QSTR with extended topochemical atom (ETA) indices. 16. Development of predictive classification and regression models for toxicity of ionic liquids towards Daphnia magna

Improving predictions of compound amenability for liquid chromatography-mass spectrometry to enhance non-targeted analysis

Mass-spectrometry-based non-targeted analysis (NTA), in which mass spectrometric signals are assigned chemical identities based on a systematic collation of evidence, is a growing area of interest for toxicological risk assessment. Successful NTA results in better identification of potentially hazardous pollutants within the environment, facilitating the development of targeted analytical strategies to best characterize risks to human and ecological health. A supporting component of the NTA process involves assessing whether suspected chemicals are amenable to the mass spectrometric method, which is necessary in order to assign an observed signal to the chemical structure. Prior work from this group involved the development of a random forest model for predicting the amenability of 5517 unique chemical structures to liquid chromatography-mass spectrometry (LC-MS). This work improves the interpretability of the group's prior model of the same endpoint, as well as integrating 1348 more data points across negative and positive ionization modes. We enhance interpretability by feature engineering, a machine learning practice that reduces the input dimensionality while attempting to preserve performance statistics. We emphasize the importance of interpretable machine learning models within the context of building confidence in NTA identification. The novel data were curated by the labeling of compounds as amenable or unamenable by expert curators, resulting in an enhanced set of chemical compounds to expand the applicability domain of the prior model. The balanced accuracy benchmark of the newly developed model is comparable to performance previously reported (mean CV BA is 0.84 vs. 0.82 in positive mode, and 0.85 vs. 0.82 in negative mode), while on a novel external set, derived from this work's data, the Matthews correlation coefficients (MCC) for the novel models are 0.66 and 0.68 for positive and negative mode, respectively. Our group's prior published models scored MCC of 0.55 and 0.54 on the same external sets. This demonstrates appreciable improvement over the chemical space captured by the expanded dataset. This work forms part of our ongoing efforts to develop models with higher interpretability and higher performance to support NTA efforts.

Chronic aquatic toxicity assessment of diverse chemicals on Daphnia magna using QSAR and chemical read-across

We have modeled here chronic Daphnia toxicity taking pNOEC (negative logarithm of no observed effect concentration in mM) and pEC50 (negative logarithm of half-maximal effective concentration in mM) as endpoints using QSAR and chemical read-across approaches. The QSAR models were developed by strictly obeying the OECD guidelines and were found to be reliable, predictive, accurate, and robust. From the selected features in the developed models, we have found that an increase in lipophilicity and saturation, the presence of electrophilic or electronegative or heavy atoms, the presence of sulphur, amine, and their related functionality, an increase in mean atomic polarizability, and higher number of (thio-) carbamates (aromatic) groups are responsible for chronic toxicity. Therefore, this information might be useful for the development of environmentally friendly and safer chemicals and data-gap filling as well as reducing the use of identified toxic chemicals which have chronic toxic effects on aquatic ecosystems. Approved classes of drugs from DrugBank databases and diverse groups of chemicals from the Chemical and Product Categories (CPDat) database were also assessed through the developed models.

Identification of a Family of Glycoside Derivatives Biologically Active against Acinetobacter baumannii and Other MDR Bacteria Using a QSPR Model

As the rate of discovery of new antibacterial compounds for multidrug-resistant bacteria is declining, there is an urge for the search for molecules that could revert this tendency. Acinetobacter baumannii has emerged as a highly virulent Gram-negative bacterium that has acquired multiple resistance mechanisms against antibiotics and is considered of critical priority. In this work, we developed a quantitative structure-property relationship (QSPR) model with 592 compounds for the identification of structural parameters related to their property as antibacterial agents against A. baumannii. QSPR mathematical validation (R2 = 70.27, RN = −0.008, a(R2) = 0.014, and δK = 0.021) and its prediction ability (Q2LMO = 67.89, Q2EXT = 67.75, a(Q2) = −0.068, δQ = 0.0, rm2¯ = 0.229, and Δrm2 = 0.522) were obtained with different statistical parameters; additional validation was done using three sets of external molecules (R2 = 72.89, 71.64 and 71.56). We used the QSPR model to perform a virtual screening on the BIOFACQUIM natural product database. From this screening, our model showed that molecules 32 to 35 and 54 to 68, isolated from different extracts of plants of the Ipomoea sp., are potential antibacterials against A. baumannii. Furthermore, biological assays showed that molecules 56 and 60 to 64 have a wide antibacterial activity against clinically isolated strains of A. baumannii, as well as other multidrug-resistant bacteria, including Staphylococcus aureus, Escherichia coli, Klebsiella pneumonia, and Pseudomonas aeruginosa. Finally, we propose 60 as a potential lead compound due to its broad-spectrum activity and its structural simplicity. Therefore, our QSPR model can be used as a tool for the investigation and search for new antibacterial compounds against A. baumannii.

