QSAR with quantum topological molecular similarity indices: toxicity of aromatic aldehydes to Tetrahymena pyriformis

q-RASTR modelling for prediction of diverse toxic chemicals towards T. pyriformis

A series of diverse organic compounds impose serious detrimental effects on the health of living organisms and the environment. Determination of the structural aspects of compounds that impart toxicity and evaluation of the same is crucial before public usage. The present study aims to determine the structural characteristics of compounds for Tetrahymena pyriformis toxicity using the q-RASTR (Quantitative Read Across Structure-Toxicity Relationship) model. It was developed using RASTR and 2-D descriptors for a dataset of 1792 compounds with defined endpoint (pIGC50) against a model organism, T. pyriformis. For the current study, the whole dataset was divided based on activity/property into the training and test sets, and the q-RASTR model was developed employing six descriptors (three latent variables) having r2, Q2F1 and Q2 values of 0.739, 0.767, and 0.735, respectively. The generated model was thoroughly validated using internationally recognized internal and external validation criteria to assess the model's dependability and predictability. It was highlighted that high molecular weight, aromatic hydroxyls, nitrogen, double bonds, and hydrophobicity increase the toxicity of organic compounds. The current study demonstrates the applicability of the RASTR algorithm in QSTR model development for the prediction of toxic chemicals (pIGC50) towards T. pyriformis.

Random forest algorithm-based accurate prediction of chemical toxicity to Tetrahymena pyriformis

The random forest (RF) algorithm, together with ten Dragon descriptors, was used to develop a quantitative structure-toxicity/activity relationship (QSTR/QSAR) model for a larger data set of 1792 chemical toxicity pIGC₅₀ towards Tetrahymena pyriformis. The optimal RF (ntree =300 and mtry =3) model yielded root mean square (rms) errors of 0.261 for the training set (1434 chemicals) and 0.348 for the test set (358 chemicals). Compared with other QSTR models reported in the literature, the optimal RF model in this paper is more accurate. The feasibility of applying the RF algorithm to predict chemical toxicity pIGC₅₀ towards Tetrahymena pyriformis has been verified.

Development of antidiabetic drugs from benzamide derivatives as glucokinase activator: A computational approach

Hyperglycemia is a condition known for the impairment of insulin secretion and is responsible for diabetes mellitus. Various small molecule inhibitors have been discovered as glucokinase activators. Recent studies on benzamide derivatives showed their importance in the treatment of diabetes as glucokinase activator. The present manuscript showed a computation study on benzamide derivatives to help in the production of potent glucokinase activators. In the present study, pharmacophore development, 3D-QSAR, and docking studies were performed on benzamide derivatives to find out the important features required for the development of a potential glucokinase activator. The generated pharmacophore hypothesis ADRR_1 consisted of essential features required for the activity. The resultant statistical data showed high significant values with R² > 0.99; 0.98 for the training set and Q² > 0.52; 0.71 for test set based on atom-based and field-based models, respectively. The potent compound 15b of the series showed a good docking score via binding with different amino acid residues such as (NH…ARG63), (SO₂…ARG250, THR65), and π-π staking with (phenyl……TYR214). The virtual screening study used 3563 compounds from ZINC database and screened hit compound ZINC08974524, binds with similar amino acids as shown by compound 15b and crystal ligand with docking scores SP (-11.17 kcal/mol) and XP (-8.43 kcal/mol). Compounds were further evaluated by ADME and MMGBSA parameters. Ligands and ZINC hits showed no violation of Lipinski rules. All the screened compounds showed good synthetic accessibility. The present study may be used by researchers for the development of novel benzamide derivatives as glucokinase activator.

