Atom-type Electrotopological State Research Articles

The rat acute oral toxicity of trifluoromethyl compounds (TFMs): a computational toxicology study combining the 2D-QSTR, read-across and consensus modeling methods.

All areas of the modern society are affected by fluorine chemistry. In particular, fluorine plays an important role in medical, pharmaceutical and agrochemical sciences. Amongst various fluoro-organic compounds, trifluoromethyl (CF3) group is valuable in applications such as pharmaceuticals, agrochemicals and industrial chemicals. In the present study, following the strict OECD modelling principles, a quantitative structure-toxicity relationship (QSTR) modelling for the rat acute oral toxicity of trifluoromethyl compounds (TFMs) was established by genetic algorithm-multiple linear regression (GA-MLR) approach. All developed models were evaluated by various state-of-the-art validation metrics and the OECD principles. The best QSTR model included nine easily interpretable 2D molecular descriptors with clear physical and chemical significance. The mechanistic interpretation showed that the atom-type electro-topological state indices, molecular connectivity, ionization potential, lipophilicity and some autocorrelation coefficients are the main factors contributing to the acute oral toxicity of TFMs against rats. To validate that the selected 2D descriptors can effectively characterize the toxicity, we performed the chemical read-across analysis. We also compared the best QSTR model with public OPERA tool to demonstrate the reliability of the predictions. To further improve the prediction range of the QSTR model, we performed the consensus modelling. Finally, the optimum QSTR model was utilized to predict a true external set containing many untested/unknown TFMs for the first time. Overall, the developed model contributes to a more comprehensive safety assessment approach for novel CF3-containing pharmaceuticals or chemicals, reducing unnecessary chemical synthesis whilst saving the development cost of new drugs.

Development and Validation of Predictive Quantitative Structure-Activity Relationship Models for Estrogenic Activities of Hydroxylated Polychlorinated Biphenyls.

Disruption of the endocrine system by hydroxylated polychlorinated biphenyls (OH-PCBs) is hypothesized, among other potential mechanisms, to be mediated via nuclear receptor binding. Due to the high cost and lengthy time required to produce high-quality experimental data, empirical data to support the nuclear receptor binding hypothesis are in short supply. In the present study, two quantitative structure-activity relationship models were developed for predicting the estrogenic activities of OH-PCBs. Findings revealed that model I (for the estrogen receptor α dataset) contained five two-dimensional (2D) descriptors belonging to the classes autocorrelation, Burden modified eigenvalues, chi path, and atom type electrotopological state, whereas model II (for the estrogen receptor β dataset) contained three 2D and three 3D descriptors belonging to the classes autocorrelation, atom type electrotopological state, and Radial Distribution Function descriptors. The internal and external validation metrics reported for models I and II indicate that both models are robust, reliable, and suitable for predicting the estrogenic activities of untested OH-PCB congeners. Environ Toxicol Chem 2023;42:823-834. © 2023 SETAC.

Antimalarial Drug Predictions Using Molecular Descriptors and Machine Learning against Plasmodium Falciparum.

Malaria remains by far one of the most threatening and dangerous illnesses caused by the plasmodium falciparum parasite. Chloroquine (CQ) and first-line artemisinin-based combination treatment (ACT) have long been the drug of choice for the treatment and controlling of malaria; however, the emergence of CQ-resistant and artemisinin resistance parasites is now present in most areas where malaria is endemic. In this work, we developed five machine learning models to predict antimalarial bioactivities of a drug against plasmodium falciparum from the features (i.e., molecular descriptors values) obtained from PaDEL software from SMILES of compounds and compare the machine learning models by experiments with our collected data of 4794 instances. As a consequence, we found that three models amongst the five, namely artificial neural network (ANN), extreme gradient boost (XGB), and random forest (RF), outperform the others in terms of accuracy while observing that, using roughly a quarter of the promising descriptors picked by the feature selection algorithm, the five models achieved equivalent and comparable performance. Nevertheless, the contribution of all molecular descriptors in the models was investigated through the comparison of their rank values by the feature selection algorithm and found that the most potent and relevant descriptors which come from the ‘Autocorrelation’ module contributed more while the ‘Atom type electrotopological state’ contributed the least to the model.