Toward the Proactive Design of Sustainable Chemicals: Ionic Liquids as a Prime Example

The tailorable and often unique properties of ionic liquids (ILs) drive their implementation into a broad variety of seminal technologies. The modular design of ILs allows in this context a proactive selection of structures that favor environmental sustainability─ideally without compromising their technological performance. To achieve this objective, the whole life cycle must be taken into account and various aspects considered simultaneously. In this review, we discuss how the structural design of ILs affects their environmental impacts throughout all stages of their life cycles and scrutinize the available data in order to point out knowledge gaps that need further research activities. The design of more sustainable ILs starts with the selection of the most beneficial precursors and synthesis routes, takes their technical properties and application specific performance into due account, and considers its environmental fate particularly in terms of their (eco)toxicity, biotic and abiotic degradability, mobility, and bioaccumulation potential. Special emphasis is placed on reported structure-activity relationships and suggested mechanisms on a molecular level that might rationalize the empirically found design criteria.

Review of the toxic effects of ionic liquids

Interest in ionic liquids (ILs), called green or designer solvents, has been increasing because of their excellent properties such as thermal stability and low vapor pressure; thus, they can replace harmful organic chemicals and help several industrial fields e.g., energy-storage materials production and biomaterial pretreatment. However, the claim that ILs are green solvents should be carefully considered from an environmental perspective. ILs, given their minimal vapor pressure, may not directly cause atmospheric pollution. However, they have the potential to cause adverse effects if leaked into the environment, for instance if they are spilled due to human mistakes or technical errors. To estimate the risks of ILs, numerous ILs have had their toxicity assessed toward several micro- and macro-organisms over the past few decades. Since the toxic effects of ILs depend on the method of estimating toxicity, it is necessary to briefly summarize and comprehensively discuss the biological effects of ILs according to their structure and toxicity testing levels. This can help simplify our understanding of the toxicity of ILs. Therefore, in this review, we discuss the key findings of toxicological information of ILs, collect some toxicity data of ILs to different species, and explain the influence of IL structure on their toxic properties. In the discussion, we estimated two different sensitivity values of toxicity testing levels depending on the experiment condition, which are theoretical magnitudes of the inherent sensitivity of toxicity testing levels in various conditions and their changes in biological response according to the change in IL structure. Finally, some perspectives, future research directions, and limitations to toxicological research of ILs, presented so far, are discussed.

The toxicological mechanism of two typical imidazole ionic liquids in textile industry on Isatis tinctoria

Ionic liquids (ILs¹) which are called "green solvents", are used widely in the textile industry as adjuvants due to their many advantages. However, their persistent residues may cause ecotoxicity. The aim of the study is to explore the toxicity of different anions on imidazole ILs and their toxicological mechanism. For the experiments 1-butyl-3-methylimidazole tetrafloroborate ([C₄mim]BF₄) and 1- butyl -3-methylimidazolium chloride ([C₄mim]Cl) were selected to study their toxic effects on Isatis tinctoria. ILs may affect the germination rate. Fresh weight, dry weight and Hill reaction activity decreased continuously with increasing of IL concentrations, showing an effect-dose relationship. Transmission electron microscopy (TEM) revealed that cell walls were fuzzy, starch granules had accumulated and the chloroplast structure was damaged. These changes will affected the function and electron transport efficiency of photosystemⅡ. Superoxide anion accumulation stimulated the activity of antioxidant enzymes (SOD, POD, CAT) and caused lipid peroxidation as well as an increased malondialdehyde content. ILs also reduced indirubin and total flavonoids contents, which reduced the pharmacological efficacy of Isatis tinctoria. This is demonstrated by three-dimensional fluorescence chromatogram. [C₄mim]Cl was more toxic than [C₄mim]BF₄. ILs caused toxic effects to Isatis tinctoria. The ecological toxicity of ILs should be considered when using them as additives in the textile industry.