Prediction of chemical toxicity to Tetrahymena pyriformis with four-descriptor models

A quantitative structure-toxicity relationship (QSTR) model based on four descriptors was successfully developed for 1163 chemical toxicants against Tetrahymena pyriformis by applying general regression neural network (GRNN). The training set consisting of 600 organic compounds was used to train GRNN models that were evaluated with the test set of 563 compounds. For the optimal GRNN model, the training set possesses the coefficient of determination R² of 0.86 and root mean square (rms) error of 0.41, and the test set has R² of 0.80 and rms of 0.41. Investigated results indicate that the optimal GRNN model is accurate, although the GRNN model has only four descriptor and more samples in the test set.

An Investigation on the Quantitative Structure-Activity Relationships of the Anti-Inflammatory Activity of Diterpenoid Alkaloids

Diterpenoid alkaloids are extracted from plants. These compounds have broad biological activities, including effects on the cardiovascular system, anti-inflammatory and analgesic actions, and anti-tumor activity. The anti-inflammatory activity was determined by carrageenan-induced rat paw edema and experimental trauma in rats. The number of studies focused on the determination, quantitation and pharmacological properties of these alkaloids has increased dramatically during the past few years. In this work we built a dataset composed of 15 diterpenoid alkaloid compounds with diverse structures, of which 11 compounds were included in the training set and the remaining compounds were included in the test set. The quantitative chemistry parameters of the 15 diterpenoid alkaloids compound were calculated using the HyperChem software, and the quantitative structure-activity relationship (QSAR) of these diterpenoid alkaloid compounds were assessed in an anti-inflammation model based on half maximal effective concentration (EC₅₀) measurements obtained from rat paw edema data. The QSAR prediction model is as follows: log ( E C 50 ) = - 0.0260 × SAA + 0.0086 × SAG + 0.0011 × VOL - 0.0641 × HE - 0.2628 × LogP - 0.5594 × REF - 0.2211 × POL - 0.1964 × MASS + 0.088 × BE + 0.1398 × HF (R² = 0.981, Q² = 0.92). The validated consensus EC₅₀ for the QSAR model, developed from the rat paw edema anti-inflammation model used in this study, indicate that this model was capable of effective prediction and can be used as a reliable computational predictor of diterpenoid alkaloid activity.

A New Descriptor of Amino Acids-SVGER and its Applications in Peptide QSAR

In the study of peptide quantitative structure activity relationship (QSAR), a new descriptor of amino acids (SVGER) was calculated. It was applied in two peptides which are angiotensin converting enzyme inhibitors and bitter tasting threshold of di-peptide. QSAR models were built by stepwise multiple regression-multiple linear regression (SMR-MLR) and stepwise multiple regression-partial least square regression (SMR-PLS). In the SMR-MLR models for angiotensin converting enzyme inhibitors, the squared cross-validation correlation coefficient (Q_LOO² ) was 0.907, squared correlation coefficient between predicted and observed activities (R_cum² ) was 0.977 and external multiple correlation coefficient (Q_ext² ) was 0.867. The corresponding data for the bitter tasting threshold of di-peptide were 0.802, 0.966, 0.719. While in the SMR-PLS model, Q_LOO² , R_cum² and Q_ext² were 0.804, 0.915, 0.858 for angiotensin converting enzyme inhibitors and 0.782, 0.881, 0.747 for bitter tasting threshold of di-peptide. Our results showed that descriptor SVGER can afford good account of relationships between activity and structure of peptide drugs.

Modeling biophysical and biological properties from the characteristics of the molecular electron density, electron localization and delocalization matrices, and the electrostatic potential

