of pharmaceuticals from DrugBank database are performed. [ABSTRACT FROM AUTHOR]

Abstract: Quantitative structure‐activity/property/toxicity relationship (QSAR/QSPR/QSTR) models are effectively employed to fill data gaps by predicting a given response from known structural features or physicochemical properties of new query compounds. The performance of a model should be assessed based on the quality of predictions checked through diverse validation metrics, which confirm the reliability of the developed QSAR models along with the acceptability of their prediction quality for untested compounds. There is an ongoing effort by QSAR modelers to improve the quality of predictions by lowering the predicted residuals for query compounds. In this endeavor, consensus models integrating all validated individual models were found to be more externally predictive than individual models in many previous studies. The objective of this work has been to explore whether the quality of predictions of external compounds can be enhanced through an “intelligent” selection of multiple models. The consensus predictions used in this study are not simple average of predictions from multiple models. It has been considered in the present study that a particular QSAR model may not be equally effective for prediction of all query compounds in the list. Our approach is different from the previous ones in that none of the previously reported methods considered selection of predictive models in a query compound specific way while at the same time using all or most of the valid models for the total set of query chemicals. We have implemented our approach in a software tool that is freely available via the web http://teqip.jdvu.ac.in/QSAR_Tools/ and http://dtclab.webs.com/software‐tools. [ABSTRACT FROM AUTHOR]

One of the important applications of quantitative structure-activity relationship (QSAR) models is to fill data gaps by predicting a given response property or activity from known molecular features or physicochemical properties of new compounds which might not have been tested experimentally. The general QSAR users are now already aware that the new compounds which will be predicted from a given QSAR model should be similar in structural and/or physicochemical property space to the training set compounds so as to be included in the chemical applicability domain of the model. It is also well-established, at least among some groups of QSAR scientists, that performance of a model should be evaluated based on the quality of predictions from the test set and not from the training set to obviate any overfitting problem. It is also important to analyze prediction errors of test set compounds in order to search for presence of any systematic error or bias in model predictions. A simple residual plot can provide sufficient information about the type of error present in model predictions. As the test set compounds are structurally and physico-chemically similar to the training set compounds, the model prediction errors for the test set also should obey, at least approximately, the basic assumptions of the least-squares regression under the best linear unbiased estimator (BLUE) framework, provided that a sufficient number of test set compounds is available allowing an acceptable degree of freedom. The present article explains the importance of analysis of prediction errors to check for the presence of systematic error and/or violation of basic assumptions of the least-squares regression models under the BLUE framework with suitable examples using real QSAR model-derived quantitative predictions for test sets and simulated prediction data. The intention of the present authors is neither to make a comparison of performances of different validation metrics for quantitative predictions (eventually favoring or disfavoring one or the other validation metric) nor to compare quality of different models but to indicate the situations where such comparison should not be made due to inappropriate functional form of a model making it unsuitable for quantitative predictions of a set of compounds and also due to the use of unrealistic and biased prediction pattern that never happens in real QSAR problems, thus making the situation unsuitable for making any generalized conclusion. This article also alerts the QSAR users to the importance of “statistical applicability domain” of QSAR models before their application for quantitative prediction of a response of test set compounds in order to compare performance of different validation approaches. [ABSTRACT FROM AUTHOR]

Quantitative structure–activity relationship (QSAR) modeling is a widely used computational technique applied in various fields including rational drug design, toxicity and property prediction of chemicals and pharmaceuticals, environmental risk assessment and fate modeling. External validation is generally considered as the gold standard in evaluating the model predictivity performance, at least to a group of QSAR practitioners. External validation is commonly performed by employing the hold-out method, where the original dataset is divided into training and test sets; the training set is employed for model building and model selection, while the test set is solely used in model assessment. However, since the composition of the training set remains the same in this method, it is not certain that the resultant model is optimal as there may be a bias in descriptor selection. This problem is more evident for the multiple linear regression (MLR) models than more robust and generalized partial least squares (PLS) and principal component regression (PCR) models. Thus, employing double cross-validation technique could be a better choice, in which the training set is further divided into ‘ n ’ calibration and validation sets resulting in diverse compositions. In the present work, we have developed an open access “Double Cross-Validation (DCV)” software tool which can be used to perform multiple linear regression (MLR) model development by employing the double cross-validation technique. Two variable selection methods, namely, stepwise MLR (S-MLR) and genetic algorithm MLR (GA-MLR) are incorporated in this tool and optionally, this tool also performs a data-pretreatment prior to the application of double cross-validation. Also, we have performed a study using the “Double Cross-Validation” tool on three different datasets in order to find out which technique among the hold-out and double cross-validation performs better in the selection of an optimal model in terms of model predictive performance checked on the test set. The performance of the tool in generating predictive PLS models is also compared. The “Double Cross-Validation” ( version 2.0) tool is freely available to download from the sites http://teqip.jdvu.ac.in/QSAR_Tools/ and http://dtclab.webs.com/software-tools . [ABSTRACT FROM AUTHOR]

