On some novel extended topochemical atom (ETA) parameters for effective encoding of chemical information and modelling of fundamental physicochemical properties

Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals

Environmental chemicals and contaminants cause a wide array of harmful implications to terrestrial and aquatic life which ranges from skin sensitization to acute oral toxicity. The current study aims to assess the quantitative skin sensitization potential of a large set of industrial and environmental chemicals acting through different mechanisms using the novel quantitative Read-Across Structure-Activity Relationship (q-RASAR) approach. Based on the identified important set of structural and physicochemical features, Read-Across-based hyperparameters were optimized using the training set compounds followed by the calculation of similarity and error-based RASAR descriptors. Data fusion, further feature selection, and removal of prediction confidence outliers were performed to generate a partial least squares (PLS) q-RASAR model, followed by the application of various Machine Learning (ML) tools to check the quality of predictions. The PLS model was found to be the best among different models. A simple user-friendly Java-based software tool was developed based on the PLS model, which efficiently predicts the toxicity value(s) of query compound(s) along with their status of Applicability Domain (AD) in terms of leverage values. This model has been developed using structurally diverse compounds and is expected to predict efficiently and quantitatively the skin sensitization potential of environmental chemicals to estimate their occupational and health hazards.

Artificial Intelligence in Decrypting Cytoprotective Activity under Oxidative Stress from Molecular Structure

Artificial intelligence (AI) is widely explored nowadays, and it gives opportunities to enhance classical approaches in QSAR studies. The aim of this study was to investigate the cytoprotective activity parameter under oxidative stress conditions for indole-based structures, with the ultimate goal of developing AI models capable of predicting cytoprotective activity and generating novel indole-based compounds. We propose a new AI system capable of suggesting new chemical structures based on some known cytoprotective activity. Cytoprotective activity prediction models, employing algorithms such as random forest, decision tree, support vector machines, K-nearest neighbors, and multiple linear regression, were built, and the best (based on quality measurements) was used to make predictions. Finally, the experimental evaluation of the computational results was undertaken in vitro. The proposed methodology resulted in the creation of a library of new indole-based compounds with assigned cytoprotective activity. The other outcome of this study was the development of a validated predictive model capable of estimating cytoprotective activity to a certain extent using molecular structure as input, supported by experimental confirmation.

Exploring quantitative structure-property relationship models for environmental fate assessment of petroleum hydrocarbons

The rate and extent of biodegradation of petroleum hydrocarbons in the different aquatic environments is an important element to address. The major avenue for removing petroleum hydrocarbons from the environment is thought to be biodegradation. The present study involves the development of predictive quantitative structure-property relationship (QSPR) models for the primary biodegradation half-life of petroleum hydrocarbons that may be used to forecast the biodegradation half-life of untested petroleum hydrocarbons within the established models' applicability domain. These models use easily computable two-dimensional (2D) descriptors to investigate important structural characteristics needed for the biodegradation of petroleum hydrocarbons in freshwater (dataset 1), temperate seawater (dataset 2), and arctic seawater (dataset 3). All the developed models follow OECD guidelines. We have used double cross-validation, best subset selection, and partial least squares tools for model development. In addition, the small dataset modeler tool has been successfully used for the dataset with very few compounds (dataset 3 with 17 compounds), where dataset division was not possible. The resultant models are robust, predictive, and mechanistically interpretable based on both internal and external validation metrics (R² range of 0.605-0.959. Q²_(Loo) range of 0.509-0.904, and Q²_F1 range of 0.526-0.959). The intelligent consensus predictor tool has been used for the improvement of the prediction quality for test set compounds which provided superior outcomes to those from individual partial least squares models based on several metrics (Q²_F1 = 0.808 and Q²_F2 = 0.805 for dataset 1 in freshwater). Molecular size and hydrophilic factor for freshwater, frequency of two carbon atoms at topological distance 4 for temperate seawater, and electronegative atom count relative to size for arctic seawater were found to be the most significant descriptors responsible for the regulation of biodegradation half-life of petroleum hydrocarbons.

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.

Prediction of band gap for 2D hybrid organic–inorganic perovskites by using machine learning through molecular graphics descriptors

Molecular graphics descriptors are used to predict the band gap of 2D perovskites.