Chemometric QSAR modeling of acute oral toxicity of Polycyclic Aromatic Hydrocarbons (PAHs) to rat using simple 2D descriptors and interspecies toxicity modeling with mouse

The information of the acute oral toxicity for most polycyclic aromatic hydrocarbons (PAHs) in mammals are lacking due to limited experimental resources, leading to a need to develop reliable in silico methods to evaluate the toxicity endpoint. In this study, we developed the quantitative structure-activity relationship (QSAR) models by genetic algorithm (GA) and multiple linear regression (MLR) for the rat acute oral toxicity (LD50) of PAHs following the strict validation principles of QSAR modeling recommended by OECD. The best QSAR model comprised eight simple 2D descriptors with definite physicochemical meaning, which showed that maximum atom-type electrotopological state, van der Waals surface area, mean atomic van der Waals volume, and total number of bonds are main influencing factors for the toxicity endpoint. A true external set (554 compounds) without rat acute oral toxicity values, and 22 limit test compounds, were firstly predicted along with reliability assessment. We also compared our proposed model with the OPERA predictions and recently published literature to prove the prediction reliability. Furthermore, the interspecies toxicity (iST) models of PAHs between rat and mouse were also established, validated and employed for filling data gap. Overall, our developed models should be applicable to new or untested or not yet synthesized PAHs falling within the applicability domain (AD) of the models for rapid acute oral toxicity prediction, thus being important for environmental or personal exposure risk assessment under regulatory frameworks.

Prediction of Dispersive Liquid–Liquid Microextraction Enrichment Effect of Aromatic Organics by [OMIM] [PF6] Ionic Liquid Based on Atom-Type Electrotopological State Indices

In this work, a quantitative structure–property relationship (QSPR) model based on atom-type electrotopological state (E-state) indices has been developed for predicting the dispersive liquid–liquid microextraction (DLLME) enrichment effect for aromatic organics by using 1-octyl-3-methylimidazolium hexafluorophosphate ([OMIM][PF6]) ionic liquid as extractant. The enrichment factors of the aromatic organics obtained through DLLME-IL process were used as the dependent variable of the multiple linear regression (MLR) equation. The E-state indices of those organic compounds which combined together both electronic and topological characteristics of the analyzed molecules were used as molecular structure descriptors and the independent variable of the equation. The squared correlation coefficient R2 for the MLR was 0.992 for the training set of 33 compounds. For the test set of 8 compounds, the linear regression coefficient was R2 = 0.985. The results of this study showed that it can successfully predict enrichment factors of [OMIM][PF6] in DLLME by using atom-type E-state indices, which can provide one more way for predicting the ability of ionic liquid to extract organic compounds based on their molecular structures.

PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints

PaDEL-Descriptor is a software for calculating molecular descriptors and fingerprints. The software currently calculates 797 descriptors (663 1D, 2D descriptors, and 134 3D descriptors) and 10 types of fingerprints. These descriptors and fingerprints are calculated mainly using The Chemistry Development Kit. Some additional descriptors and fingerprints were added, which include atom type electrotopological state descriptors, McGowan volume, molecular linear free energy relation descriptors, ring counts, count of chemical substructures identified by Laggner, and binary fingerprints and count of chemical substructures identified by Klekota and Roth. PaDEL-Descriptor was developed using the Java language and consists of a library component and an interface component. The library component allows it to be easily integrated into quantitative structure activity relationship software to provide the descriptor calculation feature while the interface component allows it to be used as a standalone software. The software uses a Master/Worker pattern to take advantage of the multiple CPU cores that are present in most modern computers to speed up calculations of molecular descriptors. The software has several advantages over existing standalone molecular descriptor calculation software. It is free and open source, has both graphical user interface and command line interfaces, can work on all major platforms (Windows, Linux, MacOS), supports more than 90 different molecular file formats, and is multithreaded. PaDEL-Descriptor is a useful addition to the currently available molecular descriptor calculation software. The software can be downloaded at http://padel.nus.edu.sg/software/padeldescriptor.