Different classes of chemicals are present in the environment as mixtures. Among them, pharmaceuticals and pesticides are of major concern due to their improper use and disposal, and subsequent additive and non-additive effects. To assess the environmental risk posed by the mixtures of pharmaceuticals and pesticides, a quantitative structure-activity relationship (QSAR) model has been developed in this study using the pEC 50 values of 198 binary and multi-component mixtures against the marine bacterium Aliivibrio fischeri. The developed partial least squares (PLS) model has been rigorously validated and proved to be a robust and extremely predictive one. To address the chances of overestimation of validation metrics, three cross-validation tests (mixtures out, compounds out, and everything out) have been applied, and the results were satisfactory. The use of simple 2-dimensional descriptors makes the prediction much quick, and also makes the model easily interpretable. A machine learning-based chemical read-across prediction has also been performed to justify the effectiveness of selected structural features in this study. In a nutshell, this study proves QSAR and chemical read-across as effective alternative approaches for the toxicity prediction of pharmaceutical and pesticide mixtures and also approves the use of mixture descriptors for modelling mixtures successfully. [Display omitted] • We apply QSAR and chemical read-across for the toxicity prediction of binary and multi-component mixtures against Aliivibrio fischeri. • We apply mixtures out, compounds out, and everything out cross-validation. • We have used simple 2D structural descriptors for fast prediction, easy interpretability and transferability of the model. • These methods are the easy, fast, cheap, and ethically acceptable alternatives to the mixture's risk assessment. [ABSTRACT FROM AUTHOR]

Validation is the most crucial concept for development and application of quantitative structure–activity relationship (QSAR) models. The validation process confirms the reliability of the developed QSAR models along with the acceptability of each step during model development such as assessing the quality of input data, dataset diversity, predictability on an external set, domain of applicability and mechanistic interpretability. External validation or validation using an independent test set is usually considered as the gold standard in evaluating the quality of predictions from a QSAR model. The external predictivity of QSAR models is commonly described by employing various validation metrics, which can be broadly categorized into two major classes, viz., R 2 based metrics namely R 2 test , Q 2 (ext_F1) , and Q 2 (ext_F2) , and purely error based measures like predicted residual sum of squares (PRESS), root mean square error (RMSE), and mean absolute error (MAE). The problem associated with the error based measures is the absence of any well-defined threshold for determining the quality of predictions making the R 2 based metrics more suitable for use due to easy comprehension. However, in this paper, we show the problems associated with the R 2 based validation metrics commonly used in QSAR studies, since their values are highly dependent on the range of the response values of the test set compounds and their distribution pattern around the training/test set mean. We also propose a guideline for determining the quality of predictions based on MAE and its standard deviation computed from the test set predictions after omitting 5% high residual data points in order to obviate the influence of any rarely occurring high prediction errors that may significantly obscure the quality of predictions for the whole test set. In this manner, we try to evaluate the prediction performance of a model on most (95%) of the data points present in the external set. An online tool (XternalValidationPlus) for computing the suggested MAE based criteria (along with other conventional metrics) for external validation has been made available at http://dtclab.webs.com/software-tools and http://teqip.jdvu.ac.in/QSAR_Tools/ . The MAE based criteria suggested here along with other commonly used validation metrics may be applied to evaluate predictive performance of QSAR models with a greater degree of confidence. [ABSTRACT FROM AUTHOR]

