Significant Molecular Descriptors Research Articles

QSPR study on normal boiling point of acyclic oxygen containing organic compounds by radial basis function artificial neural network

Radial basis function artificial neural network (RBF-ANN) model was developed to predict the normal boiling point (NBP) of 240 acyclic oxygen containing organic compounds, including alcohols, ethers, aldehydes, ketones, carboxylic acids and esters. The total database was randomly divided into a training set (192), a validation set (24) and a test set (24). 8 significant molecular descriptors, which were used to build the RBF-ANN model, were selected from a pool of descriptors by multi-stepwise regression method. The RBF-ANN model was trained by the Orthogonal Least Squares (OLS) learning algorithm. The biharmonic response surface analysis was used for the optimization of two main tuning parameters in the neural network. The final optimum RBF-ANN model represented by [8-22(32)-1] was tested and evaluated by graphic and statistical methods. Y-randomization test and a random split cross validation confirmed the robustness of the RBF-ANN model. The comparison results with the best multiple linear regression function and literature QSPR models showed the superiority of the RBF-ANN model.

Classification models of structure - P-glycoprotein activity of drugs

Thirty three classification models of substrate specificity of 177 drugs to P-glycoprotein have been created using of the linear discriminant analysis, random forest and support vector machine methods. QSAR modeling was carried out using 2 strategies. The first strategy consisted in search of all possible combinations from 1÷5 descriptors on the basis of 7 most significant molecular descriptors with clear physico-chemical interpretation. In the second case forward selection procedure up to 5 descriptors, starting from the best single descriptor was used. This strategy was applied to a set of 387 DRAGON descriptors. It was found that only one of 33 models has necessary statistical parameters. This model was designed by means of the linear discriminant analysis on the basis of a single descriptor of H-bond (ΣC(ad)). The model has good statistical characteristics as evidenced by results to both internal cross-validation, and external validation with application of 44 new chemicals. This confirms an important role of hydrogen bond in the processes connected with penetration of chemical compounds through a blood-brain barrier.

A QSAR, pharmacokinetic and toxicological study of new artemisinin compounds with anticancer activity.

The Density Functional Theory (DFT) method and the 6-31G** basis set were employed to calculate the molecular properties of artemisinin and 20 derivatives with different degrees of cytotoxicity against the human hepatocellular carcinoma HepG2 line. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to select the most important descriptors related to anticancer activity. The significant molecular descriptors related to the compounds with anticancer activity were the ALOGPS_log, Mor29m, IC5 and GAP energy. The Pearson correlation between activity and most important descriptors were used for the regression partial least squares (PLS) and principal component regression (PCR) models built. The regression PLS and PCR were very close, with variation between PLS and PCR of R2 = ±0.0106, R2ajust = ±0.0125, s = ±0.0234, F(4,11) = ±12.7802, Q2 = ±0.0088, SEV = ±0.0132, PRESS = ±0.4808 and SPRESS = ±0.0057. These models were used to predict the anticancer activity of eight new artemisinin compounds (test set) with unknown activity, and for these new compounds were predicted pharmacokinetic properties: human intestinal absorption (HIA), cellular permeability (PCaCO2), cell permeability Maden Darby Canine Kidney (PMDCK), skin permeability (PSkin), plasma protein binding (PPB) and penetration of the blood-brain barrier (CBrain/Blood), and toxicological: mutagenicity and carcinogenicity. The test set showed for two new artemisinin compounds satisfactory results for anticancer activity and pharmacokinetic and toxicological properties. Consequently, further studies need be done to evaluate the different proposals as well as their actions, toxicity, and potential use for treatment of cancers.

Antimony(III) halide compounds of thioureas: Structures and biological activity

Antimony(III) halide compounds (SbX3, X=Cl and Br) of thioureas; tetramethylthiourea (TMTU), N,N′-diethylthiourea (DETU) and 1,3-diisopropyl-2-thiourea (DIPTU) of formulae {[SbCl3(TMTU)]n} (1), {[SbBr3(TMTU)]n} (2), {[mer-SbCl3(DIPTU)3] [fac-SbCl3(DIPTU)3] C6H6} (3) and {[SbBr2(DETU)2]+·Br−}n (4) were synthesized. The complexes were characterized by their melting point, elemental analysis, FT-IR spectroscopy, FT-Raman spectroscopy, 1H and 13C NMR spectroscopy and Thermal Gravimetry-Differential Thermal Analysis (TG-DTA). The crystal and molecular structures of complexes 2–4 were determined with single crystal X-ray diffraction analysis. Compounds 1 and 2 are polymers with pseudotrigonal–bipyramidal (ψ-TBP) geometry in each monomeric unit. Compound 3 is a monomer with octahedral (Oh) geometry around Sb(III). Two different coordination modes of the ligands around antimony ions are observed in 3 forming two units. One with meridional orientation and the other with facial one. This complex is the first example of polymeric antimony(III) where two sequential antimony atoms have two different coordination modes. Two sulfur atoms and two bromines form a pseudotrigonal–bipyramidal cationic [SbBr2(L)2]+ part with a bromide as counter anion in complex 4. Complexes 1–4 and their ligands were evaluated for their in vitro cytotoxic activity against human breast adenocarcinoma (MCF-7) and human cervical adenocarcinoma (HeLa) cells. Principal components analysis (PCA) was performed to discriminate the significant physicochemical molecular descriptors while regression analysis successfully relates the experimental inhibitory concentrations, (IC50) to the independent variables indexed by PCA.

Synthesis, structural characterization and cytostatic properties of N,N-dicyclohexyldithiooxamide complexes of antimony(III) halides (SbX3, X: Br or I)

The novel antimony(III) halide complexes with the bi-dentate substituted dithiooxamide ligand, N,N-dicyclohexyldithiooxamide (HDTOA) of formulae {[SbBr3(HDTOA)1.5]}n (1) and {[SbI3(HDTOA)1.5].C6H6}n (2), were prepared by reacting antimony(III) bromide or antimony(III) iodide with HDTOA in 1:1 molar ratio. The compounds were characterized by their m.p., FT-IR spectroscopy, Raman spectroscopy, 1H, 13C NMR spectroscopy, Thermal Gravimetric – Differential Thermal Analysis (TG-DTA) and single crystal X-ray diffraction analysis. Both complexes are polymeric consisting by [SbX3(HDTOA)1.5] building blocks. The octahedral geometric demand of the metal center is covered by three halides and three sulfur atoms from three different HDTOA ligands with facial conformation in both complexes. Each ligand bridged two metal centers of two units, through sulfur donor atoms, acting as bi-dentate bridging ligand. Complexes 1 and 2 were also tested for in vitro cytotoxic activity against human breast adenocarcinoma (MCF-7) and human cervical adenocarcinoma (HeLa) cells. Principal components analysis (PCA) was performed to discriminate the significant physicochemical molecular descriptors while regression analysis successfully related the experimental inhibitory concentrations, (IC50) to the independent variables indexed by PCA. The calculated IC50 values are satisfactorily compared with the measured inhibitory activity of the complexes.

A SAR and QSAR Study of New Artemisinin Compounds with Antimalarial Activity

The Hartree-Fock method and the 6-31G** basis set were employed to calculate the molecular properties of artemisinin and 20 derivatives with antimalarial activity. Maps of molecular electrostatic potential (MEPs) and molecular docking were used to investigate the interaction between ligands and the receptor (heme). Principal component analysis and hierarchical cluster analysis were employed to select the most important descriptors related to activity. The correlation between biological activity and molecular properties was obtained using the partial least squares and principal component regression methods. The regression PLS and PCR models built in this study were also used to predict the antimalarial activity of 30 new artemisinin compounds with unknown activity. The models obtained showed not only statistical significance but also predictive ability. The significant molecular descriptors related to the compounds with antimalarial activity were the hydration energy (HE), the charge on the O11 oxygen atom (QO11), the torsion angle O1-O2-Fe-N2 (D2) and the maximum rate of R/Sanderson Electronegativity (RTe+). These variables led to a physical and structural explanation of the molecular properties that should be selected for when designing new ligands to be used as antimalarial agents.