A new method for predicting the heats of combustion of polynitro arene, polynitro heteroarene, acyclic and cyclic nitramine, nitrate ester and nitroaliphatic compounds

A new method is presented for estimating the gross and net heats of combustion of important classes of energetic compounds including polynitro arene, polynitro heteroarene, acyclic and cyclic nitramine, nitrate ester and nitroaliphatic compounds. Elemental compositions as well as the presence of some specific polar groups and molecular fragments are important parameters in the new model. The novel method can be easily used for any complex organic compounds with at least one nitro, nitramine or nitrate functional groups by which the predictions of their heats of combustion by the available methods are inaccurate or difficult. The predicted results show that this method gives reliable predictions of heats of combustion with respect to group additivity method and computed values based on atom-type electrotopological state indices for several energetic compounds where the models can be applied.

Prediction of the net heat of combustion of organic compounds based on atom-type electrotopological state indices

An accurate quantitative structure–property relationship (QSPR) model, based on the atom-type electrotopological state (E-state) indices and artificial neural network (ANN) technique, for prediction of standard net heat of combustion (Δ H c o) was developed. An extended set of 49 atom-type electrotopological state (E-state) indices that combined together both electronic and topological characteristics of the analyzed molecules were used as molecular structure descriptors for a diverse set of 1496 organic compounds. Both multilinear regression (MLR) and artificial neural network (ANN) were employed in the modeling. The ANN model with the final optimum network architecture of [49-35-1] gave a significant better performance than the MLR model. The squared correlation coefficient R 2 for the ANN model was R 2 = 0.991 for the training set of 1196 compounds. For the test set of 300 compounds, the corresponding statistics was R 2 = 0.992. The results of this study showed that it would be successful to predict Δ H c o by using the easily calculated atom-type E-state indices, which can provide one more way for predicting the Δ H c o of organic compounds for engineering based on only their molecular structures.

Prediction of soil sorption coefficient of organic pesticides from the atom‐type electrotopological state indices

A group contribution approach based on atom-type electrotopological state indices for predicting the soil sorption coefficient (log KOC) of a diverse set of 201 organic pesticides is presented. Using a training set of 143 compounds, for which the log KOC values were in the range from 0.42 to 5.31, multiple linear regression (MLR) and artificial neural networks were used to build the models. The models were validated using two test sets of 20 and 38 chemicals not included in the training set. The statistics for a linear model with 12 structural parameters were, in test set 1, r2 = 0.79, s = 0.45 and, in test set 2, r2 = 0.74, s = 0.65. These results clearly show that soil sorption coefficients can be accurately and rapidly estimated from easily calculated structural parameters.

PREDICTION OF SOIL SORPTION COEFFICIENT OF ORGANIC PESTICIDES FROM THE ATOM-TYPE ELECTROTOPOLOGICAL STATE INDICES

A group contribution approach based on atom-type electrotopological state indices for predicting the soil sorption coefficient (log KOC) of a diverse set of 201 organic pesticides is presented. Using a training set of 143 compounds, for which the log KOC values were in the range from 0.42 to 5.31, multiple linear regression (MLR) and artificial neural networks were used to build the models. The models were validated using two test sets of 20 and 38 chemicals not included in the training set. The statistics for a linear model with 12 structural parameters were, in test set 1, r2 = 0.79, s = 0.45 and, in test set 2, r2 = 0.74, s = 0.65. These results clearly show that soil sorption coefficients can be accurately and rapidly estimated from easily calculated structural parameters.

QSAR modeling with the electrotopological state indices: predicting the toxicity of organic chemicals

A quantitative structure–activity relationship model, based on the atom-type electrotopological state (E-state) indices, for the prediction of toxicity to fathead minnow for a diverse set of 140 organic chemicals is presented. Multiple linear regression and artificial neural network techniques were employed in the modeling of experimental toxicity (−logLC 50) values ranging from 0.85 to 6.09. For the training set of 130 organic compounds a linear regression model with r 2=0.84 and s=0.36 was obtained with 14 atom-type E-state indices. For the test set of 10 compounds, the corresponding statistics were r 2=0.83 and s=0.47, respectively. Neural networks gave a significant improvement using the same set of parameters, and the standard deviations were s=0.31 for the training set and s=0.30 for the test set when an artificial neural network with five neurons in the hidden layer was used. The results clearly show that accurate models can be rapidly calculated for the prediction of toxicity for a diverse set of organic chemicals using easily calculated parameters.

