Application of a genetic algorithm and an artificial neural network for global prediction of the toxicity of phenols to Tetrahymena pyriformis

2,4-disubstituted 6-fluoroquinolines as potent antiplasmodial agents: QSAR, homology modeling, molecular docking and ADMET studies

Objective

This work was designed to study 2,4-disubstituted 6-fluoroquinolines as antiplasmodial agents by using in silico techniques, to aid in the design of novel analogs with high potency against malaria and high inhibition of Plasmodium falciparum translation elongation factor 2 (PfeEF2), a novel drug target.

Methods

Quantitative structure-activity relationships (QSAR) of 2,4-disubstituted 6-fluoroquinolines were studied with the genetic function approximation technique in Material Studio software. The 3D structure of PfeEF2 was modeled in the SWISS-MODEL workspace through homology modeling. A molecular docking study of the modeled PfeEF2 and 2,4-disubstituted 6-fluoroquinolines was conducted with Autodock Vina in Pyrx software. Furthermore, the in silico pharmacokinetic properties of selected compounds were investigated.

Results

A robust, reliable and predictive QSAR model was developed that related the chemical structures of 2,4-disubstituted 6-fluoroquinolines to their antiplasmodium activities. The model had an internal squared correlation coefficient R2 of 0.921, adjusted squared correlation coefficient R2adj of 0.878, leave-one-out cross-validation coefficient Q2cv of 0.801 and predictive squared correlation coefficient R2pred of 0.901. The antiplasmodium activity of 6-fluoroquinolines was found to depend on the n5Ring, GGI9, TDB7u, TDB8u and RDF75i physicochemical properties: n5Ring, TDB8u and RDF75i were positively associated, whereas GGI9 and TDB7u were negatively associated, with the antiplasmodium activity of the compounds. Stable complexes formed between the compounds and modeled PfeEF2, with binding affinity ranging from -8.200 to -10.700 kcal/mol. Compounds 5, 11, 16, 22 and 24 had better binding affinities than quinoline-4-carboxamide (DDD107498), as well as good pharmacokinetic properties, and therefore may be better inhibitors of this novel target.

Conclusion

QSAR and docking studies provided insight into designing novel 2,4-disubstituted 6-fluoroquinolines with high antiplasmodial activity and good structural properties for inhibiting a novel antimalarial drug target.

Assessing chemical hazard and unraveling binding affinity of priority pollutants to lignin modifying enzymes for environmental remediation

Lignin-modifying enzymes (LMEs) are impactful biocatalysts in environmental remediation applications. However, LMEs-assisted experimental degradation neglects the molecular basis of pollutant degradation. Furthermore, throughout the remediation process, the inherent hazards of environmental pollutants remain untapped for in-depth toxicological endpoints. In this investigation, a predictive toxicological framework and a computational framework adopting LMEs were employed to assess the hazards of Priority Pollutants (PP) and its possible LMEs-assisted catalytic screening. The potential hazardous outcomes of PP were assessed using Quantitative structure-activity relationship (QSARs)-based techniques including Toxtree, ECOSAR, and T.E.S.T. tools. Toxicological findings revealed positive outcomes in a multitude of endpoints for all PP. The PP compound 2,3,7,8-TCDD (dioxin) was found to exhibit the lowest concentration of aquatic toxicity implementing aquatic model systems; LC₅₀ as 0.01, 0.01, 0.04 (mg L^-1) for Fish (96 H), Daphnid (48 H), Green algae (96 H) respectively. T.E.S.T. results revealed that chloroform, and 2-chlorophenol both seem to be developmental toxicants. Subsequently, LMEs-assisted docking procedure was employed in predictive mitigation of PP. The docking approach as predicted degradation revealed the far lowest docking energy score for Versatile peroxidase (VP)- 2,3,7,8-TCDD docked complex with a binding energy of -9.2 (kcal mol^-1), involved PHE-46, PRO-139, PRO-141, ILE-148, LEU-165, HIS-169, LEU-228, MET-262, and MET-265 as key interacting amino acid residues. Second most ranked but lesser than VP, Lignin peroxidase (LiP)- 2,3,7,8-TCDD docked complex exhibited a rather lower binding affinity score (-8.8 kcal mol^-1). Predictive degradation screening employing comparative docking revealed varying binding affinities, portraying that each LMEs member has independent feasibility to bind PP as substrate. Predictive findings endorsed the hazardous nature of associated PP in a multitude of endpoints, which could be attenuated by undertaking LMEs as a predictive approach to protect the environment and implement it in regulatory considerations.