The electron density and the electrostatic potential are fundamentally related to the molecular hamiltonian, and hence are the ultimate source of all properties in the ground- and excited-states. The advantages of using molecular descriptors derived from these fundamental scalar fields, both accessible from theory and from experiment, in the formulation of quantitative structure-to-activity and structure-to-property relationships, collectively abbreviated as QSAR, are discussed. A few such descriptors encode for a wide variety of properties including, for example, electronic transition energies, pK(a)'s, rates of ester hydrolysis, NMR chemical shifts, DNA dimers binding energies, π-stacking energies, toxicological indices, cytotoxicities, hepatotoxicities, carcinogenicities, partial molar volumes, partition coefficients (log P), hydrogen bond donor capacities, enzyme-substrate complementarities, bioisosterism, and regularities in the genetic code. Electronic fingerprinting from the topological analysis of the electron density is shown to be comparable and possibly superior to Hammett constants and can be used in conjunction with traditional bulk and liposolubility descriptors to accurately predict biological activities. A new class of descriptors obtained from the quantum theory of atoms in molecules' (QTAIM) localization and delocalization indices and bond properties, cast in matrix format, is shown to quantify transferability and molecular similarity meaningfully. Properties such as "interacting quantum atoms (IQA)" energies which are expressible into an interaction matrix of two body terms (and diagonal one body "self" terms, as IQA energies) can be used in the same manner. The proposed QSAR-type studies based on similarity distances derived from such matrix representatives of molecular structure necessitate extensive investigation before their utility is unequivocally established.

Nano-quantitative structure-activity relationship modeling using easily computable and interpretable descriptors for uptake of magnetofluorescent engineered nanoparticles in pancreatic cancer cells

As experimental evaluation of the safety of nanoparticles (NPs) is expensive and time-consuming, computational approaches have been found to be an efficient alternative for predicting the potential toxicity of new NPs before mass production. In this background, we have developed here a regression-based nano quantitative structure-activity relationship (nano-QSAR) model to establish statistically significant relationships between the measured cellular uptakes of 109 magnetofluorescent NPs in pancreatic cancer cells with their physical, chemical, and structural properties encoded within easily computable, interpretable and reproducible descriptors. The developed model was rigorously validated internally as well as externally with the application of the principles of Organization for Economic Cooperation and Development (OECD). The test for domain of applicability was also carried out for checking reliability of the predictions. Important fragments contributing to higher/lower cellular uptake of NPs were identified through critical analysis and interpretation of the developed model. Considering all these identified structural attributes, one can choose or design safe, economical and suitable surface modifiers for NPs. The presented approach provides rich information in the context of virtual screening of relevant NP libraries.

Predicting pKa Values in Aqueous Solution for the Guanidine Functional Group from Gas Phase Ab Initio Bond Lengths

Here we applied a novel method1a to predict pKa values of the guanidine functional group, which is a notoriously difficult. This method, which was developed in our lab, uses only one ab initio bond length obtained at a low level of theory. The method is shown to work for drug molecules, delivers prediction errors of less than 0.5 log units, successfully treats tautomerisation in close relation with experiment, and demonstrates strong correlations with only a few data points. The high structural content of the ab initio bond length makes a given data set essentially divide itself into high correlation subsets. One then observes that molecules within a subset possess a common substructure. Each high correlation subset exists in its own region of chemical space. The high correlation subset method is explored with respect to this position in chemical space, in particular tautomerisation. The proposed method is able to distinguish between different tautomeric forms and the preferred tautomeric form emerges naturally, in agreement with experiment.

Transcriptome profiling of Chironomus kiinensis under phenol stress using Solexa sequencing technology

Phenol is a major pollutant in aquatic ecosystems due to its chemical stability, water solubility and environmental mobility. To date, little is known about the molecular modifications of invertebrates under phenol stress. In the present study, we used Solexa sequencing technology to investigate the transcriptome and differentially expressed genes (DEGs) of midges (Chironomus kiinensis) in response to phenol stress. A total of 51,518,972 and 51,150,832 clean reads in the phenol-treated and control libraries, respectively, were obtained and assembled into 51,014 non-redundant (Nr) consensus sequences. A total of 6,032 unigenes were classified by Gene Ontology (GO), and 18,366 unigenes were categorized into 238 Kyoto Encyclopedia of Genes and Genomes (KEGG) categories. These genes included representatives from almost all functional categories. A total of 10,724 differentially expressed genes (P value <0.05) were detected in a comparative analysis of the expression profiles between phenol-treated and control C. kiinensis including 8,390 upregulated and 2,334 downregulated genes. The expression levels of 20 differentially expressed genes were confirmed by real-time RT-PCR, and the trends in gene expression that were observed matched the Solexa expression profiles, although the magnitude of the variations was different. Through pathway enrichment analysis, significantly enriched pathways were identified for the DEGs, including metabolic pathways, aryl hydrocarbon receptor (AhR), pancreatic secretion and neuroactive ligand-receptor interaction pathways, which may be associated with the phenol responses of C. kiinensis. Using Solexa sequencing technology, we identified several groups of key candidate genes as well as important biological pathways involved in the molecular modifications of chironomids under phenol stress.

