CORAL: Development of a hybrid descriptor based QSTR model to predict the toxicity of dioxins and dioxin-like compounds with correlation intensity index and consensus modelling

QSAR models for the ozonation of diverse volatile organic compounds at different temperatures

In order to assess the fate and persistence of volatile organic compounds (VOCs) in the atmosphere, it is necessary to determine their oxidation rate constants for their reaction with ozone (kO3). However, given that experimental values of kO3 are only available for a few hundred compounds and their determination is expensive and time-consuming, developing predictive models for kO3 is of great importance. Thus, this study aimed to develop reliable quantitative structure-activity relationship (QSAR) models for 302 values of 149 VOCs across a broad temperature range (178-409 K). The model was constructed based on the combination of a simplified molecular-input line-entry system (SMILES) and temperature as an experimental condition, namely quasi-SMILES. In this study, temperature was incorporated in the models as an independent feature. The hybrid optimal descriptor generated from the combination of quasi-SMILES and HFG (hydrogen-filled graph) was used to develop reliable, accurate, and predictive QSAR models employing the CORAL software. The balance between the correlation method and four different target functions (target function without considering IIC or CII, target function using each IIC or CII, and target function based on the combination of IIC and CII) was used to improve the predictability of the QSAR models. The performance of the developed models based on different target functions was compared. The correlation intensity index (CII) significantly enhanced the predictability of the model. The best model was selected based on the numerical value of Rm2 of the calibration set (split #1, Rtrain2 = 0.9834, Rcalibration2 = 0.9276, Rvalidation2 = 0.9136, and calibration = 0.8770). The promoters of increase/decrease for log kO3 were also computed based on the best model. The presence of a double bond (BOND10000000 and $10 000 000 000), absence of halogen (HALO00000000), and the nearest neighbor codes for carbon equal to 321 (NNC-C⋯321) are some significant promoters of endpoint increase.

Monte Carlo Optimization Method Based QSAR Modeling of Placental Barrier Permeability

PURPOSE

In order to ensure that drug administration is safe during pregnancy, it is crucial to have the possibility to predict the placental permeability of drugs in humans. The experimental method which is most widely used for the said purpose is in vitro human placental perfusion, though the approach is highly expensive and time consuming. Quantitative structure-activity relationship (QSAR) modeling represents a powerful tool for the assessment of the drug placental transfer, and can be successfully employed to be an alternative in in vitro experiments.

METHODS

The conformation-independent QSAR models covered in the present study were developed through the use of the SMILES notation descriptors and local molecular graph invariants. What is more, the Monte Carlo optimization method, was used in the test sets and the training sets as the model developer with three independent molecular splits.

RESULTS

A range of different statistical parameters was used to validate the developed QSAR model, including the standard error of estimation, mean absolute error, root-mean-square error (RMSE), correlation coefficient, cross-validated correlation coefficient, Fisher ratio, MAE-based metrics and the correlation ideality index. Once the mentioned statistical methods were employed, an excellent predictive potential and robustness of the developed QSAR model was demonstrated. In addition, the molecular fragments, which are derived from the SMILES notation descriptors accounting for the decrease or increase in the investigated activity, were revealed.

CONCLUSION

The presented QSAR modeling can be an invaluable tool for the high-throughput screening of the placental permeability of drugs.

Predictive binary mixture toxicity modeling of fluoroquinolones (FQs) and the projection of toxicity of hypothetical binary FQ mixtures: a combination of 2D-QSAR and machine-learning approaches

All sorts of chemicals get degraded under various environmental stresses, and the degradates coexist with the parent compounds as mixtures in the environment. Antibiotics emerge as an additional concern due to the bioactive nature of both the parent compound and degradation products and their combined exposure to the environment. Therefore, environmental risk assessment of antibiotics and their degradation products is very much necessary. In this direction, we made use of in silico new approach methodologies (NAMs) and machine-learning algorithms. In this study, we have developed a robust and predictive mixture-quantitative structure-activity relationship (QSAR) model with promising quality and predictability (internal: MAETrain = 0.085, QLOO2 = 0.849, external: MAETest = 0.090, and QF12 = 0.859) for predicting the toxicity of the mixtures of a class of antibiotics and their degradation products. To obtain the predictive model, toxicity data of 78 binary fluoroquinolone mixtures in E. coli (endpoint: log 1/IC50 in molar) have been utilized. We have used only 0D-2D descriptors to efficiently encode the structural features of mixture components without any additional complexities. The optimization of the class of mixture descriptors has been performed in this study by using three different mixing rules (linear combination of molecular contributions, the squared molecular contributions, and the norm of molecular contributions). Different machine-learning approaches namely, random forest (RF), ada boost, gradient boost (GB), extreme gradient boost (XGB), support vector machine (SVM), linear support vector machine (LSVM), and ridge regression (RR) have been employed here apart from the conventional partial least squares (PLS) regression to optimize the modeling approach. A rigorous validation protocol has been used for assessing the goodness-of-fit, robustness, and external predictability of the models. Finally, the toxicity of possible untested mixtures of different photodegradation products of fluoroquinolones has been predicted using the best model reported in this study.