Robust classification-based molecular modelling of diverse chemical entities as potential SARS-CoV-2 3CLpro inhibitors: theoretical justification in light of experimental evidences

COVID-19 is the most unanticipated incidence of 2020 affecting the human population worldwide. Currently, it is utmost important to produce novel small molecule anti-SARS-CoV-2 drugs urgently that can save human lives globally. Based on the earlier SARS-CoV and MERS-CoV infection along with the general characters of coronaviral replication, a number of drug molecules have been proposed. However, one of the major limitations is the lack of experimental observations with different drug molecules. In this article, 70 diverse chemicals having experimental SARS-CoV-2 3CL^proinhibitory activity were accounted for robust classification-based QSAR analysis statistically validated with 4 different methodologies to recognize the crucial structural features responsible for imparting the activity. Results obtained from all these methodologies supported and validated each other. Important observations obtained from these analyses were also justified with the ligand-bound crystal structure of SARS-CoV-2 3CL^pro enzyme. Our results suggest that molecules should contain a 2-oxopyrrolidine scaffold as well as a methylene (hydroxy) sulphonic acid warhead in proper orientation to achieve higher inhibitory potency against SARS-CoV-2 3CL^pro. Outcomes of our study may be able to design and discover highly effective SARS-CoV-2 3CL^pro inhibitors as potential anticoronaviral therapy to crusade against COVID-19.

A simple and robust model to predict the inhibitory activity of α-glucosidase inhibitors through combined QSAR modeling and molecular docking techniques

Quantitative structure-activity relationships (QSAR) and molecular docking studies have been performed on a series of 35 α-glucosidase inhibitory derivatives. The QSAR models have been developed by genetic algorithm-multiple linear regression (GA-MLR) and least squares-support vector machine (LS-SVM) methods to correlate the conformational descriptors to the inhibitory activity. The obtained models with 5 descriptors were validated and illustrated to be statistically significant. They had desirable prediction based on squared correlation coefficient (R²), cross-validated correlation coefficient (Q²), root-mean-squares error (RMSE) and Fisher (F) parameters (R² = 0.951, Q² = 0.931, RMSE = 0.121, and F = 114.629 for GA-MLR model, and R² = 0.989, Q² = 0.987, RMSE = 0.056 and F = 543.754 for LS-SVM model). The crucial descriptor named DELS was explored to have the highest correlation with the inhibitory activity and thus has been chosen to build a simple model. The QSAR model developed with this mono-descriptor showed appropriate results of the predicted model using LS-SVM method (R² = 0.888, Q² = 0.872, RMSE = 0.185 and F = 221.459). Also, molecular docking which focuses on the interaction between ligands and α-glucosidase in the protein active site considered different binding positions to find the best binding mode. It helped the QSAR study to propose more comprehensive details of the compounds structures and was used to design more active compounds. The most active designed compound had a high inhibitory activity of 9.22 that can be proposed for the treatment of diabetes type 2.

QSAR modeling of algal low level toxicity values of different phenol and aniline derivatives using 2D descriptors

The deposition of different types of phenol and aniline derivatives in the aquatic environment leads to toxicity to living organisms. Under such condition, evaluation of these toxicants is very much important. Due to non-availability of sufficient experimental data as well as sufficient number of Quantitative Structure-Activity Relationship (QSAR) models for the low level toxicity values for such pollutants, we have employed here the partial least squares (PLS) regression for the development of robust and predictive QSAR models using low level toxicity values against algal species. Here, we have used both Extended Topochemical Atom (ETA) and non-ETA indices as 2D descriptors for model development. The statistical validation parameters ensure the robustness and the predictivity of the developed models. From the insights of the final PLS models, it can be concluded that presence of nitro groups (in the ortho position to phenolic hydroxyl group increasing intramolecular hydrogen bonding capacity), presence of chlorine substituents (influencing lipophilicity) especially at the para position, oxygen and nitrogen at the topological distance three, aliphatic side chain (contributing to hydrophobicity), molecules with large size atoms and higher molecular bulk will increase the toxicity towards the algal species. On the other hand, the phenol ring without any substituent or with a polar substituent (like amino group), presence of chlorine at ortho-ortho or ortho-para position, absence of nitro group, presence of chlorine and oxygen at the topological distance three, presence of lower number of aliphatic groups will decrease the toxic effect towards the algal species.