The present study explores the chemical attributes of diverse ionic liquids responsible for their cytotoxicity in a rat leukemia cell line (IPC-81) by developing predictive classification as well as regression-based mathematical models. Simple and interpretable descriptors derived from a two-dimensional representation of the chemical structures along with quantum topological molecular similarity indices have been used for model development, employing unambiguous modeling strategies that strictly obey the guidelines of the Organization for Economic Co-operation and Development (OECD) for quantitative structure–activity relationship (QSAR) analysis. The structure–toxicity relationships that emerged from both classification and regression-based models were in accordance with the findings of some previous studies. The models suggested that the cytotoxicity of ionic liquids is dependent on the cationic surfactant action, long alkyl side chains, cationic lipophilicity as well as aromaticity, the presence of a dialkylamino substituent at the 4-position of the pyridinium nucleus and a bulky anionic moiety. The models have been transparently presented in the form of equations, thus allowing their easy transferability in accordance with the OECD guidelines. The models have also been subjected to rigorous validation tests proving their predictive potential and can hence be used for designing novel and “greener” ionic liquids. The major strength of the present study lies in the use of a diverse and large dataset, use of simple reproducible descriptors and compliance with the OECD norms. [ABSTRACT FROM AUTHOR]

Chemicals used in our daily life show different toxic effects to the aquatic and terrestrial species and thus hamper the ecological balance. In the present time, amphibians are one of them, which are threatened to be extinct. Quantitative structure-activity relationship (QSAR) is an useful tool for prediction involving less time, money and manpower without requiring any animal experiments to assess the unavailable acute toxicity data for the untested molecules. In this study, we have developed QSAR models for ecotoxicity of some waterborne diverse aromatic compounds on an amphibian species Rana japonica (Japanese brown frog) employing Genetic Algorithm (GA) for variable selection followed by Partial Least Squares (PLS) regression method following recommendations of the Organization for Economic Co-operation and Development (OECD) for QSAR model development. Double cross-validation (DCV) followed by Best Subset Selection (BSS) were employed to select suitable models. The models displayed promising statistical quality in terms of R2 (= 0.837–0.841), Q2 LOO (= 0.782–0.787), R2 pred or Q2 F1 (= 0.802–0.82) and some other internal and external validation metrics for tadpoles of Rana japonica (N Training = 44, N Test = 14). These models can be applied for data gap filling for a new untested compound falling within the applicability domain (AD) of the models. • We developed QSAR models by employing LC 50 against Rana japonica as the endpoint. • The models were developed by using 2D descriptors for a set of diverse waterborne aromatic molecules. • Several validation metrics ensure robustness, and predictive capacity of our models. • Our models can be used to fill data gap by toxicity prediction. [ABSTRACT FROM AUTHOR]

Quantitative structure–activity/property/toxicity relationship (QSAR/QSPR/QSTR) modeling has been used in medicinal chemistry, material sciences, environmental fate modeling, risk assessment and computational toxicology for a long time. The Organization for Economic Co-operation and Development (OECD) has recommended that for application of validated QSAR models for prediction of new data points, there is a strict requirement of defining the applicability domain (AD) according to the Principle 3 . The AD is a theoretical region in chemical space encompassing both the model descriptors and modeled response which allows one to estimate the uncertainty in the prediction of a particular compound based on how similar it is to the training compounds employed in the model development. The AD is an important tool for reliable application of QSAR models, while characterization of interpolation space is significant in defining the AD. An attempt is made here to suggest a simple method for defining the X-outliers (in the case of the training set) and identifying the compounds that reside outside the AD (in the case of the test set) employing the basic theory of the standardization approach. Further, a standalone application named “Applicability domain using standardization approach” (available at http://dtclab.webs.com/software-tools and http://teqip.jdvu.ac.in/QSAR_Tools/ ) has been developed. The present study reports that the web application can be easily used for identification of the X-outliers for training set compounds and detection of the test compounds residing outside the AD using the descriptor pool of the training and test sets. [ABSTRACT FROM AUTHOR]