Versatile in silico modeling of XAD-air partition coefficients for POPs based on abraham descriptor and temperature

The concentration of persistent organic pollutants (POPs) makes remarkable difference to environmental fate. In the field of passive sampling, the partition coefficients between polystyrene-divinylbenzene resin (XAD) and air (i.e., K_XAD-A) are indispensable to obtain POPs concentration, and the K_XAD-A is generally thought to be governed by temperature and molecular structure of POPs. However, experimental determination of K_XAD-A is unrealistic for countless and novel chemicals. Herein, the Abraham solute descriptors of poly parameter linear free energy relationship (pp-LFER) and temperature were utilized to develop models, namely pp-LFER-T, for predicting K_XAD-A values. Two linear (MLR and LASSO) and four nonlinear (ANN, SVM, kNN and RF) machine learning algorithms were employed to develop models based on a data set of 307 sample points. For the aforementioned six models, R²_adj and Q²_ext were both beyond 0.90, indicating distinguished goodness-of-fit and robust generalization ability. By comparing the established models, the best model was observed as the RF model with R²_adj = 0.991, Q²_ext = 0.935, RMSE_tra = 0.271 and RMSE_ext = 0.868. The mechanism interpretation revealed that the temperature, size of molecules and dipole-type interactions were the predominant factors affecting K_XAD-A values. Concurrently, the developed models with the broad applicability domain provide available tools to fill the experimental data gap for untested chemicals. In addition, the developed models were helpful to preliminarily evaluate the environmental ecological risk and understand the adsorption behavior of POPs between XAD membrane and air.

Prediction models with multiple machine learning algorithms for POPs: The calculation of PDMS-air partition coefficient from molecular descriptor

Polydimethylsiloxane-air partition coefficient (K_PDMS-air) is a key parameter for passive sampling to measure POPs concentrations. In this study, 13 QSPR models were developed to predict K_PDMS-air, with two descriptor selection methods (MLR and RF) and seven algorithms (MLR, LASSO, ANN, SVM, kNN, RF and GBDT). All models were based on a data set of 244 POPs from 13 different categories. The diverse model evaluation parameters calculated from training and test set were used for internal and external verification. Notably, the R_adj², Q_BOOT² and Q_ext² are 0.995, 0.980 and 0.951 respectively for GBDT model, showing remarkable superiority in fitting, robustness and predictability compared with other models. The discovery that molecular size, branches and types of the bonds were the main internal factors affecting the partition process was revealed by mechanism explanation. Different from the existing QSPR models based on single category compounds, the models developed herein considered multiple classes compounds, so that its application domain was more comprehensive. Therefore, the obtained models can fill the data gap of missing experimental K_PDMS-air values for compounds in the application range, and help researchers better understand the distribution behavior of POPs from the perspective of molecular structure.

Theoretical design of novel antimalarial agents against P. falciparum strain, Dd2 through the QSAR modeling of synthesized 2'-substituted triclosan derivatives

In an attempt to design compounds with higher antimalarial activities, quantitative structure-activity relationship (QSAR) technique was utilized in the development of a molecular model for some synthesized 2′-substituted triclosan derivatives through a hybrid of the GA-MLR method. The model was found to have excellent statistical parameters (R² = 0.8919, R²_Adj = 0.8728, LOF = 0.2563). The descriptors mean effect (MF) revealed BCUTw-1l, which increases with an increase in molecular weight, to be the most contributive to the antimalarial activity. Consequently, compound 3, with the highest activities (pEC₅₀ = 6.9586) was deployed as the design template. The molecular weight of the template was increasing through substitutions of its atoms at several positions with heavier atoms/groups to increases the descriptor (BCUTw-1l) value. Twelves (12) theoretical derivatives of the template were designed where six of the designed derivatives have better activity than the design template. The most active designed compound, 3L was found to have the highest antimalarial activity (pEC₅₀ = 7.930) than that of the design template.