A simple and reliable QSPR model for prediction of chromatography retention indices of volatile organic compounds in peppers

Worldwide, various types of pepper are used in food as an additive due to their unique pungency, aroma, taste, and color. This spice is valued for its pungency contributed by the alkaloid piperine and aroma attributed to volatile essential oils. The essential oils are composed of volatile organic compounds (VOCs) in different concentrations and ratios. In chromatography, the identification of compounds is done by comparing obtained peaks with a reference standard. However, there are cases where reference standards are either unavailable or the chemical information of VOCs is not documented in reference libraries. To overcome these limitations, theoretical methodologies are applied to estimate the retention indices (RIs) of new VOCs. The aim of the present work is to develop a reliable QSPR model for the RIs of 273 identified VOCs of different types of pepper. Experimental retention indices were measured using comprehensive two-dimensional gas chromatography coupled to quadrupole mass spectrometry (GC × GC/qMS) using a coupled BPX5 and BP20 column system. The inbuilt Monte Carlo algorithm of CORAL software is used to generate QSPR models using the hybrid optimal descriptor extracted from a combination of SMILES and HFG (hydrogen-filled graph). The whole dataset of 273 VOCs is used to make ten splits, each of which is further divided into four sets: active training, passive training, calibration, and validation. The balance of correlation method with four target functions i.e. TF0 (WIIC = WCII = 0), TF1 (WIIC = 0.5 & WCII = 0), TF2 (WIIC = 0 & WCII = 0.3) and TF3 (WIIC = 0.5 & WCII = 0.3) is used. The results of the statistical parameters of each target function are compared with each other. The simultaneous application of the index of ideality of correlation (IIC) and correlation intensity index (CII) improves the predictive potential of the model. The best model is judged on the basis of the numerical value of R2 of the validation set. The statistical result of the best model for the validation set of split 6 computed with TF3 (WIIC = 0.5 & WCII = 0.3) is R2 = 0.9308, CCC = 0.9588, IIC = 0.7704, CII = 0.9549, Q2 = 0.9281 and RMSE = 0.544. The promoters of increase/decrease for RI are also extracted using the best model (split 6). Moreover, the proposed model was used for an external validation set.

Biodegradability analysis of Dioxins through in silico methods: Model construction and mechanism analysis

The biodegradation treatment of dioxins has long been of interest due to its good ecological and economic effects. In this study, the biodegradability of polychlorinated dibenzo-p-dioxins (PCDDs) were investigated by constructing machine learning and multiple linear regression models. The maximum chlorine atomic charge (qHirshfeldCl+), which characterizes the biodegradation ability of PCDDs, was used as the response value. The random forest model was used to rank the importance on the 1471 descriptors of PCDDs, and the BCUTp-1 h, QXZ, JGI4, ATSC8c, VE3_Dt, topoShape, and maxwHBa were screened as the important descriptors by Pearson's correlation coefficient method. A quantitative structure-activity relationship (QSAR) model was constructed to predict the biodegradability of PCDDs. In addition, the extreme gradient boosting (XGBoost) and random forest model were also constructed and proved the good predictability of QSAR model. The biodegradability of polychlorinated dibenzofurans (PCDFs) can also be predicted by the constructed three models from a certain level after adjusting some model parameters, which further proved the versatility of the models. Besides, the sensitivity analysis of the QSAR model and a 3D-QSAR model was developed to investigate the biodegradability mechanisms of PCDDs. Results showed that the descriptors BCUTp-1 h, JGI4, and maxwHBa were the key descriptors in the biodegradability effect by the sensitivity analysis of the QSAR model. Coupled with the results of PCDDs biodegradability 3D-QSAR model, BCUTp-1 h, JGI4, and maxwHBa were confirmed as the main descriptors that affect the biodegradability of dioxins. This study provides a novel theoretical perspective for the research of the biodegradation of both PCDDs and PCDFs dioxins.

Large Dataset-Based Regression Model of Chemical Toxicity to Vibrio fischeri

For the first time, a global regression quantitative structure-toxicity/activity relationship (QSTR/QSAR) model was developed for the toxicity of a large data set including 1236 chemicals towards Vibrio fischeri, by using random forest (RF) regression algorithm. The optimal RF model with RF parameters of mtry = 3, ntree = 150 and nodesize = 5 was based on 13 molecular descriptors. It can achieve accurate prediction for the toxicity of 99.1% of 1236 chemicals, and yield coefficients of determination R² of 0.893 for 930 log(Mw/IBC50) in the training set, 0.723 for 306 log(Mw/IBC50) in the test se, and 0.865 for 1236 toxicity log(Mw/IBC50) in the total set. The optimal RF global model proposed in this work is comparable to other published local QSTR models on small datasets of the toxicity to Vibrio fischeri.