Prediction of the Partition Coefficient between Adipose Tissue and Blood for Environmental Chemicals: From Single QSAR Models to an Integrated Approach

The adipose tissue:blood partition coefficient is a key-endpoint to predict the pharmacokinetics of chemicals in humans and animals, since other organ:blood affinities can be estimated as a function of this parameter. We performed a search in the literature to select all the available rat in vivo data. This approach resulted into two improvements to existing models: a homogeneous definition of the endpoint and an expanded data collection. The resulting dataset was used to develop QSAR models as a function of linear and non-linear algorithms. Several applicability domain definitions were assessed and the definition corresponding to a good balance between performance and coverage was retained. We assessed the pertinence of combining single models into integrated approaches to increase the accuracy in predictions. The best integrated model outperformed the single models and it was characterized by an external mean absolute error (MAE) equal to 0.26, while preserving an adequate coverage (84 %). This performance is comparable to experimental variability and it highlights the pertinence of the integrated model.

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 the toxicity of pesticides against Americamysis bahia

The widespread use of pesticides has received increasing attention in regulatory agencies because their extensive overuse and various adverse effects on all living organisms. Organizations such as EPA and ECHA have published laws that pesticides should be fully evaluated before bring them to market. In the present study, we evaluated the pesticides toxicity using the Quantitative Structural-Activity Relationship (QSAR) method. The models for the single class pesticides (herbicides, insecticides and fungicides) as well as the general class pesticides (the combined dataset plus some microbicides, molluscicides, etc.) were developed using the Genetic Algorithm and Multiple Linear Regression method. The internal and external validation results suggested that all the obtained models were stable and predictive. According to the modeling descriptors, the lipophilic descriptors contributed positively while all the electrotopological state descriptors showed a negative contribution, their presences in every model verified the conspicuous influence of molecular lipophilicity and hydrophilicity on the pesticides toxicity. However, the influence of topological structure descriptors was different and varies with the physiochemical information they encode. Finally, the models presented in this paper would help assess the pesticides toxicity against Americamysis bahia, shorten test time, and reduce the cost of pesticides risk assessment.

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.

Modeling pesticides toxicity to Sheepshead minnow using QSAR

Nowadays, the environmental risk caused by the widespread use of pesticides and their ubiquitous residuals has received more and more attention in academia and regulatory agencies. Due to the large number of pesticides used in agriculture and their adverse effects on all living organisms and the numerous end-points, it is necessary to employ the in silico tools to quickly highlight hazardous pesticides. In this study, we have evaluated the toxicity of pesticides against Sheepshead minnow with the Quantitative Structure-Activity Relationship (QSAR) approach. The models for the specific-type (insecticides, herbicides and fungicides) as well as the general-type (combing all the specific-type pesticides and some microbicides, nematicides, etc.) pesticides were developed using the Genetic Algorithm and the Multiple Linear Regression method, subsequently validated with various metrics. The validation results suggested that the obtained models were highly robust, externally predictive and characterized by a broad applicability domain. Considering the modeling descriptors, the toxicity of pesticides would increase with the lipophilicity and decrease with the polarity and hydrophilicity. Most electrotopological state descriptors contribute negatively to the toxicity, while the influence of topological structure descriptors mainly depends on the physiochemical information they encode. The models proposed in this paper would be useful in filling the data gaps, prioritizing and then focusing experiments on more hazardous pesticides.

Chemometric modeling to predict air half-life of persistent organic pollutants (POPs)

We have reported here a quantitative structure-property relationship (QSPR) model for prediction of air half-life of organic chemicals using a dataset of 302 diverse organic chemicals employing only two-dimensional descriptors with definite physicochemical meaning in order to avoid the computational complexity for higher dimensional molecular descriptors. The developed model was rigorously validated using the internationally accepted internal and external validation metrics. The final partial least squares (PLS) regression model obtained at three latent variables comprises six simple and interpretable 2D descriptors. The simple and highly robust model with good quality of predictions explains 66% for the variance of the training set (R²) (64% in terms of LOO variance (Q²)) and 76% for test set variance (R²_pred) (prediction quality). This model might be applicable for data gap filling for determination of POPs in the environment, in case of new or untested chemicals falling within the applicability domain of the model. In general, the model indicates that the air half-life of organic chemicals increases with presence of H-bond acceptor atoms, number of halogen atoms and presence of the R-CH-X fragment and lipophilicity, and decreases with presence of a number of halogens on ring C(sp3) (substitution of halogen atoms on a ring).