Versatile modelling of polyoxymethylene-water partition coefficients for hydrophobic organic contaminants using linear and nonlinear approaches

Environmental fate or transport of hydrophobic organic contaminants (HOCs) depends on the partitioning properties of compounds within various environmental phases. Due to the wide application of polyoxymethylene (POM) in the passive sampling technique, several in silico models were developed to predict POM-water partition coefficients (K_POM-w) in accordance with the guidelines of the Organization for Economic Cooperation and Development (OECD). It is an attempt to combine conventional linear method (multiple linear regression, MLR) and popular nonlinear algorithm (artificial neural network, ANN) for estimating partition coefficients of HOCs. All models were performed on a dataset of 210 chemicals from 13 different classes. The polyparameter linear free energy relationship (pp-LFER) model included 5 molecular descriptors, namely, E, S, A, B and V, and predicted log K_POM-w with R²_adj of 0.825. The values of statistical parameters including R²_adj, Q²_ext, RMSE_tra and RMSE_ext for quantitative structure-property relationship (QSPR)-MLR and QSPR-ANN models with four descriptors (ALOGP, MeanDD, E1m and Mor24s) were: (0.928, 0.877, 0.498 and 0.649) and (0.943, 0.905, 0.443 and 0.571), with high similarity for both models, which confirmed the robustness, significance, and remarkable prediction accuracy of the QSPR models. Moreover, the mechanism interpretation revealed that the molecular volume and hydrophobicity had a major impact on distribution procedure of HOCs. The models developed herein, with the broad applicability domain (AD), provide suitable tools to fill the experimental data gap for untested chemicals and help researchers better understand the mechanistic basis of adsorption behavior of POM.

QSPR study on the polyacrylate-water partition coefficients of hydrophobic organic compounds

The partition coefficient is essential for the analysis of organic chemicals using solid-phase microextraction (SPME) techniques. In this study, a quantitative structure-property relationship (QSPR) model was developed with chemical descriptors for the prediction of the polyacrylate (PA)-water partition coefficient (K_PA-w). The major variables influencing K_PA-w in the QSPR model were CrippenlogP (crippen octanal-water partition coefficient), RNCG (relative negative charge-most negative charge/total negative charge), VE2_Dzv (average coefficient sum of the last eigenvector from the Barysz matrix/weighted by van der Waals volume), and ATSC4v (centred Broto-Moreau autocorrelation-lag 4/weighted by van der Waals volume). The relative determination coefficient (R²) and cross-validation coefficient (Q²) were 0.898 and 0.858, respectively, which implied that the model had excellent robustness. Mechanistic interpretation suggested that the factors affecting the partitioning process between PA and water are the hydrophobicity, relative negative charge, and van der Waals volume of a chemical. The results of this study provide a good tool for predicting the log K_PA-w values of diverse hydrophobic organic compounds (HOCs) within the applicability domain to reduce experimental costs and the time required for innovation.

QSAR and molecular docking studies of 1,3-dioxoisoindoline-4-aminoquinolines as potent antiplasmodium hybrid compounds

Quantitative structure-activity relationships (QSAR) provides a model that link biological activities of compounds to thier chemical stuctures and molecular docking study reveals the interaction between drug and its target enzyme. These studies were conducted on 1,3-dioxoisoindoline-4-aminoquinolines with the aim of producing a model that could be used to design highly potent antiplasmodium. The compounds were first optimized using Density Functional Theory (DFT) with basis set B3LYP/6-31G∗ then their descriptors calculated. Genetic Function Algorithm (GFA) was used to select descriptors and build the model. One of the four models generated was found to be the best having internal and external squared correlation coefficient (R 2) of 0.9459 and 0.7015 respectively, adjusted squared correlation coefficient (R adj) of 0.9278, leave-one-out (LOO) cross-validation coefficient (Q 2 cv) of 0.8882. The model shows that antiplasmodial activities of 1,3-dioxoisoindoline-4-aminoquinolines depend on ATSC5i, GATS8p, minHBint3, minHBint5, MLFER_A and topoShape descriptors. The model was validated to be predictive, robust and reliable. Hence, it can predict the antiplasmodium activities of new 1,3-dioxoisoindoline-4-aminoquinolines.The docking result indicates strong binding between 1,3-dioxoisoindoline-4-aminoquinolines and Plasmodium falciparum lactate dehydrogenase (pfLDH), and revealed the important of the morpholinyl substituent and amide linker in inhibiting pfLDH. These results could serve as a model for designing novel 1,3-dioxoisoindoline-4-aminoquinolines as inhibitors of PfLDH with higher antiplasmodial activities.

