A simple approach for assessment of toxicity of nitroaromatic compounds without using complex descriptors and computer codes

Toxicity of individual and mixture of organic compounds to P. Phosphoreum and S. Capricornutum using interpretable simple structural parameters

Human and environmental ecosystem beings are exposed to multicomponent compound mixtures but the toxicity nature of compound mixtures is not alike to the individual chemicals. This work introduces four models for the prediction of the negative logarithm of median effective concentration (pEC50) of individual chemicals to marine bacteria Photobacterium Phosphoreum (P. Phosphoreum) and algal test species Selenastrum Capricornutum (S. Capricornutum) as well as their mixtures to P. Phosphoreum, and S. Capricornutum. These models provide the simplest approaches for the forecast of pEC50 of some classes of organic compounds from their interpretable structural parameters. Due to the lack of adequate toxicity data for chemical mixtures, the largest available experimental data of individual chemicals (55 data) and their mixtures (99 data) are used to derive the new correlations. The models of individual chemicals are based on two simple structural parameters but chemical mixture models require further interaction terms. The new model's results are compared with the outputs of the best accessible quantitative structure-activity relationships (QSARs) models. Various statistical parameters are done on the new and comparative complex QSAR models, which confirm the higher reliability and simplicity of the new correlations.

A reliable QSPR model for predicting drug release rate from metal-organic frameworks: a simple and robust drug delivery approach

During the drug release process, the drug is transferred from the starting point in the drug delivery system to the surface, and then to the release medium. Metal-organic frameworks (MOFs) potentially have unique features to be utilized as promising carriers for drug delivery, due to their suitable pore size, high surface area, and structural flexibility. The loading and release of various therapeutic drugs through the MOFs are effectively accomplished due to their tunable inorganic clusters and organic ligands. Since the drug release rate percentage (RES%) is a significant concern, a quantitative structure-property relationship (QSPR) method was applied to achieve an accurate model predicting the drug release rate from MOFs. Structure-based descriptors, including the number of nitrogen and oxygen atoms, along with two other adjusted descriptors, were applied for obtaining the best multilinear regression (BMLR) model. Drug release rates from 67 MOFs were applied to provide a precise model. The coefficients of determination (R2) for the training and test sets obtained were both 0.9999. The root mean square error for prediction (RMSEP) of the RES% values for the training and test sets were 0.006 and 0.005, respectively. To examine the precision of the model, external validation was performed through a set of new observations, which demonstrated that the model works to a satisfactory degree.

In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach

In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure-Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD₅₀). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR). The obtained Ensemble Model showed improved performance over the base SVR models in the training set (R² = 0.88), validation set (R² = 0.95), and true external test set (R² = 0.92). The models were also internally validated by 5-fold cross-validation and Y-scrambling experiments, showing that the models have high levels of goodness-of-fit, robustness and predictivity. The contribution of descriptors to the toxicity in the models was assessed using the Accumulated Local Effect (ALE) technique. The proposed approach provides an important tool to assess toxicity of nitroaromatic compounds, based on the ensemble QSAR model and the structural relationship to toxicity by analyzed contribution of the involved descriptors.

Enhanced Photocatalytic CO2 Reduction by Novel Designed Porphyrin-Based MOFs: From Accurate QSPR Model to Experimental Exploration

A reliable quantitative structure-property relationship (QSPR) model was established for predicting the evolution rate of CO₂ photoreduction over porphyrin-based metal-organic frameworks (MOFs) as photocatalysts. The determination coefficient (R ²) for both training and test sets was 0.999. The root-mean-squared error of prediction (RMSEP) obtained was 0.006 and 0.005 for training and test sets, respectively. Based on the proposed model, two porphyrin-based MOFs, Cu-PMOF and Co-PMOF, were designed, synthesized, and applied for CO₂ photoreduction under UV-visible irradiation without any additional photosensitizer. The X-ray diffraction (XRD), diffuse reflectance spectroscopy (DRS), and Fourier transform infrared (FTIR) measurements revealed the successful formation of the porous MOFs. The N₂ adsorption isotherms at 77 K showed a high Brunauer-Emmett-Teller (BET) surface area of 932.64 and 974.06 m²·g^-1 for Cu-PMOF and Co-PMOF, respectively. Theoretical and experimental results showed that HCOOH evolution rates over Cu-PMOF and Co-PMOF were (127.80, 101.62 μmol) and (130.6, 103.47 μmol), respectively. These results were robust and satisfactory.