First report on development of quantitative interspecies structure-carcinogenicity relationship models and exploring discriminatory features for rodent carcinogenicity of diverse organic chemicals using OECD guidelines

Different regulatory agencies in food and drug administration and environmental protection worldwide are employing quantitative structure-activity relationship (QSAR) models to fill the data gaps related with properties of chemicals affecting the environment and human health. Carcinogenicity is a toxicity endpoint of major concern in recent times. Interspecies toxicity correlations may provide a tool for estimating sensitivity towards toxic chemical exposure with known levels of uncertainty for a diversity of wildlife species. In this background, we have developed quantitative interspecies structure-carcinogenicity correlation models for rat and mouse [rodent species according to the Organization for Economic Cooperation and Development (OECD) guidelines] based on the carcinogenic potential of 166 organic chemicals with wide diversity of molecular structures, spanning a large number of chemical classes and biological mechanisms. All the developed models have been assessed according to the OECD principles for the validation of QSAR models. Consensus predictions for carcinogenicity of the individual compounds are presented here for any one species when the data for the other species are available. Informative illustrations of the contributing structural fragments of chemicals which are responsible for specific carcinogenicity endpoints are identified by the developed models. The models have also been used to predict mouse carcinogenicities of 247 organic chemicals (for which rat carcinogenicities are present) and rat carcinogenicities of 150 chemicals (for which mouse carcinogenicities are present). Discriminatory features for rat and mouse carcinogenicity values have also been explored.

Linear and non-linear QSAR modelling of juvenile hormone esterase inhibitors

A tight control of juvenile hormone (JH) titre is crucial during the life cycle of a holometabolous insect. JH metabolism is made through the action of enzymes, particularly the juvenile hormone esterase (JHE). Trifluoromethylketones (TFKs) are able to inhibit this enzyme to disrupt the endocrine function of the targeted insect. In this context, a set of 96 TFKs, tested on Trichoplusia ni for their JHE inhibition, was split into a training set (n = 77) and a test set (n = 19) to derive a QSAR model. TFKs were initially described by 42 CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) descriptors, but a feature selection process allowed us to consider only five descriptors encoding the structural characteristics of the TFKs and their reactivity. A classical and spline regression analysis, a three-layer perceptron, a radial basis function network and a support vector regression were experienced as statistical tools. The best results were obtained with the support vector regression (r(2) and r(test)(2) = 0.91). The model provides information on the structural features and properties responsible for the high JHE inhibition activity of TFKs.

Atomic charges of individual reactive chemicals in binary mixtures determine their joint effects: an example of cyanogenic toxicants and aldehydes