QSAR modeling with ETA indices for cytotoxicity and enzymatic activity of diverse chemicals

The discharge of huge amount of chemicals from industries into the environment has led to toxicity towards different living species. Therefore, risk assessment of these chemicals is essential. In order to comply with the ethical issues, in this present work, we have developed quantitative structure-activity relationship (QSAR) models for cytotoxicity against GFS (goldfish scale) tissue (Crassius auratus) and enzymatic activity against PLHC-1 cell line (topminnow hepatoma cell line) (Poeciliopsis lucida). The final models were developed by means of PLS (Partial Least Squares) regression method applying only ETA (extended topochemical atom) descriptors. The results obtained from various validation parameters (obtained from the both datasets) suggested that the developed models are statistically robust and predictive. From the insights obtained from the models developed from the Neutral Red dye (NR) dataset, it can be concluded that presence of bulky atoms, unsaturation, branching and hetero atoms (most importantly N, Cl) enhance the cytotoxicity towards the Goldfish scale tissue. On the other hand, in case of the Ethoxyresorufin-O-deethylase (EROD) dataset, presence of higher electronegative atoms (O, Cl), polycyclic aromatic hydrocarbons (PAHs) with more number of rings and absence of polar groups and hydrogen bond acceptors enhance enzymatic activity of the PLHC-1 cell line.

Application of general toxic effects of ionic liquids to predict toxicities of ionic liquids to Spodoptera frugiperda 9, Eisenia fetida, Caenorhabditis elegans, and Danio rerio

Modeling for the toxicity of ionic liquids (ILs) is necessary to fill data gaps for untested chemicals and to understand the relevant mechanisms at the molecular level. In order for many researchers to easily predict toxicity and/or develop some prediction model, simple method(s) based on a single parameter should be proposed. Therefore, previously our group developed a comprehensive toxicity prediction model with unified linear free-energy relationship descriptors to address the single parameter for predicting the toxicities, as follows (Cho et al., 2016b). Log 1/toxicity in the unit of mM= (2.254 E_c - 2.545 S_c + 0.646 A_c - 1.471 B_c + 1.650 V_c + 2.917 J⁺ - 0.201 E_a + 0.418 V_a + 0.131 J^-) - 0.709. It is considered that the model can calculate the general toxicological effect of ILs in parenthesis, as it was developed on the basis of numerous toxic effects i.e., 58 toxicity testing methods and about 1600 data points. In order to check the hypothesis, the values calculated by the model were correlated with four different datasets from insect cell line (Spodoptera frugiperda 9), earthworm (Eisenia fetida), nematode (Caenorhabditis elegans), and fish (Danio rerio). The results clearly showed that the calculated values are in good agreement with each dataset. In the case of S. frugiperda 9 cells, the calculated parameters were correlated with log1/LC₅₀ values, measured after 24 h and 48 h incubation, in R² of 0.67 and 0.88, respectively. The R² values for the earthworm, nematode, and fish were 0.88, 0.96, and 0.94-0.95, respectively. This study confirmed that the comprehensive model can be simply and accurately used to predict toxicity of ILs.

Quantitative Structure-Toxicity Relationship analysis of combined toxic effects of lignocellulose-derived inhibitors on bioethanol production

This study performed a Quantitative Structure-Toxicity Relationship (QSTR) model to evaluate the combined toxicity of lignocellulose-derived inhibitors on bioethanol production. Compared with all the control groups, the combined systems exhibited lower conductivity values, higher oxidation-reduction potential values, as well as maximum inhibition rates. These results indicated that the presence of combined inhibitors had a negative effect on the bioethanol fermentation process. Meanwhile, QSTR model was excellent for evaluating the combined toxic effects at lower ferulic acid concentration (([1:4] × IC50)) and (([1:1] × IC50)), due to higher R² values (0.994 and 0.762), lower P values (0.000 and 0.023) and relative error values (less than 30%). The obtained results also showed that the combined toxic effects of ferulic acid and representative lignocellulose-derived inhibitors were relevant to different molecular descriptors. Meanwhile, the interactions of combined inhibitors were weaker when ferulic acid was at low concentration ([1:4] × IC50).

Ecotoxicological QSAR modeling of endocrine disruptor chemicals

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.