A novel model for prediction of stability constants of the thiosemicarbazone ligands with different types of toxic heavy metal ions using structural parameters and multivariate linear regression method

A novel model is presented for reliable estimation of the stability constants of the thiosemicarbazone ligands with different types of toxic heavy metal ions (log β₁₁) in an aqueous solution, which has wide usage in environmental safety and ecotoxicology applications. The biggest reported data of log β₁₁ for 120 metalthiosemicarbazone complexes are used for deriving and testing the novel model. In contrast to available methods where they need the two-dimensional (2D) and three-dimensional (3D) complex molecular descriptors as well as expert users and computer codes, the novel correlation uses four additive and two non-additive structural parameters of thiosemicarbazone ligands. The calculated results of the novel correlation are compared with the outputs of the genetic algorithm with multivariate linear regression method (GA-MLR) as one of the best existing methods, which requires seven complex descriptors. The estimated results for 78 of training as well as 42 of two different test sets were established by external and internal validations. The values of statistical parameters comprising average deviation, average absolute deviation, average absolute relative deviation, absolute maximum deviation, and the coefficient of determination for 73 data of training set of New model/GA-MLR are 0.04/ - 0.25, 1.06/1.31, 14.4/18.7, 3.18/7.92, and 0.830/0.652, respectively. Thus, the predicted results of the new model are worthy as compared to the complex GA-MLR model. Moreover, assessments of various statistical parameters confirm that the new model provides great reliability, goodness-of-fit, accuracy, and precision.

Quantitative Structure-Activity Relationship (QSAR) Studies on the Toxic Effects of Nitroaromatic Compounds (NACs): A Systematic Review

Nitroaromatic compounds (NACs) are ubiquitous in the environment due to their extensive industrial applications. The recalcitrance of NACs causes their arduous degradation, subsequently bringing about potential threats to human health and environmental safety. The problem of how to effectively predict the toxicity of NACs has drawn public concern over time. Quantitative structure-activity relationship (QSAR) is introduced as a cost-effective tool to quantitatively predict the toxicity of toxicants. Both OECD (Organization for Economic Co-operation and Development) and REACH (Registration, Evaluation and Authorization of Chemicals) legislation have promoted the use of QSAR as it can significantly reduce living animal testing. Although numerous QSAR studies have been conducted to evaluate the toxicity of NACs, systematic reviews related to the QSAR modeling of NACs toxicity are less reported. The purpose of this review is to provide a thorough summary of recent QSAR studies on the toxic effects of NACs according to the corresponding classes of toxic response endpoints.

Two spectral QSPR models of porphyrin macromolecules for chelating heavy metals and different ligands released from industrial solvents: CH2Cl2, CHCl3 and toluene

Two simple and reliable correlations are introduced for the prediction of emission and absorption of porphyrins and their derivatives, i.e. metalloporphyrins and ligand coordinated metalloporphyrins. They can be used to sense the extracted precious metals. The proposed models require only simple structural parameters such as the number of carbon, metal and metal-free molecular fragments of desirable porphyrins or their derivatives. Since the proposed models depend on molecular structures of the desired compounds, they can be easily applied for complex molecular structures. Experimental data of 272 porphyrin derivatives were used to derive and test the novel models for the assessment of their emission (Em.) and absorption (Abs.) values in three solvents namely dichloromethane, toluene and chloroform. The values of the coefficients of determination (r ²) for the training set (183 compounds) in dichloromethane and three different test sets, corresponding to the three mentioned solvents, for the emission and absorption correlations were greater than 0.70. The calculated values of the root-mean-square error (RMSE) for the training sets of Em. and Abs. correlations were equal to 7.56 and 4.86 nm, respectively. Further statistical parameters also confirm the high reliability of the new models.