Development of Quantitative Structure−Property Relationship Models for Early ADME Evaluation in Drug Discovery. 2. Blood-Brain Barrier Penetration

A new molecular lipoaffinity descriptor was introduced in this paper to account for the effect of molecular hydrophobicity on blood-brain barrier penetration. The descriptor was defined based on Kier and Hall's atom-type electrotopological state indices. Its evaluation requires 2-D molecular bonding information only. A multiple linear regression equation using this descriptor and molecular weight reproduces the experimental logBB values of 55 training set compounds and 11 test set compounds satisfactorily with statistical parameters nearly identical to the best models based on polar surface area and ClogP. The results indicate that the lipoaffinity descriptor defined in this paper may be a significant descriptor for molecular transport properties across lipid bilayers.

Prediction of biodegradation from the atom‐type electrotopological state indices

A group contribution method based on atom-type electrotopological state indices for predicting the biodegradation of a diverse set of 241 organic chemicals is presented. Multiple linear regression and artificial neural networks were used to build the models using a training set of 172 compounds, for which the approximate time for ultimate biodegradation was estimated from the results of a survey of an expert panel. Derived models were validated by using a leave-25%-out method and against two test sets of 12 and 57 chemicals not included in the training set. The squared correlation coefficient (r2) for a linear model with 15 structural parameters was 0.76 for the training set and 0.68 for the test set of 12 molecules. The model predicted correctly the biodegradation of 48 chemicals in the test set of 57 molecules, for which biodegradability was presented as rapid or slow. The use of artificial neural networks gave better prediction for both test sets when the same set of parameters was tested as inputs in neural network simulations. The predictions of rapidly biodegradable chemicals were more accurate than the predictions of slowly biodegradable chemicals for both the regression and neural network models.

Estimation of water solubility from atom‐type electrotopological state indices

Based on the atom-type electrotopological state (E-state) indices, a quantitative structure-property relationship model for the prediction of aqueous solubility for a diverse set of 745 organic compounds is presented. The multiple linear regression analysis was used to build the models. A training set of 674 compounds, containing 349 liquids and 325 solids and having a range of aqueous solubility (log S) values from 2.77 to 11.62, was obtained from the literature. For this set, the squared correlation coefficient and standard deviation for a linear model with 31 atom-type E-state indices and three simple correction factors were r2 = 0.94 and s = 0.58 (log units), respectively. The corresponding statistics for the test sets not included in the training set were, for a set of 50 pesticides, r2 = 0.79 and s = 0.81 and, for a set of 21 drug and pesticide compounds, r2 = 0.83 and s = 0.84, respectively. The contribution of melting points was also evaluated. The use of melting point increased the accuracy of the models in the fit of the training set but not in the prediction of the test sets. Hence, the proposed method offers fast and accurate estimation of aqueous solubility of organic compounds using atom-type E-state indices without the need of any experimental parameters like the melting points.

Prediction of aqueous solubility for a diverse set of organic compounds based on atom-type electrotopological state indices

We describe robust methods for estimating the aqueous solubility of a set of 734 organic compounds from different structural classes based on multiple linear regression (MLR) and artificial neural networks (ANN) model. The structures were represented by atom-type electrotopological state (E-state) indices. The squared correlation coefficient and standard deviation for the MLR with 34 structural parameters were r 2=0.94 and s=0.58 for the training set of 675 compounds. For the test set of 21 compounds, the equivalent statistics were r 2 pred=0.80 and s=0.87, respectively. Neural networks gave a significant improvement using the same set of parameters, and the standard deviations were s=0.52 for the training set and s=0.75 for the test set when an artificial neural network with five neurons in the hidden layer was used. The results clearly show that accurate models can be rapidly calculated for the estimation of aqueous solubility for a large and diverse set of organic compounds using easily calculated structural parameters.