A dielectric and spectrophotometric study of the tautomerization of 2-hydroxypyridine and 2-mercaptopyridine in water

The basic ionization (pk₁) and acidic ionization (pk₂) constants and equilibrium constant (K_T) of 2HPy and 2MPy were determined. The pk₁(s) of their N- and X-methyl derivatives (X = O, S) were also determined. The equilibrium constant of 2MPy is approximately 60 times greater than its oxygen analog, 2HPy. The micro-ionization constants of the functional groups, –NH (pk_A and pk_C) and –XH (pk_B and pk_D), were determined to provide further insights into the ionization equilibria of these N-heteroaromatic XH compounds (2HPy and 2MPy). The relaxation time of water (τ) in aqueous solutions of 2HPy and 2MPy are collectively used with the K_T values to determine the forward (k_f) and backward (k_b) rate constants of tautomerization. Subsequently, the k_f and k_b are used to provide the rationale for the K_T and τ values.

The basic ionization (pk₁) and acidic ionization (pk₂) constants and equilibrium constant (K_T) of 2HPy and 2MPy were determined.

QSAR Study of 17β-HSD3 Inhibitors by Genetic Algorithm-Support Vector Machine as a Target Receptor for the Treatment of Prostate Cancer

The 17β-HSD3 enzyme plays a key role in treatment of prostate cancer and small inhibitors can be used to efficiently target it. In the present study, the multiple linear regression (MLR), and support vector machine (SVM) methods were used to interpret the chemical structural functionality against the inhibition activity of some 17β-HSD3inhibitors. Chemical structural information were described through various types of molecular descriptors and genetic algorithm (GA) was applied to decrease the complexity of inhibition pathway to a few relevant molecular descriptors. Non-linear method (GA-SVM) showed to be better than the linear (GA-MLR) method in terms of the internal and the external prediction accuracy. The SVM model, with high statistical significance (R2train = 0.938; R2test = 0.870), was found to be useful for estimating the inhibition activity of 17β-HSD3 inhibitors. The models were validated rigorously through leave-one-out cross-validation and several compounds as external test set. Furthermore, the external predictive power of the proposed model was examined by considering modified R2 and concordance correlation coefficient values, Golbraikh and Tropsha acceptable model criteria's, and an extra evaluation set from an external data set. Applicability domain of the linear model was carefully defined using Williams plot. Moreover, Euclidean based applicability domain was applied to define the chemical structural diversity of the evaluation set and training set.

Prediction of acute toxicity of phenol derivatives using multiple linear regression approach for Tetrahymena pyriformis contaminant identification in a median-size database

In this article, the modeling of inhibitory grown activity against Tetrahymena pyriformis is described. The 0-2D Dragon descriptors based on structural aspects to gain some knowledge of factors influencing aquatic toxicity are mainly used. Besides, it is done by some enlarged data of phenol derivatives described for the first time and composed of 358 chemicals. It overcomes the previous datasets with about one hundred compounds. Moreover, the results of the model evaluation by the parameters in the training, prediction and validation give adequate results comparable with those of the previous works. The more influential descriptors included in the model are: X3A, MWC02, MWC10 and piPC03 with positive contributions to the dependent variable; and MWC09, piPC02 and TPC with negative contributions. In a next step, a median-size database of nearly 8000 phenolic compounds extracted from ChEMBL was evaluated with the quantitative-structure toxicity relationship (QSTR) model developed providing some clues (SARs) for identification of ecotoxicological compounds. The outcome of this report is very useful to screen chemical databases for finding the compounds responsible of aquatic contamination in the biomarker used in the current work.

A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, domain of application and prediction

Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q(2)ext and the root mean square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides.