CP-MLR directed QSAR studies on the antimycobacterial activity of functionalized alkenols—topological descriptors in modeling the activity

Reference dose prediction by using CDK molecular descriptors: A non-experimental method

Reference dose (RfD) is an estimate of a daily dose that individual can be exposed chronically without obvious deleterious effects during a lifetime. In the area of toxicology, researchers always use the traditional approach by employing NOAEL/LOAEL or the benchmark dose (BMD) and other dose-response approaches to estimate RfD. These methods have, despite their typicalness, certain limitations. In this study, we present a novel method of the estimation of reference dose without experiments. The information of the organic chemicals is available from the Integrated Risk Information System (IRIS) of USEPA. Molecular descriptors for each molecular structure were calculated by an integrated platform, and the chemicals were classified into four categories based on molecular similarity: 128 contained benzene rings, 47 were heteroaromatics, 104 contained halogen substituents and 44 were halogenated aliphatic hydrocarbons. The predictive model of RfD was constructed by the multiple linear stepwise regression (MLR) method. Approximately 95% and 82% of the data points differ by less than 10-fold and 5-fold between the predicted values and the true values respectively. The non-experimental method improves the estimation efficiency and has a certain reference value to predict.

Azole Compounds as Inhibitors of Candida albicans: QSAR Modelling

Candida albicans is a pathogenic opportunistic yeast found in the human gut flora. It may also live outside of the human body, causing diseases ranging from minor to deadly. Candida albicans begins as a budding yeast that can become hyphae in response to a variety of environmental or biological triggers. The hyphae form is responsible for the development of multidrug resistant biofilms, despite the fact that both forms have been associated to virulence Here, we have proposed a linear and SPA-linear quantitative structure activity relationship (QSAR) modeling and prediction of Candida albicans inhibitors. A data set that consisted of 60 derivatives of benzoxazoles, benzimidazoles, oxazolo (4, 5-b) pyridines have been used. In this study, that after applying the leverage analysis method to detect outliers' molecules, the total number of these compounds reached 55. SPA-MLR model shows superiority over the multiple linear regressions (MLR) by accounting 90% of the Q ² of anti-fungus derivatives 'activity. This paper focuses on investigating the role of SPA-MLR in developing model. The accuracy of SPA-MLR model was illustrated using leave-one-out (LOO). The mean effect of descriptors and sensitivity analysis show that RDF090u is the most important parameter affecting the as behavior of the inhibitors of Candida albicans.

Versatile in silico modeling of partition coefficients of organic compounds in polydimethylsiloxane using linear and nonlinear methods

Environmental fate, behavior and effects of hazardous organic compounds have recently received great attention in diverse environmental phases, including water, atmosphere, soil and sediment. Considering polydimethylsiloxane (PDMS) fibers were validated for the wide application in the determination of partition behavior in passive sampling, in this work, several in silico models were established to predict PDMS-water (K_PDMS-w), PDMS-air (K_PDMS-a) and PDMS-seawater partition coefficients (K_PDMS-sw) of diverse chemicals. This is an attempt to combine conventional linear method and popular nonlinear algorithm for the estimation of partition coefficients between PDMS and different environmental media. All of the developed models showed satisfactory goodness-of-fit with high adjusted correlation coefficient (R²_adj) and were validated to be robust, stable and predictable by various internal and external validation techniques, deriving a wide series of statistical checks. Moreover, it was found that hydrophobicity, polarizability, charge distribution and molecular size of compounds contributed significantly to the model development by interpreting the selected descriptors. Based on the broad applicability domains (ADs), the current study provides suitable tools to fill the experimental data gap for other compounds and to help researchers better understand the mechanistic basis of adsorption behavior of PDMS.

CP-MLR directed QSAR study of carbonic anhydrase inhibitors: sulfonamide and sulfamate inhibitors

The inhibition activities of sulfonamide and sulfamate derivatives for human carbonic anhydrases have been quantitatively analyzed using DRAGON descriptors. QSAR models have been obtained through combinatorial protocol-multiple linear regression (CP-MLR) computational procedure. For the hCA I inhibition activity, a higher value of information content index of the 1-order neighborhood symmetry (IC1) and a lower value of the Moran autocorrelations, MATS2v and MATS1p, along with a lower number of sulfur atoms in a molecular structure (nRSR) is beneficial to the activity. A higher number of 5-membered rings (nR05), a bigger distance between nitrogen and sulfur T(N..S), and a higher value of van der Waals volume weighted descriptor (GATS6v), are helpful to improve the hCA II inhibition activity. For the inhibition of hpCA, a lower value of the descriptors Jhetv and PW5, and higher values of the eigenvalue sum from Z weighted distance matrix, SEigZ, the Moran autocorrelation of lag 8 weighted by atomic van der Waals volumes, MATS8v and the Moran autocorrelation of lag 4 weighted by atomic Sanderson electronegativities, MATS4e are favorable. The derived significant models in such descriptors may further be used to synthesize new potential compounds and to decipher the mode of their actions at molecular level.

Modeling the antileishmanial activity screening of 5-nitro-2-heterocyclic benzylidene hydrazides using different chemometrics methods

QSAR analysis for modeling the antileishmanial activity screening of a series of 49 nitro derivatives of Hydrazides were carried out using different Chemometrics methods. First, a large number of descriptors were calculated using Hyperchem, Mopac and Dragon softwares. Then, a suitable number of these descriptors were selected using multiple linear regression (MLR) technique. Then selected descriptors were used as inputs for artificial neural networks with three different weight update functions including Levenberg-Marquardt back propagation network (LM-ANN), resilient back propagation network (RP-ANN) and variable learning rate algorithm (GDX-ANN). The best artificial neural network model was an LM-ANN with a 5-5-1 architecture. Comparison of the results indicates that the LM-ANN method has better predictive power than the other methods.

Modeling of cyclooxygenase-2 and 5-lipooxygenase inhibitory activity of apoptosis-inducing agents potentially useful in prostate cancer chemotherapy: derivatives of diarylpyrazole

The structure-activity models of the twenty derivatives for COX-2 and ten derivatives of 1,5-diarylpyrazole for 5-LOX inhibitory activity have been investigated using Combinatorial Protocol in Multiple Linear Regression (CP-MLR) with topological descriptors which were calculated from DRAGON software. Among the descriptor classes considered collectively in the study the COX-2 inhibitory activity was, however, correlated with topological (TOPO) and Galvez topological charge indices (GVZ). Modified Burden eigenvalues (BCUT) and 2D autocorrelations (2DAUTO) classes of descriptors have shown correlation to 5-LOX inhibitory activity. The developed models and participating descriptors in them have suggested that the substitutional modification in the diarylpyrazole moiety may have sufficient scope in optimization of prevailing inhibitory activities of these analogues.

Modeling of the growth hormone secretagogue receptor antagonistic activity using chemometric tools

A quantitative structure-activity relationship (QSAR) study has been carried out on growth hormone secretagogue receptor antagonistic activity of the derivatives of 2,4-diaminopyrimidine. To obtain significant QSARs, the approaches involving the non-parametric such as Fujita-Ban, and the parametric based on physicochemical and DRAGON descriptors in Hansch type of analysis have been employed. The Fujita-Ban approach, however, was constrained to 18 compounds only due to a limited number of substituents appeared at varying positions. The derived contributions of different substituents and the parent moiety were used to identify the potential congeners. The physicochemical model of Hansch was subsequently used to interpret the type of interactions involved between the receptor sites and varying positions of these compounds. The study, employing DRAGON descriptors in Hansch approach was also carried out on this data set to discuss the prevailing interactions in terms of topological descriptors. The derived highest significant model was discussed to delineate the type of interactions involved and suggestions have been made for different alterations to lead to further potential compounds of the series.