The “ETA” Indices in QSAR/QSPR/QSTR Research

Descriptors are one of the most essential components of predictive Quantitative Structure-Activity/Property/Toxicity Relationship (QSAR/QSPR/QSTR) modeling analysis, as they encode chemical information of molecules in the form of quantitative numbers, which are used to develop mathematical correlation models. The quality of a predictive model not only depends on good modeling statistics, but also on the extraction of chemical features. A significant amount of research since the beginning of QSAR analysis paradigm has led to the introduction of a large number of predictor variables or descriptors. The Extended Topochemical Atom (ETA) indices, developed by the authors' group, successfully address the aspects of molecular topology, electronic information, and different types of bonded interactions, and have been extensively employed for the modeling of different types of activity/property and toxicity endpoints. This chapter provides explicit information regarding the basis, algorithm, and applicability of the ETA indices for a predictive modeling paradigm.

Comprehensive approach for predicting toxicological effects of ionic liquids on several biological systems using unified descriptors

The challenge and opportunity for design of environmentally-benign ionic liquids (ILs) would start from prediction of their toxicological effects on several endpoints solely based on the structural formulas. Especially, a comprehensive yet simple equation able to predict several biological responses to IL toxicity is of much advantage. Therefore, based on 50 toxicity testing systems on ILs a comprehensively approachable prediction method was developed. For the modelling, approximately 1600 toxicity values measured by several biological systems and an amended linear free energy relationship (LFER) model were used. Since the toxicological activities of an IL could be differently described according to sensitivity of toxicity testing systems, the sensitivity of each of toxicity testing systems was also estimated in the modelling. By statistical analysis with the calculated descriptors, a LFER model was built. Also the sensitivity value of each system on the basis of the comprehensively approachable model was numerically estimated. In results, it was observed that the combination of single model and sensitivity terms was able to predict each of 50 toxicological effects of ILs with R(2) of 0.593~0.978, and SE of 0.098~0.699 log unit, and the total data set with R(2) of 0.901 and SE of 0.426 log unit.

Interpretation of toxicological activity of ionic liquids to acetylcholinesterase inhibition via in silico modelling

For designing environmentally friendly ionic liquids (ILs), their structural effects on the toxicity should be interpreted via modelling based on the quantitative-structure-activity-relationship (QSAR) concept. For the purpose, QSAR models for predicting IL toxicity in acetylcholinesterase activity were developed by using linear free-energy relationship (LFER) descriptors, whose chemical meanings are well defined. These are excess molar refraction (Ec or a), dipolarity/polarizability (Sc or a), H-bonding acidity (Ac or a), H-bonding basicity (Bc or a), McGowan volume (Vc or a), and ionic interactions of cation (J(+)) and anion (J(-)). Since the experimentally determined LFER descriptors are not available, we calculated them based on density functional theory, conductor-like screening model and the open-source software, obprop. The toxicity values of imidazolium- and pyridinium-based ILs could be predicted by a combination of four descriptors (Ac, Bc, Vc and Sa) with an R(2) of 0.828, and (Ec, Ac, Ea and Sa) with an R(2) of 0.879, respectively. In prediction study using the overall dataset containing various IL structures, the six calculated terms (Ec, Sc, Ac, J(+), Ea, and Sa) were selected and correlated with the observed toxicity values in R(2) of 0.748 for the training set, R(2) of 0.711 for the test set and R(2) of 0.655 for external validation set. And this study explains how the selected terms are contributing to the prediction models, and their chemical meanings were understood.

Correlating toxicological effects of ionic liquids on Daphnia magna with in silico calculated linear free energy relationship descriptors

In silico prediction model for toxicological effects of ionic liquids (ILs) is useful to understand ILs' toxicological interactions and to design environmentally benign IL structures. Actually, it is essential since the types of ILs are extremely numerous. Accordingly, prediction models were developed in this study. For the modelling, well-defined linear free energy relationship (LFER) descriptors - i.e. excess molar refraction (E), dipolarity/polarizability (S), H-bonding acidity (A), H-bonding basicity (B), McGowan volume (V), cation interaction (J(+)) and anion interaction (J(-)) - were in silico calculated using density functional theory and conductor-like screening model. These descriptors were then correlated with the toxicological values of ILs to Daphnia magna. First, a model established by Hoover et al. (2007) using measured LFER descriptors of 97 neutral compounds was applied to the prediction of ILs' toxicity. As expected, the model by Hoover et al. (2007) needs to be amended for ILs. To that end, the difference in toxicological interactions between neutral compounds and ILs was addressed by additional single J(+) or five LFER descriptors of cation i.e. Ec, Sc, Bc, Vc, and J(+). Secondly, a prediction model for only ILs was developed by using the three LFER descriptors Ec, Bc, and J(+). The model had a reasonable predictability and robustness of R(2) = 0.880 for the training set, 0.848 for the test set, and 0.867 for the overall set. The established models can be used to design environmentally benign IL structures and to reduce labour, danger, time, and materials compared to the experiment-based study.