Environmental contaminants are usually encountered as mixtures, and many of these mixtures yield synergistic or antagonistic effects attributable to an intracellular chemical reaction that pose a potential threat on ecological systems. However, how atomic charges of individual chemicals determine their intracellular chemical reactions, and then determine the joint effects for mixtures containing reactive toxicants, is not well understood. To address this issue, the joint effects between cyanogenic toxicants and aldehydes on Photobacterium phosphoreum were observed in the present study. Their toxicological joint effects differed from one another. This difference is inherently related to the two atomic charges of the individual chemicals: the oxygen charge of -CHO (O(aldehyde toxicant)) in aldehyde toxicants and the carbon-atom charge of a carbon chain in the cyanogenic toxicant (C(cyanogenic toxicant)). Based on these two atomic charges, the following QSAR (quantitative structure-activity relationship) model was proposed: When (O(aldehyde toxicant) -C(cyanogenic toxicant) )> -0.125, the joint effect of equitoxic binary mixtures at median inhibition (TU, the sum of toxic units) can be calculated as TU = 1.00 ± 0.20; when (O(aldehyde toxicant) -C(cyanogenic toxicant) ) ≤ -0.125, the joint effect can be calculated using TU = - 27.6 x O (aldehyde toxicant) - 5.22 x C (cyanogenic toxicant) - 6.97 (n = 40, r = 0.887, SE = 0.195, F = 140, p < 0.001, q(2) (Loo) = 0.748; SE is the standard error of the regression, F is the F test statistic). The result provides insight into the relationship between the atomic charges and the joint effects for mixtures containing cyanogenic toxicants and aldehydes. This demonstrates that the essence of the joint effects resulting from intracellular chemical reactions depends on the atomic charges of individual chemicals. The present study provides a possible approach for the development of a QSAR model for mixtures containing reactive toxicants based on the atomic charges.

A descriptor of amino acids: SVRG and its application to peptide quantitative structure-activity relationship

In this work, a descriptor, SVRG (principal component scores vector of radial distribution function descriptors and geometrical descriptors), was derived from principal component analysis (PCA) of a matrix of two structural variables of coded amino acids, including radial distribution function index (RDF) and geometrical index. SVRG scales were then applied in three panels of peptide quantitative structure-activity relationships (QSARs) which were modelled by partial least squares regression (PLS). The obtained models with the correlation coefficient (R²(cum)), cross-validation correlation coefficient (Q²(LOO)) were 0.910 and 0.863 for 48 bitter-tasting dipeptides; 0.968 and 0.931 for 21 oxytocin analogues; and 0.992 and 0.954 for 20 thromboplastin inhibitors. Satisfactory results showed that SVRG contained much chemical information relating to bioactivities. The approach may be a useful structural expression methodology for studies on peptide QSAR.

pKa prediction from an ab initio bond length: part 2--phenols

The prediction of pK(a) continues to attract much attention with ongoing investigations into new ways to predict pK(a) accurately, where predicted pK(a) values deviate less than 0.50 log units from experiment. We show that a single descriptor, i.e. an ab initio bond length, can predict pK(a). The emphasis was placed on model simplicity and a demonstration that more accurate predictions emerge from single-bond-length models. A data set of 171 phenols was studied. The carbon-oxygen bond length, connecting the OH to the phenyl ring, consistently provided accurate predictions. The pK(a) of meta- and para-substituted phenols is predicted here by a single-bond-length model within 0.50 log units. However, accurate prediction of the pK(a) of ortho-substituted phenols necessitated their splitting into groups called high-correlation subsets in which the pK(a) of the compounds strongly correlated with a single bond-length. The highly compound-specific single-bond-length models produced better predictions than models constructed with more compounds and more bond lengths. Outliers were easily identified using single-bond-length models and in most cases we were able to determine the reason for the outlier discrepancy. Furthermore, the single-bond-length models showed better cross-validation statistics than the PLS models constructed using more than one bond length. For all of the single-bond-length models, RMSEE was less than 0.50. For the majority of the models, RMSEP was less than 0.50. The results support the use of multiple high-correlation subsets and a single bond-length to predict pK(a). Six one-term linear equations are listed as a starting point for the construction of a more comprehensive list covering a larger variety of compound classes.