Synthesis, characterization and biological activity of antimony(III) or bismuth(III) chloride complexes with dithiocarbamate ligands derived from thiuram degradation

Antimony(III) or bismuth(III) complexes of formulae {[SbCl(Me2DTC)2]n} (1), {[BiCl(Me2DTC)2]n} (2) and {[Bi(Et2DTC)3]2} (3) (Me2DTCH=dimethyldithiocarbamate, C3H7NS2 and Et2DTCH=diethyldithiocarbamate, C5H11NS2) were isolated from the reactions between SbCl3 or BiCl3 with tetramethylthiuram monosulfide (Me4tms), tetramethylthiuram disulfide (Me4tds) or tetraethylthiuram disulfide (Et4tds). In the case of 1 two polymorphs were isolated depending on the synthetic procedure followed. Crystal growth from the reaction of antimony(III)–chloride with Me4tms in methanol produced 1a polymorph, while those derived from Me4tds in acetonitrile/dichloromethane produced 1b form. The complexes 1–3 were characterized by m.p., e.a., FT-IR, FT-Raman, 1H, 13C NMR spectroscopy and Thermal Gravimetry–Differential Thermal Analysis (TGA–DTA). Moreover, single crystal X-ray diffraction analysis was carried out for 1a, 1b, 2 and 3. X-ray powder diffraction data confirm the existence of one polymorph in the bulk of each sample of 1a and 1b. 1H NMR spectra in the DMSO-d6 solutions of 1a and 1b suggest the retention of the structural variations. Complexes 1 and 2 are polymers with distorted square pyramidal (SPY) geometry in each monomeric unit. The known structure of 3 was re-determined to be used for the theoretical and structure activity relationship studies (SAR).Complexes 1–3 were evaluated for their in vitro cytotoxic activity against human breast adenocarcinoma (MCF-7) and human cervix adenocarcinoma (HeLa) cells. Complex 3 is more active against HeLa cells whereas 1a, 1b and 2 against MCF-7. Compound 1a shows slightly higher activity than 1b. Principal components analysis (PCA) was performed to discriminate the significant physicochemical molecular descriptors while regression analysis successfully related the experimental inhibitory concentration, (IC50) to the independent variables indexed by PCA. The calculated IC50 values are satisfactorily compared with the measured inhibitory activity of the complexes.

Phenylpropiophenone derivatives as potential anticancer agents: Synthesis, biological evaluation and quantitative structure–activity relationship study

Series of twelve chalcone and propafenone derivatives has been synthesized and evaluated for anticancer activities against HeLa, Fem-X, PC-3, MCF-7, LS174 and K562 cell lines. The 2D-QSAR and 3D-QSAR studies were performed for all compounds with cytotoxic activities against each cancer cell line. Partial least squares (PLS) regression has been applied for selection of the most relevant molecular descriptors and QSAR models building. Predictive potentials of the created 2D-QSAR and 3D-QSAR models for each cell line were compared, by use of leave-one-out cross-validation and external validation, and optimal QSAR models for each cancer cell line were selected. The QSAR studies have selected the most significant molecular descriptors and pharmacophores of the chalcone and propafenone derivatives and proposed structures of novel chalcone and propafenone derivatives with enhanced anticancer activity on the HeLa, Fem-X, PC-3, MCF-7, LS174 and K562 cells.

Antimicrobial Activity and SAR Study of Some Novel Thiosemicarbazide Derivatives Bearing Piperidine Moiety

In this study novel thiosemicarbazides bearing piperidine moiety were synthesized in order to investigate their possible antibacterial and antifungal activities. A structure-activity relationship (SAR) study including conformational analysis and some physicochemical descriptors was carried out to provide the guidance for further synthetic work. The significant molecular descriptors related to the compounds with antifungal activity were: electrostatic potential surface, the highest occupied molecular orbital energy, surface area, volume, and hydration energy.

Rough Sets for Selection of Molecular Descriptors to Predict Biological Activity of Molecules

Quantitative structure activity relationship (QSAR) is one of the important disciplines of computer-aided drug design that deals with the predictive modeling of properties of a molecule. In general, each QSAR dataset is small in size with large number of features or descriptors. Among the large amount of descriptors presented in the QSAR dataset, only a small fraction of them is effective for performing the predictive modeling task. In this paper, a new feature selection algorithm is presented, based on rough set theory, to select a set of effective molecular descriptors from a given QSAR dataset. The proposed algorithm selects the set of molecular descriptors by maximizing both relevance and significance of the descriptors. An important finding is that the proposed feature selection algorithm is shown to be effective in selecting relevant and significant molecular descriptors from the QSAR dataset for predictive modeling. The performance of the proposed algorithm is studied using R2 statistic of support vector regression method. The effectiveness of the proposed algorithm, along with a comparison with existing algorithms, is demonstrated on three QSAR datasets.