Development of Quantitative Structure-Activity Relationship Models for Predicting Chronic Toxicity of Substituted Benzenes to Daphnia Magna

The chronic toxicity of anthropogenic molecules such as substituted benzenes to Daphnia magna is a basic eco-toxicity parameter employed to assess their environmental risk. As the experimental methods are laborious, costly, and time-consuming, development in silico models for predicting the chronic toxicity is vitally important. In this study, on the basis of five molecular descriptors and 48 compounds, a quantitative structure-property relationship model that can predict the chronic toxicity of substituted benzenes were developed by employing multiple linear regressions. The correlation coefficient (R (2)) and root-mean square error (RMSE) for the training set were 0.836 and 0.390, respectively. The developed model was validated by employing 10 compounds tested in our lab. The R EXT (2) and RMSE EXT for the validation set were 0.736 and 0.490, respectively. To further characterizing the toxicity mechanism of anthropogenic molecules to Daphnia, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were developed.

A QSPR study on the liquid crystallinity of five-ring bent-core molecules using decision trees, MARS and artificial neural networks

We present an approach for the prediction of liquid crystallinity of five-ring bent-core molecules. Reported classifiers can be also used for the estimation of influence of structural modifications on LC phase formation and its stability.

Acute toxicity of benzophenone-type UV filters for Photobacterium phosphoreum and Daphnia magna: QSAR analysis, interspecies relationship and integrated assessment

The hazardous potential of benzophenone (BP)-type UV filters is becoming an issue of great concern due to the wide application of these compounds in many personal care products. In the present study, the toxicities of BPs to Photobacterium phosphoreum and Daphnia magna were determined. Next, density functional theory (DFT) and comparative molecular field analysis (CoMFA) descriptors were used to obtain more detailed insight into the structure - activity relationships and to preliminarily discuss the toxicity mechanism. Additionally, the sensitivities of the two organisms to BPs and the interspecies toxicity relationship were compared. Moreover, an approach for providing a global index of the environmental risk of BPs to aquatic organisms is proposed. The results demonstrated that the mechanism underlying the toxicity of BPs to P. phosphoreum is primarily related to their electronic properties, and the mechanism of toxicity to D. magna is hydrophobicity. Additionally, D. magna was more sensitive than P. phosphoreum to most of the BPs, with the exceptions of the polyhydric BPs. Moreover, comparisons with published data revealed a high interspecies correlation coefficient among the experimental toxicity values for D. magna and Dugesia japonica. Furthermore, hydrophobicity was also found to be the most important descriptor of integrated toxicity. This investigation will provide insight into the toxicity mechanisms and useful information for assessing the potential ecological risk of BP-type UV filters.

Assessment of the electronic structure and properties of trichothecene toxins using density functional theory

A comprehensive quantum chemical study was carried out on 35 type A and B trichothecenes and biosynthetic precursors, including selected derivatives of deoxynivalenol and T-2 toxin. Quantum chemical properties, Natural Bond Orbital (NBO) analysis, and molecular parameters were calculated on structures geometry optimized at the B3LYP/6-311+G** level. Type B trichothecenes possessed significantly larger electrophilicity index compared to the type A trichothecenes studied. Certain hydroxyl groups of deoxynivalenol, nivalenol, and T-2 toxin exhibited considerable rotation during molecular dynamics simulations (5 ps) at the B3LYP/6-31G** level in implicit aqueous solvent. Quantitative structure activity relationship (QSAR) models were developed to evaluate toxicity and detection using genetic algorithm, principal component, and multilinear analyses. The models suggest electronegativity and several 2-dimensional topological descriptors contain important information related to trichothecene cytotoxicity, phytotoxicity, immunochemical detection, and cross-reactivity.