pKa prediction from an ab initio bond length: part 3--benzoic acids and anilines

The prediction of pK(a) from a single ab initio bond length has been extended to provide equations for benzoic acids and anilines. The HF/6-31G(d) level of theory is used for all geometry optimisations. Similarly to phenols (Part 2 of this series of publications), the meta-/para-substituted benzoic acids can be predicted from a single model constructed from one bond length. This model had an impressive RMSEP of 0.13 pK(a) units. The prediction of ortho-substituted benzoic acids required the identification of high-correlation subsets, where the compounds in the same subset have at least one of the same (e.g. halogens, hydroxy) ortho substituent. Two pK(a) equations are provided for o-halogen benzoic acids and o-hydroxybenzoic acids, where the RMSEP values are 0.19 and 0.15 pK(a) units, respectively. Interestingly, the bond length that provided the best model differed between these two high-correlation subsets. This demonstrates the importance of investigating the most predictive bond length, which is not necessarily the bond involving the acid hydrogen. Three high-correlation subsets were identified for the ortho-substituted anilines. These were o-halogen, o-nitro and o-alkyl-substituted aniline high-correlation subsets, where the RMSEP ranged from 0.23 to 0.44 pK(a) units. The RMSEP for the meta-/para-substituted aniline model was 0.54 pK(a) units. This value exceeded our threshold of 0.50 pK(a) units and was higher than both the m-/p-benzoic acids in this work and the m-/p-phenols (RMSEP = 0.43) of Part 2. Constructing two separate models for the meta- and para- substituted anilines, where RMSEP values of 0.63 and 0.33 pK(a) units were obtained respectively, revealed it was the meta-substituted anilines that caused the large RMSEP value. For unknown reasons the RMSEP value increased with the addition of a further twenty meta-substituted anilines to this model. The C-N bond always produced the best correlations with pK(a) for all the high-correlation subsets. A higher level of theory and an ammonia probe improved the statistics only marginally for the hydroxybenzoic acid high-correlation subsets.

In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods

There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. It is especially true to address the persistence, bioaccumulative and toxicity potentials of organic chemicals. Tetrahymena pyriformis toxicity is often used as a toxic endpoint. In this study, 1571 diverse unique chemicals were collected from the literature and composed of the largest diverse data set for T. pyriformis toxicity. Classification predictive models of T. pyriformis toxicity were developed by substructure pattern recognition and different machine learning methods, including support vector machine (SVM), C4.5 decision tree, k-nearest neighbors and random forest. The results of a 5-fold cross-validation showed that the SVM method performed better than other algorithms. The overall predictive accuracies of the SVM classification model with radial basis functions kernel was 92.2% for the 5-fold cross-validation and 92.6% for the external validation set, respectively. Furthermore, several representative substructure patterns for characterizing T. pyriformis toxicity were also identified via the information gain analysis methods.

QSTR with extended topochemical atom (ETA) indices. 14. QSAR modeling of toxicity of aromatic aldehydes to Tetrahymena pyriformis

Aldehydes are a toxic class of chemicals causing severe health hazards. In this background, quantitative structure-toxicity relationship (QSTR) models have been developed in the present study using Extended Topochemical Atom (ETA) indices for a large group of 77 aromatic aldehydes for their acute toxicity against the protozoan ciliate Tetrahymena pyriformis. The ETA models have been compared with those developed using various non-ETA topological indices. Attempt was also made to include the n-octanol/water partition coefficient (logK(o/w)) as an additional descriptor considering the importance of hydrophobicity in toxicity prediction. Thirty different models were developed using different chemometric tools. All the models have been validated using internal validation and external validation techniques. The statistical quality of the ETA models was found to be comparable to that of the non-ETA models. The ETA models have shown the important effects of steric bulk, lipophilicity, presence of electronegative atom containing substituents and functionality of the aldehydic oxygen to the toxicity of the aldehydes. The best ETA model (without using logK(o/w)) shows encouraging statistical quality (Q(int)(2)=0.709,Q(ext)(2)=0.744). It is interesting to note that some of the topological models reported here are better in statistical quality than previously reported models using quantum chemical descriptors.