QSAR Studies on Aminothiazole Derivatives as Aurora A Kinase Inhibitors

Quantitative structure-activity relationship studies on 54 aminothiazole derivatives as Aurora A kinase inhibitors were performed to explore the important factors affecting their biologic activity. For 2D-quantitative structure-activity relationship study, genetic algorithm combined with multiple linear regression was used to select significant molecular descriptors. The MLR model gave squared correlation coefficient of 0.828 and squared cross-validated correlation coefficient of 0.771 for the training set compounds. Comparative molecular field analysis and comparative molecular similarity indices analysis were used to develop 3D-quantitative structure-activity relationship models. The comparative molecular field analysis model gave cross-validated correlation coefficient q² of 0.695 and non-cross-validated correlation coefficient r² of 0.977. For comparative molecular similarity indices analysis model, the corresponding q² and r² were 0.698 and 0.960, respectively. The proposed 3D-quantitative structure-activity relationship models were validated by the test set compounds not used in the modeling process, with r²(pred) values of 0.788 for comparative molecular field analysis and 0.798 for comparative molecular similarity indices analysis. The 3D contour maps suggested that further modification of the aniline group of compound 22 considering electrostatic, hydrophobic and hydrogen bond properties would influence the inhibitory activity. The results from quantitative structure-activity relationship models would be very useful to understand the structure-activity relationship of these inhibitors and to guide the further structural modification of new potential inhibitors.

Validated models for predicting skin penetration from different vehicles

The permeability of a penetrant though skin is controlled by the properties of the penetrants and the mixture components, which in turn relates to the molecular structures. Despite the well-investigated models for compound permeation through skin, the effect of vehicles and mixture components has not received much attention. The aim of this Quantitative Structure Activity Relationship (QSAR) study was to develop a statistically validated model for the prediction of skin permeability coefficients of compounds dissolved in different vehicles. Furthermore, the model can help with the elucidation of the mechanisms involved in the permeation process. With this goal in mind, the skin permeability of four different penetrants each blended in 24 different solvent mixtures were determined from diffusion cell studies using porcine skin. The resulting 96 kp values were combined with a previous dataset of 288 kp data for QSAR analysis. Stepwise regression analysis was used for the selection of the most significant molecular descriptors and development of several regression models. The selected QSAR employed two penetrant descriptors of Wiener topological index and total lipole moment, boiling point of the solvent and the difference between the melting point of the penetrant and the melting point of the solvent. The QSAR was validated internally, using a leave-many-out procedure, giving a mean absolute error of 0.454 for the log kp value of the test set.

Prediction of infinite-dilution activity coefficients of organic solutes in ionic liquids using temperature-dependent quantitative structure–property relationship method

Ionic liquids (ILs) are a type of potential green solvents, which can be used as a media for reaction and separation. The infinite-dilution activity coefficient is an important parameter to measure the interaction between ILs and solutes. In this work, we proposed a new method to predict infinite-dilution activity coefficients of ILs at different temperatures. A temperature-dependent quantitative structure–property relationship (QSPR) model was developed for a series of organic solutes in the ionic liquid trihexyl(tetradecyl)phosphonium bis(trifluoromethylsulfonyl)imide. By using genetic algorithm-variables subset selection (GA-VSS) and ordinary least-square regression (OLS) methods, six variables, including temperature and five significant molecular descriptors, were selected and used to build the temperature-dependent prediction model. The satisfactory results of the internal and external validations proved the reliability, stability and predictive ability of the built model.

Modelling of Cytotoxicity Data (CC50) of Anti‐HIV 1‐[5‐Chlorophenyl) Sulfonyl]‐1H‐Pyrrole Derivatives Using Calculated Molecular Descriptors and Levenberg–Marquardt Artificial Neural Network

A nonlinear quantitative structure anti-HIV activity relationship study was presented for modelling and predicting pyrryl aryl sulfones cytotoxicity data. Levenberg-Marquardt artificial neural network was used to link molecular structures and cytotoxicity data. A data set consisting of 27 derivatives of 1-[5-chlorophenyl) sulfonyl]-1H-pyrrole was used in this study. Among a large number of calculated descriptors, only eight significant molecular descriptors were obtained by stepwise regression, as the most feasible descriptors, and then they were used as inputs for neural network. The data set was randomly divided into 20 training and 7 validation sets and the neural network architecture and its parameters were optimized. The prediction ability of the model was evaluated using the validation data set, leave-one-out cross-validation and response randomization method. The mean square errors and mean absolute errors for the validation data set were 0.0067 and 0.066, respectively, and for the leave-one-out method, they were 0.013 and 0.087, respectively. The results obtained showed the excellent prediction ability and stability of the proposed model in the prediction of cytotoxicity data of the corresponding anti-HIV analogues.

Prediction of hERG Potassium Channel Blockade Using kNN‐QSAR and Local Lazy Regression Methods

AbstractWe have collated hERG inhibition data of 165 compounds from literature and employed two regression procedures, namely, Local Lazy Regression (LLR) and k‐Nearest Neighbor (kNN)‐QSAR regression methods in combination with Genetic Algorithms (GAs) to select significant and independent molecular descriptors and to build robust predictive models. This methodology helped us to derive four, optimal 2D‐ and 3D‐QSPR models, M1–M4, based on five descriptors. Extensive validation tests using leave‐one‐out method and 61 compounds that are not used in the model generation strongly suggest that: (i) models M1 and M2, based on LLR, are very stable and robust; (ii) the model, M2 based on 3‐D descriptors, performs better than the one based on 2‐D descriptors, M1; and (iii) LLR method outperforms kNN regression approach. These results strongly suggest that the combination of GA and LLR method is a promising methodology, to build multiple stable models that are useful in consensus prediction. Further, from the analysis of the physical meaning of the descriptors, used in the best 2‐D and 3‐D descriptor models, M1 and M2, the significant physico‐chemical forces that determine the hERG inhibition profile of small organic compounds are uncovered. Finally, as the models reported herein, are based on computed properties, they appear a valuable tool in virtual screening, where selection and prioritization of candidates is required.

Classification of drug molecules considering their IC50 values using mixed-integer linear programming based hyper-boxes method

BackgroundA priori analysis of the activity of drugs on the target protein by computational approaches can be useful in narrowing down drug candidates for further experimental tests. Currently, there are a large number of computational methods that predict the activity of drugs on proteins. In this study, we approach the activity prediction problem as a classification problem and, we aim to improve the classification accuracy by introducing an algorithm that combines partial least squares regression with mixed-integer programming based hyper-boxes classification method, where drug molecules are classified as low active or high active regarding their binding activity (IC50 values) on target proteins. We also aim to determine the most significant molecular descriptors for the drug molecules.ResultsWe first apply our approach by analyzing the activities of widely known inhibitor datasets including Acetylcholinesterase (ACHE), Benzodiazepine Receptor (BZR), Dihydrofolate Reductase (DHFR), Cyclooxygenase-2 (COX-2) with known IC50 values. The results at this stage proved that our approach consistently gives better classification accuracies compared to 63 other reported classification methods such as SVM, Naïve Bayes, where we were able to predict the experimentally determined IC50 values with a worst case accuracy of 96%. To further test applicability of this approach we first created dataset for Cytochrome P450 C17 inhibitors and then predicted their activities with 100% accuracy.ConclusionOur results indicate that this approach can be utilized to predict the inhibitory effects of inhibitors based on their molecular descriptors. This approach will not only enhance drug discovery process, but also save time and resources committed.