Development of predictive models for silicone rubber-water partition coefficients of hydrophobic organic contaminants

The silicone rubber passive sampling technique is extensively applied to monitor the aqueous freely dissolved concentration of hydrophobic organic compounds (HOCs). The silicone rubber-water partition coefficient (Ksrw) is an important parameter to accurately measure the concentrations of chemicals using passive sampling devices. In this study, two theoretical linear solvation energy relationship (TLSER) models and a quantitative structure-property relationship (QSPR) model were developed for predicting the Ksrw of HOCs. The 119 model compounds studied here included 31 personal care products, such as musks, UV-filters, and organophosphate flame retardants, as well as "conventional" pollutants, such as polycyclic aromatic hydrocarbons and polychlorinated biphenyls. The statistical parameters indicated that the final QSPR model with seven descriptors for all 119 chemicals had a satisfactory goodness-of-fit (Radj2 = 0.898), robustness (QLOO2 = 0.881) and predictive ability (Qext-F1,2,32 = 0.897-0.926). In comparison, the results of one TLSER model with four descriptors for 113 chemicals (Radj2 = 0.826, QLOO2 = 0.790, Qext-F1,2,32 = 0.805-0.837) and another TLSER model with one descriptor for 5 chemicals (Radj2 = 0.747, QLOO2 = 0.647) were also acceptable. The applicability domains of the obtained models covered chemicals containing hydroxyl, imino groups, carbonyl groups, ether bonds, halogen atoms, sulfur atoms, phosphorus atoms, nitro groups, and cyano groups. In addition, the structural features governing the partition behavior of chemicals between silicone rubber and water were explored through interpretation of appropriate mechanisms.

The use of fast molecular descriptors and artificial neural networks approach in organochlorine compounds electron ionization mass spectra classification

Developing of theoretical tools can be very helpful for supporting new pollutant detection. Nowadays, a combination of mass spectrometry and chromatographic techniques are the most basic environmental monitoring methods. In this paper, two organochlorine compound mass spectra classification systems were proposed. The classification models were developed within the framework of artificial neural networks (ANNs) and fast 1D and 2D molecular descriptor calculations. Based on the intensities of two characteristic MS peaks, namely, [M] and [M-35], two classification criterions were proposed. According to criterion I, class 1 comprises [M] signals with the intensity higher than 800 NIST units, while class 2 consists of signals with the intensity lower or equal than 800. According to criterion II, class 1 consists of [M-35] signals with the intensity higher than 100, while signals with the intensity lower or equal than 100 belong to class 2. As a result of ANNs learning stage, five models for both classification criterions were generated. The external model validation showed that all ANNs are characterized by high predicting power; however, criterion I-based ANNs are much more accurate and therefore are more suitable for analytical purposes. In order to obtain another confirmation, selected ANNs were tested against additional dataset comprising popular sunscreen agents disinfection by-products reported in previous works.

Topological QSAR Modelling of Carboxamides Repellent Activity to Aedes Aegypti

Aedes aegypti vector control is of paramount importance owing to the damages induced by the various severe diseases that these insects may transmit, and the increasing risk of important outbreaks of these pathologies. Search for new chemicals efficient against Aedes aegypti, and devoid of side-effects, which have been associated to the currently most used active substance i. e. N,N-diethyl-m-toluamide (DEET), is therefore an important issue. In this context, we developed various Quantitative Structure Activity Relationship (QSAR) models to predict the repellent activity against Aedes aegypti of 43 carboxamides, by using Multiple Linear Regression (MLR) and various machine learning approaches. The easy computation of the four topological descriptors selected in this study, compared to the CODESSA descriptors used in the literature, and the predictive ability of the here proposed MLR and machine learning models developed using the software QSARINS and R, make the here proposed QSARs attractive. As demonstrated in this study, these models can be applied at the screening level, to guide the design of new alternatives to DEET.

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.

Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Hansen Solubility Parameters Based on 1D and 2D Molecular Descriptors Computed from SMILES String

A new method of Hansen solubility parameters (HSPs) prediction was developed by combining the multivariate adaptive regression splines (MARSplines) methodology with a simple multivariable regression involving 1D and 2D PaDEL molecular descriptors. In order to adopt the MARSplines approach to QSPR/QSAR problems, several optimization procedures were proposed and tested. The effectiveness of the obtained models was checked via standard QSPR/QSAR internal validation procedures provided by the QSARINS software and by predicting the solubility classification of polymers and drug-like solid solutes in collections of solvents. By utilizing information derived only from SMILES strings, the obtained models allow for computing all of the three Hansen solubility parameters including dispersion, polarization, and hydrogen bonding. Although several descriptors are required for proper parameters estimation, the proposed procedure is simple and straightforward and does not require a molecular geometry optimization. The obtained HSP values are highly correlated with experimental data, and their application for solving solubility problems leads to essentially the same quality as for the original parameters. Based on provided models, it is possible to characterize any solvent and liquid solute for which HSP data are unavailable.

Chemometric modeling of larvicidal activity of plant derived compounds against zika virus vectorAedes aegypti: application of ETA indices

Dengue, zika and chikungunya have severe public health concerns in several countries. Human modification of the natural environment continues to create habitats in which mosquitoes, vectors of a wide variety of human and animal pathogens, thrive, which can bring about an enormous negative impact on public health if not controlled properly. Quantitative structure–activity relationship (QSAR) modeling has been applied in this work with the aim of exploring features contributing to promising larvicidal properties against the vector Aedes aegypti (Diptera: Culicidae). A dataset of 61 plant derived compounds reported in previous literature was used in this present study. A genetic algorithm (GA) was used for QSAR model development employing the “Double Cross Validation” (DCV) tool available at http://teqip.jdvu.ac.in/QSAR_Tools/. The DCV tool removes any bias in descriptor selection from a fixed composition of a training set and often provides an optimum solution in terms of predictivity. Simple topological descriptors, the “Extended Topochemical Atom” (ETA) indices developed by the present authors' group, were used for model development. These descriptors do not require pretreatment of molecular structures by conformational analysis or energy minimization before model development, thus saving computational time and resources. They also avoid ambiguities with respect to the existence of compounds in various conformational states leading to the loss of predictive capability in QSAR models. A number of models were generated from GA, and further, the descriptors appearing in the best model obtained from GA were subjected to partial least squares (PLS) regression to obtain the final robust model. The developed model was validated extensively using different validation metrics to check the reliability and predictivity of the model for enhancing confidence in QSAR predictions. Based on the insights obtained from the PLS model, we can conclude that the presence of hydrogen bond acceptor atoms, the presence of multiple bonds as well as sufficient lipophilicity and a limited polar surface area play crucial roles in regulating the activity of the compounds.

Dengue, zika and chikungunya have severe public health concerns in several countries. We have developed here a QSAR model for larvicidal activity of plant derived compounds against the vector Aedes aegypti.

A Simple, Robust and Efficient Computational Method for n-Octanol/Water Partition Coefficients of Substituted Aromatic Drugs

In this paper, multiple linear regression (MLR) was used to build quantitative structure property relationship (QSPR) of n-octanol-water partition coefficient (logP_o/w) of 195 substituted aromatic drugs. The molecular descriptors were calculated for each compound by the VLifeMDS. By applying genetic algorithm/multiple linear regressions (GA/MLR) the most relevant descriptors were selected to build a QSPR model. The robustness of the model was characterized by the statistical validation and applicability domain (AD). The prediction results from MLR are in good agreement with the experimental values. The R² and Q²_LOO for MLR are 0.9433, 0.9341. The AD of the model was analyzed based on the Williams plot. The effects of different selected descriptors are described.