Topological descriptors in modeling malonyl coenzyme A decarboxylase inhibitory activity: N-Alkyl-N-(1,1,1,3,3,3-hexafluoro-2-hydroxypropylphenyl)amide derivatives

The malonyl-CoA decarboxylase (MCD) inhibition activity of derivatives of N-alkyl-N-(1,1,1,3,3,3-hexafluoro-2-hydroxypropylphenyl)amide has been analyzed through combinatorial protocol in multiple linear regression (CP-MLR) using different topological descriptors obtained from Dragon software for the energy minimized 3D-structures of these molecules. Among the topological descriptor classes considered in the study, the MCD inhibition activity is correlated with simple topological descriptors (TOPO) and 2D-autocorrelation descriptors (2DAUTO). The complementary information contents having neighborhood symmetry of 2-order, CIC2 from the TOPO class, the Geary autocorrelations-lag 8, weighted by atomic Sanderson electronegativities, GATS8e and the Moran autocorrelations-lag 6, weighted by atomic Sanderson electronegativities, MATS6e both from 2DAUTO class have contributed significantly in the development of a statistical significant model.

Quantitative structure-toxicity relationship study of lethal concentration to tadpole (Bufo vulgaris formosus) for organophosphorous pesticides

In the present study more than 1,000 structural parameters of 41 organophosphorus pesticides (OPs) were calculated using the software ChemOffice 8.03 and Dragon 2.1. Then, with multivariate linear regression and best subset regression analyses, different equations were derived to calculate the lethal toxicity, LC(50), for these 41 organophosphorous pesticides found in tadpoles (Bufo vulgaris formosus). An equation was developed for all selected OPs, especially those with relatively low toxicity levels (LC(50)>4.5mM) that accounted for 89.09% of the variability in the toxic effect. The equation indicated that the main contributions to OPs toxicity with tadpoles were the electrostatic contribution qH(+) (maximum net positive H atomic charge), spatial autocorrelation (MATS7 m) and hydrophobicity (lgK(ow)), with the two former being the most important parameters. For OPs with high toxicity, however, different structural parameters were introduced. The following equation was developed with LC(50)<4.5mM. These equations implied that with different levels of toxicity there could have different mechanisms in the tadpole. Furthermore, the results showed that molecular structural parameters had a particular value in modeling chemical reactivity within a homologous series of compounds.

QSAR study on tetrahydroquinoline analogues as plasmodium protein farnesyltransferase inhibitors: a comparison of rationales of malarial and mammalian enzyme inhibitory activities for selectivity

The quantitative structure-activity relationships of Plasmodium falciparum and Rat protein farnesyltransferase (PFT) inhibitory activities of 6-cyano-1-(3-methyl-3H-imidazoly-4-ylmethyl)-3-substituted-1,2,3,4-tetrahydroquinoline (THQ) analogues are investigated in order to explore the similarities/deviations between the two enzymes for these analogues. The structure space of a ligand (BMS-214662) bound to Rat-PFT (PDB code 1SA5) has been used as the conformational space of the compounds under investigation. The study has been carried out using the combinatorial protocol in multiple linear regression with several 2D- and 3D-descriptors from molecular operating environment (MOE) representing the physicochemical and electronic features of the compounds. The molecular potential energy and partially charged van der Waals surface areas have taken part in the PFT models. They suggested in favor of molecular arrangement with minimum energy and low positively/negatively charged surfaces for optimum Pf-PFT inhibitory activity. Furthermore, less hydrophobic compounds are preferred for the activity. The Rat-PFT inhibitory activity models suggested in favor of more negatively as well as more positively charged surface area descriptors for the better activity. The PLS analysis carried out on the descriptors of the Pf-PFT and Rat-PFT models suggested that among the parameters, the partially charged surface areas in the range -0.20 to -0.15 (PEOE_VSA-3) and -0.30 to -0.25 (PEOE_VSA-5), hydrophobicity (a_hyd, logP(o/w) and SlogP_VSA4), and electronic energy (PM3_Eele) of the molecules hold promise for modulating the Pf-PFT/R-PFT inhibitory activities of the compounds. This suggested the possibility of modulating the Pf-PFT/R-PFT inhibitory activities and bringing about selectivity in the THQ analogues for the malarial parasite enzyme.