Assessment of bromide-based ionic liquid toxicity toward aquatic organisms and QSAR analysis

The toxicities of 24 bromide-based ionic liquids (Br-ILs) towards Vibrio fischeri (V. fischeri) and Daphnia magna (D. magna) were determined. These Br-ILs are composed of a bromide ion and a generic cation (i.e., pyrrolidinium, piperidinium, pyridinium or imidazolium) with different alkyl side chains. QSAR models with relatively high correlation coefficients, R(2), of 0.954 and 0.895 were developed for V. fischeri and D. magna. The model for V. fischeri indicated that the Br-IL toxicity towards V. fischeri was negatively correlated with the energy of the lowest unoccupied molecular orbitals (ELUMO) which reflects the electron affinities (EAs) and positively correlated with the volumes of Br-IL cations. For the D. magna model, the Br-IL toxicity was positively correlated with the dipole moment (μ) and negatively correlated with the total energy (TE) that is highly correlated with the molecular volume (V). For Br-ILs with the same cation ring, the toxicity increased as the length of the alkyl chains increased. For the same alkyl chain length, the toxicity order for V. fischeri was pyridinium>imidazolium>piperidinium>pyrrolidinium, except for those containing octyl side chains, while the toxicity ranking for D. magna was imidazolium~pyridinium>piperidinium>pyrrolidinium.

The “ETA” Indices in QSAR/QSPR/QSTR Research

Descriptors are one of the most essential components of predictive Quantitative Structure-Activity/Property/Toxicity Relationship (QSAR/QSPR/QSTR) modeling analysis, as they encode chemical information of molecules in the form of quantitative numbers, which are used to develop mathematical correlation models. The quality of a predictive model not only depends on good modeling statistics, but also on the extraction of chemical features. A significant amount of research since the beginning of QSAR analysis paradigm has led to the introduction of a large number of predictor variables or descriptors. The Extended Topochemical Atom (ETA) indices, developed by the authors' group, successfully address the aspects of molecular topology, electronic information, and different types of bonded interactions, and have been extensively employed for the modeling of different types of activity/property and toxicity endpoints. This chapter provides explicit information regarding the basis, algorithm, and applicability of the ETA indices for a predictive modeling paradigm.

Toxicity of ionic liquids: database and prediction via quantitative structure-activity relationship method

A comprehensive database on toxicity of ionic liquids (ILs) is established. The database includes over 4000 pieces of data. Based on the database, the relationship between IL's structure and its toxicity has been analyzed qualitatively. Furthermore, Quantitative Structure-Activity relationships (QSAR) model is conducted to predict the toxicities (EC50 values) of various ILs toward the Leukemia rat cell line IPC-81. Four parameters selected by the heuristic method (HM) are used to perform the studies of multiple linear regression (MLR) and support vector machine (SVM). The squared correlation coefficient (R(2)) and the root mean square error (RMSE) of training sets by two QSAR models are 0.918 and 0.959, 0.258 and 0.179, respectively. The prediction R(2) and RMSE of QSAR test sets by MLR model are 0.892 and 0.329, by SVM model are 0.958 and 0.234, respectively. The nonlinear model developed by SVM algorithm is much outperformed MLR, which indicates that SVM model is more reliable in the prediction of toxicity of ILs. This study shows that increasing the relative number of O atoms of molecules leads to decrease in the toxicity of ILs.

Predictive modeling studies for the ecotoxicity of ionic liquids towards the green algae Scenedesmus vacuolatus

Hazardous potential of ionic liquids is becoming an issue of high concern with increasing application of these compounds in various industrial processes. Predictive toxicological modeling on ionic liquids provides a rational assessment strategy and aids in developing suitable guidance for designing novel analogues. The present study attempts to explore the chemical features of ionic liquids responsible for their ecotoxicity towards the green algae Scenedesmus vacuolatus by developing mathematical models using extended topochemical atom (ETA) indices along with other categories of chemical descriptors. The entire study has been conducted with reference to the OECD guidelines for QSAR model development using predictive classification and regression modeling strategies. The best models from both the analyses showed that ecotoxicity of ionic liquids can be decreased by reducing chain length of cationic substituents and increasing hydrogen bond donor feature in cations, and replacing bulky unsaturated anions with simple saturated moiety having less lipophilic heteroatoms.

Classification study of solvation free energies of organic molecules using machine learning techniques

Classification models to predict the solvation free energies of organic molecules were developed using decision tree, random forest and support vector machine approaches and with MACCS fingerprints, MOE and PaDEL descriptors.