QSAR models for predicting the toxicity of piperidine derivatives against Aedes aegypti

QSAR models are proposed for predicting the toxicity of 33 piperidine derivatives against Aedes aegypti. From 2D topological descriptors, calculated with the PaDEL software, ordinary least squares multilinear regression (OLS-MLR) treatment from the QSARINS software and machine learning and related approaches including linear and radial support vector machine (SVM), projection pursuit regression (PPR), radial basis function neural network (RBFNN), general regression neural network (GRNN) and k-nearest neighbours (k-NN), led to four-variable models. Their robustness and predictive ability were evaluated through both internal and external validation. Determination coefficients (r²) greater than 0.85 on the training sets and 0.8 on the test sets were obtained with OLS-MLR and linear SVM. They slightly outperform PPR, radial SVM and RBFNN, whereas GRNN and k-NN showed lower performance. The easy availability of the involved structural descriptors and the simplicity of the MLR model make the corresponding model attractive at an exploratory level for proposing, from this limited dataset, guidelines in the design of new potentially active molecules.

In silico prediction of chemical subcellular localization via multi-classification methods

Chemical subcellular localization is closely related to drug distribution in the body and hence important in drug discovery and design. Although many in vivo and in vitro methods have been developed, in silico methods play key roles in the prediction of chemical subcellular localization due to their low costs and high performance. For that purpose, machine learning-based methods were developed here. At first, 614 unique compounds localized in the lysosome, mitochondria, nucleus and plasma membrane were collected from the literature. 80% of the compounds were used to build the models and the rest as the external validation set. Both fingerprints and molecular descriptors were used to describe the molecules, and six machine learning methods were applied to build the multi-classification models. The performance of the models was measured by 5-fold cross-validation and external validation. We further detected key substructures for each localization and analyzed potential structure-localization relationships, which could be very helpful for molecular design and modification. The key substructures can also be used as features complementary to fingerprints to improve the performance of the models.

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.

Predictive QSAR modelling of algal toxicity of ionic liquids and its interspecies correlation with Daphnia toxicity

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.

In silico prediction of chemical toxicity on avian species using chemical category approaches

Avian species are sensitive to pesticides and industrial chemicals, and hence used as model species in evaluation of chemical toxicity. In present study, we assessed the toxicity of more than 663 diverse chemicals on 17 avian species. All the chemicals were classified into three categories, i.e. highly toxic, slightly toxic and non-toxic, based on the toxicity classification criteria of the United States Environmental Protection Agency (EPA). To evaluate these chemicals, the toxicity prediction models were built using chemical category approaches with molecular descriptors and five commonly used fingerprints, in which five machine learning methods were performed on two standard test species: aquatic bird mallard duck and terrestrial bird northern bobwhite quail. The support vector machine (SVM) method with Pubchem fingerprint performed best as revealed by 5-fold cross-validation and the external validation set on Japanese quail. No species difference existed in our database despite several chemicals with different toxicity on some avian species. The best model had an overall accuracy at 0.851 for the prediction of toxicity on avian species, which outperformed the work of Mazzatorta et al. Furthermore, several representative substructures for characterizing avian toxicity were identified via information gain (IG) method. This study would provide a new tool for chemical safety assessment.

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.

In silico prediction of chemical aquatic toxicity with chemical category approaches and substructural alerts

Aquatic toxicity is an important endpoint in the evaluation of chemically adverse effects on ecosystems.

Development of dual inhibitors against Alzheimer's disease using fragment-based QSAR and molecular docking

Alzheimer's (AD) is the leading cause of dementia among elderly people. Considering the complex heterogeneous etiology of AD, there is an urgent need to develop multitargeted drugs for its suppression. β-amyloid cleavage enzyme (BACE-1) and acetylcholinesterase (AChE), being important for AD progression, have been considered as promising drug targets. In this study, a robust and highly predictive group-based QSAR (GQSAR) model has been developed based on the descriptors calculated for the fragments of 20 1,4-dihydropyridine (DHP) derivatives. A large combinatorial library of DHP analogues was created, the activity of each compound was predicted, and the top compounds were analyzed using refined molecular docking. A detailed interaction analysis was carried out for the top two compounds (EDC and FDC) which showed significant binding affinity for BACE-1 and AChE. This study paves way for consideration of these lead molecules as prospective drugs for the effective dual inhibition of BACE-1 and AChE. The GQSAR model provides site-specific clues about the molecules where certain modifications can result in increased biological activity. This information could be of high value for design and development of multifunctional drugs for combating AD.

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.