QSAR Studies on Chalcones and Flavonoids as Anti-tuberculosis Agents Using Genetic Function Approximation (GFA) Method

Design of compounds having good anti-tubercular activity is gaining much importance in the field of tuberculosis research due to reemergence of antibiotic resistance strains. In this paper quantitative structure activity relationships (QSAR) were developed on chalcones, chalcone-like compounds, flavones and flavanones to understand the relationship between biological activity and structural features. Genetic function approximation (GFA) method was used to identify the descriptors that would lead to good regression equations. The best molecular descriptors identified were Jurs descriptors (Jurs charged partial surface area), hydrogen bond donor, principal moment of inertia, molecular energy, dipole magnetic, molecular area, absorption, distribution, metabolism and excretion (ADME) properties and Chi indices (Kier & Hall chi connectivity indices). Excellent statistically significant models were developed by this approach (r(2)=0.8-0.97) for the four groups of compounds. The cross validated r(2) (XV r(2)) which is an indication of the predictive capability of the model for all the cases was also very good (=0.79-0.94).

Bayesian-regularized genetic neural networks applied to the modeling of non-peptide antagonists for the human luteinizing hormone-releasing hormone receptor

Bayesian-regularized genetic neural networks (BRGNNs) were used to model the binding affinity (IC(50)) for 128 non-peptide antagonists for the human luteinizing hormone-releasing hormone (LHRH) receptor using 2D spatial autocorrelation vectors. As a preliminary step, a linear dependence was established by multiple linear regression (MLR) approach, selecting the relevant descriptors by genetic algorithm (GA) feature selection. The linear model showed to fit the training set (N=102) with R(2)=0.746, meanwhile BRGNN exhibited a higher value of R(2)=0.871. Beyond the improvement of training set fitting, the BRGNN model overcame the linear one by being able to describe 85% of test set (N=26) variance in comparison with 73% the MLR model. Our non-linear QSAR model illustrates the importance of an adequate distribution of atomic properties represented in topological frames and reveals the electronegativities, masses and polarizabilities as the most influencing atomic properties in the structures of the heterocycles under analysis for having an appropriate LHRH antagonistic activity. Furthermore, the ability of the non-linear selected variables for differentiating the data was evidenced when total data set was well distributed in a Kohonen self-organizing map (SOM).

Inhibitory mode of 2-acetoxyphenyl alkyl sulfides against COX-1 and COX-2: QSAR analyses

Selective inhibition of cyclooxygenase-2 (COX-2) inhibitors is an important strategy in design of potent anti-inflammatory compounds with significantly reduced side effects. Therefore, QSAR studies of 2-acetoxyphenyl alkyl sulfides were performed using Bioloom, CAChe 6.1, and Dragon 3.0 for the COX-2 and COX-1 inhibition. The analyses have produced good predictive and statistically significant QSAR models. These studies suggest that lipophilicity affects both COX-1 and COX-2 inhibition in different manner and indicator variables like presence of aromatic ring and triple bond play an important role in COX-2 selectivity. Branching in the molecule, higher path length 6 rich in polarizability, and lesser number of carbonyl groups would be favorable for COX-2 inhibition. Fourth highest eigenvalue of burden matrix corresponding to atomic mass would be favorable for COX-2 inhibition and sixth lowest eigenvalue of burden matrix corresponding to Sanderson electronegativities is conducive for COX-1 inhibition. Lower path length 3 rich in atomic mass and lesser degree of unsaturation in the molecule would be favorable for COX-1 inhibition.