The enhancement scheme for the predictive ability of QSAR: A case of mutagenicity

Mutagenicity is one of the most dangerous properties from the point of view of medicine and ecology. Experimental determination of mutagenicity remains a costly process, which makes it attractive to identify new hazardous compounds based on available experimental data through in silico methods or quantitative structure-activity relationships (QSAR). A system for constructing groups of random models is proposed for comparing various molecular features extracted from SMILES and graphs. For mutagenicity (mutagenicity values were expressed by the logarithm of the number of revertants per nanomole assayed by Salmonella typhimurium TA98-S9 microsomal preparation) models, the Morgan connectivity values are more informative than the comparison of quality for different rings in molecules. The resulting models were tested with the previously proposed model self-consistency system. The average value of the determination coefficient for the validation set is 0.8737 ± 0.0312.

Parsing structural fragments of thiazolidin-4-one based α-amylase inhibitors: A combined approach employing in vitro colorimetric screening and GA-MLR based QSAR modelling supported by molecular docking, molecular dynamics simulation and ADMET studies

α-Amylase (EC.3.2.1.1) is a ubiquitous digestive endoamylase. The abrupt rise in blood glucose levels due to the hydrolysis of carbohydrates by α-amylase at a faster rate is one of the main reasons for type 2 diabetes. The inhibitors prevent the action of digestive enzymes, slowing the digestion of carbs and eventually assisting in the management of postprandial hyperglycemia. In the course of developing α-amylase inhibitors, we have screened 2-aryliminothiazolidin-4-one based analogs for their in vitro α-amylase inhibitory potential and employed various in silico approaches for the detailed exploration of the bioactivity. The DNSA bioassay revealed that compounds 5c, 5e, 5h, 5j, 5m, 5o and 5t were more potent than the reference drug (IC₆₀ value = 22.94 ± 0.24 μg mL^-1). The derivative 5o with -NO₂ group at both the rings was the most potent analog with an IC₆₀ value of 19.67 ± 0.20 μg mL^-1 whereas derivative 5a with unsubstituted aromatic rings showed poor inhibitory potential with an IC₆₀ value of 33.40 ± 0.15 μg mL^-1. The reliable QSAR models were developed using the QSARINS software. The high value of R²_ext = 0.9632 for model IM-9 showed that the built model can be applied to predict the α-amylase inhibitory activity of the untested molecules. A consensus modelling approach was also employed to test the reliability and robustness of the developed QSAR models. Molecular docking and molecular dynamics were employed to validate the bioassay results by studying the conformational changes and interaction mechanisms. A step further, these compounds also exhibited good ADMET characteristics and bioavailability when tested for in silico pharmacokinetics prediction parameters.

Quantitative structure-activity relationship modeling for predication of inhibition potencies of imatinib derivatives using SMILES attributes

Chronic myelogenous leukemia (CML) which is resulted from the BCR-ABL tyrosine kinase (TK) chimeric oncoprotein, is a malignant clonal disorder of hematopoietic stem cells. Imatinib is used as an inhibitor of BCR-ABL TK in the treatment of CML patients. The main object of the present manuscript is focused on constructing quantitative activity relationships (QSARs) models for the prediction of inhibition potencies of a large series of imatinib derivatives against BCR-ABL TK. Herren, the inbuilt Monte Carlo algorithm of CORAL software is employed to develop QSAR models. The SMILES notations of chemical structures are used to compute the descriptor of correlation weights (CWs). QSAR models are established using the balance of correlation method with the index of ideality of correlation (IIC). The data set of 306 molecules is randomly divided into three splits. In QSAR modeling, the numerical value of R², Q², and IIC for the validation set of splits 1 to 3 are in the range of 0.7180-0.7755, 0.6891-0.7561, and 0.4431-0.8611 respectively. The numerical result of [Formula: see text] > 0.5 for all three constructed models in the Y-randomization test validate the reliability of established models. The promoters of increase/decrease for pIC₅₀ are recognized and used for the mechanistic interpretation of structural attributes.

Identifications of good and bad structural fragments of hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids with correlation intensity index and consensus modelling using Monte Carlo based QSAR studies, their molecular docking and ADME analysis

The application of QSAR along with other in silico tools like molecular docking, and molecular dynamics provide a lot of promise for finding new treatments for life-threatening diseases like Type 2 diabetes mellitus (T2DM). The present study is an attempt to develop Monte Carlo algorithm-based QSAR models using freely available CORAL software. The experimental data on the α-amylase inhibition by a series of benzothiazole-linked hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids were selected as endpoint for the model generation. Initially, a total of eight QSAR models were built using correlation intensity index (CII) as a criterion of predictive potential. The model developed from split 6 using CII was the most reliable because of the highest numerical value of the determination coefficient of the validation set (r²_VAL = 0.8739). The important structural fragments responsible for altering the endpoint were also extracted from the best-built model. With the goal of improved prediction quality and lower prediction errors, the validated models were used to build consensus models. Molecular docking was used to know the binding mode and pose of the selected derivatives. Further, to get insight into their metabolism by living beings, ADME studies were investigated using internet freeware, SwissADME.

Monte Carlo based QSGFEAR: prediction of Gibb's free energy of activation at different temperatures using SMILES based descriptors

Monte Carlo optimization based QSGFEAR model development using CII results in the formation of more reliable, robust and predictive models.