Modeling bioconcentration factor (BCF) using mechanistically interpretable descriptors computed from open source tool "PaDEL-Descriptor"

Predictive regression-based models for bioconcentration factor (BCF) have been developed using mechanistically interpretable descriptors computed from open source tool PaDEL-Descriptor ( http://padel.nus.edu.sg/software/padeldescriptor/ ). A data set of 522 diverse chemicals has been used for this modeling study, and extended topochemical atom (ETA) indices developed by the present authors' group were chosen as the descriptors. Due to the importance of lipohilicity in modeling BCF, XLogP (computed partition coefficient) was also tried as an additional descriptor. Genetic function approximation followed by multiple linear regression algorithm was applied to select descriptors, and subsequent partial least squares analyses were performed to establish mathematical equations for BCF prediction. The model generated from only ETA indices shows importance of seven descriptors in model development, while the model generated from ETA descriptors along with XlogP shows importance of four descriptors in model development. In general, BCF depends on lipophilicity, presence of heteroatoms, presence of halogens, fused ring system, hydrogen bonding groups, etc. The developed models show excellent statistical qualities and predictive ability. The developed models were used also for prediction of an external data set available from the literature, and good quality of predictions (R (2) pred = 0.812 and 0.826) was demonstrated. Thus, BCF can be predicted using ETA and XlogP descriptors calculated from open source PaDEL-Descriptor software in the context of aquatic chemical toxicity management.

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

Ionic liquids have been judged much with respect to their wide applicability than their considerable harmful effects towards the living ecosystem which has been observed in many instances. Hence, toxicological introspection of these chemicals by the development of predictive mathematical models can be of good help. This study presents an attempt to develop predictive classification and regression models correlating the structurally derived chemical information of a group of 62 diverse ionic liquids with their toxicity towards Daphnia magna and their interpretation. We have principally used the extended topochemical atom (ETA) indices along with various topological non-ETA and thermodynamic parameters as independent variables. The developed quantitative models have been subjected to extensive statistical tests employing multiple validation strategies from which acceptable results have been reported. The best models obtained from classification and regression studies captured necessary structural information on lipophilicity, branching pattern, electronegativity and chain length of the cationic substituents for explaining ecotoxicity of ionic liquids towards D. magna. The derived information can be successfully used to design better ionic liquid analogues acquiring the qualities of a true eco-friendly green chemical.

Predictive chemometric modeling of DPPH free radical-scavenging activity of azole derivatives using 2D- and 3D-quantitative structure–activity relationship tools

Background: The endogenous antioxidants often fail to manage the systemic free radical overload resulting from extensive exposure to environmental pollutants and improper diet. Such free-radical burden over a prolonged period leads to oxidative stress, which in turn, promotes an array of fatal diseases. Results: Five different in silico methodologies have been employed here for a series of azole derivatives, which identify the essential structural attributes of the molecules and quantify the contributions of the prime molecular prerequisites for designing compounds with improved antioxidant activity. Conclusion: The importance of the different constituents is quantitatively analyzed using the descriptor-based quantitative structure–activity relationship and group-based quantitative structure–activity relationship models while the pharmacophore, comparative molecular similarity index analysis and hologram quantitative structure–activity relationship models serve as essential query tools for screening of azole compounds in order to select potent antioxidant molecules.

QSTR with extended topochemical atom (ETA) indices. 15. Development of predictive models for toxicity of organic chemicals against fathead minnow using second-generation ETA indices

Modern industrialisation has led to the production of millions of toxic chemicals having hazardous effects on the ecosystem. It is impracticable to determine the toxic potential of a large number of chemicals in animal models, making the use of quantitative structure-toxicity relationship (QSTR) models an alternative strategy for toxicity prediction. Recently we introduced a set of second-generation extended topochemical atom (ETA) indices for predictive modelling. Here we have developed predictive toxicity models on a large dataset of 459 diverse chemicals against fathead minnow (Pimephales promelas) using the second-generation ETA indices. These descriptors can be easily calculated from two-dimensional molecular representation without the need of time-consuming conformational analysis and alignment, making the developed models easily reproducible. Considering the importance of hydrophobicity for toxicity prediction, AlogP98 was used as an additional predictor in all the models, which were validated rigorously using multiple strategies. The ETA models were comparable in predictability to those involving various non-ETA topological parameters and those previously reported using various descriptors including computationally demanding quantum-chemical ones.