Application of linear discriminant analysis in the virtual screening of antichagasic drugs through trypanothione reductase inhibition

We have performed virtual screening to identify new lead trypanothione reductase inhibitor (TRI) compounds, enzyme present in Tripanozoma cruzi, the agent responsible of Chagas disease. From a training set of 58 compounds, linear discriminant analysis (LDA) was performed using 2D and 3D descriptors as discriminating variables in order to find out which function of descriptors characterizes the active TRI. The values of the statistical parameters F--Snedecor and Wilk's lambda for the discriminant function (DF) showed good statistical significance, as long as the rate of success in the prediction for both the training and the test set: 91.38% and 88.63%, in that order. Internal validation through the Leave--Group--Out methodology was performed with good results, assuring the stability of the DF. Afterwards, the DF was applied in virtual screening of 422,367 compounds. The optimum range of values of octanol--water partition coefficient for a compound to develop trypanothione reductase inhibition was applied as a second filtering criteria. 739 structurally heterogeneous drugs of the virtual library were selected as promissory TRI.

Modeling of activity of cyclic urea HIV-1 protease inhibitors using regularized-artificial neural networks

Artificial neural networks (ANNs) were used to model both inhibition of HIV-1 protease (K(i)) and inhibition of HIV replication (IC90) for 55 cyclic urea derivatives using constitutional and 2D descriptors. As a preliminary step, linear dependences were established by multiple linear regression (MLR) approaches, selecting the relevant descriptors by genetic algorithm (GA) feature selection. For ANN models non-linear GA feature selection was also applied. Non-linear modeling of K(i) overcame the results of the linear one using four properties, keeping in mind standard Pearson R correlation coefficients (0.931 vs. 0.862) and leave one out (LOO) cross-validation analysis (Q(LOO)2 = 0.703 vs. 0.510). On the other hand, IC90 modeling was insoluble by a linear approach: no predictive model was achieved; however, a non-linear relation was encountered according to statistic results (R = 0.891; Q(LOO)2 = 0.568). The best non-linear models suggested the influence of the presence of nitrogen atoms and the molecular volume distribution in the inhibitor structures on the HIV-1 protease inhibition as well as that the inhibition of HIV replication was dependent on the occurrence of five-member rings. Finally, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map built using the input variables of the best non-linear models.

A topological function based on spectral moments for predicting affinity toward A3 adenosine receptors

The spectral moment descriptors have been applied to the study of affinity for A(3) adenosine receptors of 32 adenosine analogues. A model, able to describe more than 95% of the variance in the experimental activity, was developed with the use of the above-mentioned approach. The fragment contributions to the activity carried out show that the sulfonamido moiety at the N(6) position and hydrogen bonding play an important role in the interaction with the receptor.

BCUT descriptors to predicting affinity toward A3 adenosine receptors

The BCUT descriptors have been applied to the study of the A(3) adenosine receptor agonist effect of 32 adenosine analogues. A model, able to describe more than 80% of the variance in the experimental activity was developed with the use of the above-mentioned approach. Four different approaches (topological, Galvez topological charges indexes, Randić molecular profiles, and geometrical descriptors) failed to give satisfactory models for this property with the same number of variables in the equation. Although statistically significant models were derived containing descriptors other than BCUT, the best fitted model was still found with these descriptors.

Quantitative structure–activity relationship to predict differential inhibition of aldose reductase by flavonoid compounds

Inhibitory activity against aldose reductase enzyme of flavonoid derivatives were modelled using 11 kinds of molecular descriptors from Dragon software. Model with four Galvez Charge Indices described 67% of data variance and overtaken other models using the same number of variables. Galvez indices showed to contain important information on the relationship between the inhibitor structures and its activity by describing the molecular topology and charge transfer through the molecule. In addition, artificial neural networks were trained using charge indices from the linear models but the obtaining networks overfitted the data having low predictive power.