Neural network modeling for estimation of partition coefficient based on atom-type electrotopological state indices

A method for predicting log P values for a diverse set of 1870 organic molecules has been developed based on atom-type electrotopological-state (E-state) indices and neural network modeling. An extended set of E-state indices, which included specific indices with a more detailed description of amino, carbonyl, and hydroxy groups, was used in the current study. For the training set of 1754 molecules the squared correlation coefficient and root-mean-squared error were r2 = 0.90 and RMS(LOO) = 0.46, respectively. Structural parameters which included molecular weight and 38 atom-type E-state indices were used as the inputs in 39-5-1 artificial neural networks. The results from multilinear regression analysis were r2 = 0.87 and RMS(LOO) = 0.55, respectively. For a test set of 35 nucleosides, 12 nucleoside bases, 19 drug compounds, and 50 general organic compounds (n = 116) not included in the training set, a predictive r2 = 0.94 and RMS = 0.41 were calculated by artificial neural networks. The results for the same set by multilinear regression were r2 = 0.86 and RMS = 0.72. The improved prediction ability of artificial neural networks can be attributed to the nonlinear properties of this method that allowed the detection of high-order relationships between E-state indices and the n-octanol/water partition coefficient. The present approach was found to be an accurate and fast method that can be used for the reliable estimation of log P values for even the most complex structures.

Estimation of aqueous solubility for a diverse set of organic compounds based on molecular topology

An accurate and generally applicable method for estimating aqueous solubilities for a diverse set of 1297 organic compounds based on multilinear regression and artificial neural network modeling was developed. Molecular connectivity, shape, and atom-type electrotopological state (E-state) indices were used as structural parameters. The data set was divided into a training set of 884 compounds and a randomly chosen test set of 413 compounds. The structural parameters in a 30-12-1 artificial neural network included 24 atom-type E-state indices and six other topological indices, and for the test set, a predictive r2 = 0.92 and s = 0.60 were achieved. With the same parameters the statistics in the multilinear regression were r2 = 0.88 and s = 0.71, respectively.

Boiling Point of a Set of Alkanes, Alcohols and Chloroalkanes: QSAR with Atom Type Electrotopological State Indices Using Artificial Neural Networks

The normal boiling point is modeled for a set of 372 saturated compounds, including 154 alkanes, 108 alcohols and 110 (poly)chloroalkanes. The newly introduced atom type electrotopological state indices serve as the structure variables and artificial neural networks (with back propagation of error) are used for the analysis. A network with a 6:7:1 architecture produces an average relative error of 0.97% for the whole data set, including the 21% of the data used as the test set. The mean absolute error (MAE) for this model is 4.00 for the whole set, corresponding to an rms error of 5.41; for the test set the MAE is 4.03 with an rms of 5.23. The low error on the test set indicates that this model has predictive power.

ChemInform Abstract: Boiling Point and Critical Temperature of a Heterogeneous Data Set: QSAR with Atom Type Electrotopological State Indices Using Artificial Neural Networks.

AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

Boiling Point and Critical Temperature of a Heterogeneous Data Set: QSAR with Atom Type Electrotopological State Indices Using Artificial Neural Networks

Two sets of heterogeneous organic compounds were analyzed with artificial neural networks using atom type electrotopological state indices. The first set contains the boiling point for 298 compounds; 30 were placed in a testing set. The neural network model used atom type E-state indices for the 19 atom types present in the data set; the actual network used for prediction had a 19:5:1 architecture. This model produced a mean absolute error (MAE) of 3.93 K for the overall set, 3.86 for the training set, and 4.57 for the test set. The average relative percent error for 10 runs is 0.94% for the whole data set and 1.12% for the test set. The second set contains critical temperatures for 165 compounds; 18 were placed in the testing set. The neural network possessed a 19:4:1 architecture and produced an MAE of 4.52 K for the whole set, 4.39 K for the training set, and 5.59 K for the test set. The average relative percent error for 5 runs is 0.77% for the whole data set and 0.95% for the test set.