Predictive toxicology using chemometric tools can be very useful in order to fill the data gaps for ionic liquids (ILs) with limited available experimental toxicity information, in view of their growing industrial uses. Though originally promoted as green chemicals, ILs have now been shown to possess considerable toxicity against different ecological endpoints. Against this background, quantitative structure-activity relationship (QSAR) models have been developed here for the toxicity of ILs against the green algae Scenedesmus vacuolatus using computed descriptors with definite physicochemical meaning. The final models emerged from E-state indices, extended topochemical atom (ETA) indices and quantum topological molecular similarity (QTMS) indices. The developed partial least squares models support the established mechanism of toxicity of ionic liquids in terms of a surfactant action of cations and chaotropic action of anions. The models have been developed within the guidelines of the Organization of Economic Co-operation and Development (OECD) for regulatory QSAR models, and they have been validated both internally and externally using multiple strategies and also tested for applicability domain. A preliminary attempt has also been made, for the first time, to develop interspecies quantitative toxicity-toxicity relationship (QTTR) models for the algal toxicity of ILs with Daphnia toxicity, which should be interesting while predicting toxicity of ILs for an endpoint when the data for the other are available. [ABSTRACT FROM AUTHOR]

Benzodiazepines have been widely used therapeutically for their ability to act as tranquilizers, sedative-hypnotics, antiepileptics and frequently prescribed to women during pregnancy for managing preeclampsia or eclampsia. The present report deals with quantitative structure–toxicity relationship (QSTR) modelling of a series of benzodiazepines, in the context of the 3R concept, to provide an insight into the main structural fragments that impart toxicity to these molecules. Three differentin silicotechniques, namely descriptor-based QSTR, group-based QSTR and 3D-toxicophore mapping, were employed to obtain statistically significant models. Multiplein silicomodels made it possible to reach a unified conclusion regarding the structural fragments and features responsible for the toxicity and provide consensus predictions which can be effectively utilised to design and predict less toxic new benzodiazepines. [ABSTRACT FROM AUTHOR]

The increase in number of cancer-affected patients worldwide has amplified the need for the development of new anticancer drug-like molecules. In this background, the quantitative structure–activity relationship (QSAR) has proved to be an efficient tool for the drug-designing process. In this work, thein silicotechnique is applied to study the anticancer activity of two different sets of triphenyl methyl (TPM) derivatives on SK-MEL 5 cell line. Chemometric tools such as genetic partial least squares (G/PLS) algorithm was used for phenethylamine andd-phenylalanine TPM derivatives, whereas genetic function approximation technique was utilised for phosphonate and phosphonochloridate TPM derivatives in development of QSAR models. The calculated descriptors represent the influence of geometry, topology, molecular shape and polarisability of the molecules on the anticancer activity of TPM derivatives. Internal and external validation tests of the models provide satisfactory results (R2 = 0.823, 0.880,Q2 = 0.732, 0.771, = 0.644, 0.716) proving the fitness and predictivity of the model in compliance with Organization for Economic Co-operation and Development principles. The developed models can be effectively utilised further for the prediction of anticancer activity of new compounds that fall within the domain of applicability of the referred models. [ABSTRACT FROM AUTHOR]

Water solubility of ionic liquids (ILs) allows their dispersion into aquatic systems and raises concerns on their pollutant potential. Again, lipophilicity can contribute to the toxicity of ILs due to increased ability of the compounds to cross lipoidal bio-membranes. In the present work, we have performed statistical model development for toxicity of a set of ionic liquids to Daphnia magna, a widely accepted model organism for toxicity testing, using computed lipophilicity, atom-type fragment, quantum topological molecular similarity (QTMS) and extended topochemical atom (ETA) descriptors. The models have been developed and validated in accordance with the Organization for Economic Co-operation and Development (OECD) guidelines for quantitative structure-activity relationships (QSARs). The best partial least squares (PLS) model outperforms the previously reported multiple linear regression (MLR) model in statistical quality and predictive ability (R2=0.955, Q2=0.917, R pred 2 0.848. In this work, the ETA descriptors show importance of branching and aromaticity while the QTMS descriptor ellipticity efficiently shows which compounds are influential in the data set, with reference to the model. While obvious importance of lipophilicity is evident from the models, the best model clearly shows the importance of aromaticity suggesting that more lipophilic ILs with less toxicity may be designed by avoiding aromaticity, nitrogen atoms and increasing branching in the cationic structure. The developed quantitative models are in consonance with the recent hypothesis of importance of aromaticity for toxicity of ILs. [ABSTRACT FROM AUTHOR]