Development of a Phospholipidosis Database and Predictive Quantitative Structure-Activity Relationship (QSAR) Models

ABSTRACTDrug-induced phospholipidosis (PL) is a condition characterized by the accumulation of phospholipids and drug in lysosomes, and is found in a variety of tissue types. PL is frequently manifested in preclinical studies and may delay or prevent the development of pharmaceuticals. This report describes the construction of a database of PL findings in a variety of animal species and its use as a training data set for computational toxicology software. PL data and chemical structures were compiled from the published literature, existing pharmaceutical databases, and Food and Drug Administration (FDA) internal reports yielding a total of 583 compounds suitable for modeling. The database contained 190 (33%) positive drugs and 393 (77%) negative drugs, of which 39 were electron microscopy–confirmed negative compounds and 354 were classified as negatives due to the absence of positive reported data. Of the 190 positive findings, 76 were electron microscopy confirmed and 114 were considered positive based on other evidence. Quantitative structure-activity relationship (QSAR) models were constructed using two commercially available software programs, MC4PC and MDL-QSAR, and internal cross-validation (10 × 10%) experiments were performed to assess their predictive performance. Performance parameters for the MC4PC model were specificity 92%, sensitivity 50%, concordance 78%, positive predictivity 76%, and negative predictivity 78%. For MDL-QSAR, predictive performance was similar: specificity 80%, sensitivity 76%, concordance 79%, positive predictivity 65%, and negative predictivity 87%. By combining the output of the two QSAR programs, the overall predictive performance was vastly improved and sensitivity could be optimized to 81% without significant loss of specificity (79%). Many of the structural alerts and significant molecular descriptors obtained from the QSAR software were found to be associated with parts of active molecules known for their cationic amphiphilic drug (CAD) properties supporting the hypothesis that the endpoint of PL is statistically correlated with chemical structure. QSAR models can be useful tools for screening drug candidate molecules for potential PL.

A theoretical study of phenolic compounds with antioxidant properties

Quantum chemical calculations at the DFT/B3LYP, HF, and AM1 and PM3 semiempirical levels were employed to calculate a set of molecular properties for 41 phenol compounds with antioxidant activity. The significant molecular descriptors related to the compounds were the vertical ionization potentials (IPvs) and the charge on oxygen atom 7. The IPv has been calculated using Koopman's theorem IPv = − ɛ HOMO(DFT), IPv = − ɛ HOMO(HF) and as the difference of energy calculations for the corresponding cation and for the neutral form IPv( E cation − E neutral) obtained with the DFT/B3LYP method. The best model obtained showed not only the statistical significance but also predictive ability.

Quantitative structure–property relationship studies for estimating boiling points of alcohols using calculated molecular descriptors with radial basis function neural networks

Radial basis function (RBF) neural networks were used to link molecular structures and boiling points. The data sets were composed of 106 alcohol compounds, with experimental boiling points values ranging from 64.60 to 245.00 °C, and the number of carbon atoms ranging from 1 to 11. Each compound was characterized by a set of 70 molecular structure descriptors calculated by semi-empirical quantum chemical AM1 and topological method. In the process of model optimization, first, 10 significant molecular descriptors were obtained from the pool of descriptors by objective methods (identity test and pairwise correlation test) and followed by subjective feature reduction method (descriptors can be entered or removed from the model depending on the probability of the F-value). After that, the RBF neural networks were trained to model the structure and property by the orthogonal least squares (OLS) training algorithm. The total database was randomly divided into a training set (75), a validation set (15) and a testing set (15). Simulated with the final optimum RBF neural networks [10- 20 [16] -1], the absolute average residues for the training, the validation, and the testing set were 1.87, 1.74 and 2.86 °C, and mean squared error (MSE) were 5.14, 3.80 and 14.16, and the predictive correlation coefficients R=0.998 (training), 0.998 (validation) and 0.991 (testing). Also, the results based on the best multilinear regression model were presented to evaluate the possible improvement in RBF neural networks. Results show that RBF neural networks can give more satisfactory prediction ability than linear model.

A QSAR study of 8.O.4′-neolignans with antifungal activity

The PM3 semi-empirical method was employed to calculate a set of molecular properties (variables or descriptors) of 18 neolignans compounds with activities against Epidermophyton floccosum a most susceptible species of dermophytes. The correlation between biological activity and structural properties was obtained by using the multiple linear regression method. The model obtained showed not only statistical significance but also predictive ability. The significant molecular descriptors related to the compounds with antifungal activity were: hydration energy and the charge on C1′ carbon atom ( Q 1′). The model obtained was applied to a set of 10 new derived from neolignans, where five of them presented promising biological activities against E. floccosum.