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Open AccessArticle

Alert-QSAR. Implications for Electrophilic Theory of Chemical Carcinogenesis

by Mihai V. Putz, Cosmin Ionaşcu, Ana-Maria Putz and Vasile Ostafe

Int. J. Mol. Sci. 2011, 12(8), 5098-5134; https://doi.org/10.3390/ijms12085098 - 11 Aug 2011

Cited by 39 | Viewed by 10318

Abstract

Given the modeling and predictive abilities of quantitative structure activity relationships (QSARs) for genotoxic carcinogens or mutagens that directly affect DNA, the present research investigates structural alert (SA) intermediate-predicted correlations A^SA of electrophilic molecular structures with observed carcinogenic potencies in rats (observed [...] Read more.

Given the modeling and predictive abilities of quantitative structure activity relationships (QSARs) for genotoxic carcinogens or mutagens that directly affect DNA, the present research investigates structural alert (SA) intermediate-predicted correlations A^SA of electrophilic molecular structures with observed carcinogenic potencies in rats (observed activity, A = Log[1/TD₅₀], i.e., A^SA=f(X₁^SA,X₂^SA,...)). The present method includes calculation of the recently developed residual correlation of the structural alert models, i.e., ARA^SA=f(A-A^SA,X₁^SA,X₂^SA,...) . We propose a specific electrophilic ligand-receptor mechanism that combines electronegativity with chemical hardness-associated frontier principles, equality of ligand-reagent electronegativities and ligand maximum chemical hardness for highly diverse toxic molecules against specific receptors in rats. The observed carcinogenic activity is influenced by the induced SA-mutagenic intermediate effect, alongside Hansch indices such as hydrophobicity (LogP), polarizability (POL) and total energy (Etot), which account for molecular membrane diffusion, ionic deformation, and stericity, respectively. A possible QSAR mechanistic interpretation of mutagenicity as the first step in genotoxic carcinogenesis development is discussed using the structural alert chemoinformation and in full accordance with the Organization for Economic Co-operation and Development QSAR guidance principles. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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306 KiB

Open AccessArticle

Predictivity Approach for Quantitative Structure-Property Models. Application for Blood-Brain Barrier Permeation of Diverse Drug-Like Compounds

by Sorana D. Bolboacă and Lorentz Jäntschi

Int. J. Mol. Sci. 2011, 12(7), 4348-4364; https://doi.org/10.3390/ijms12074348 - 5 Jul 2011

Cited by 17 | Viewed by 7735

Abstract

The goal of the present research was to present a predictivity statistical approach applied on structure-based prediction models. The approach was applied to the domain of blood-brain barrier (BBB) permeation of diverse drug-like compounds. For this purpose, 15 statistical parameters and associated 95% [...] Read more.

The goal of the present research was to present a predictivity statistical approach applied on structure-based prediction models. The approach was applied to the domain of blood-brain barrier (BBB) permeation of diverse drug-like compounds. For this purpose, 15 statistical parameters and associated 95% confidence intervals computed on a 2 × 2 contingency table were defined as measures of predictivity for binary quantitative structure-property models. The predictivity approach was applied on a set of compounds comprised of 437 diverse molecules, 122 with measured BBB permeability and 315 classified as active or inactive. A training set of 81 compounds (~2/3 of 122 compounds assigned randomly) was used to identify the model and a test set of 41 compounds was used as the internal validation set. The molecular descriptor family on vertices cutting was the computation tool used to generate and calculate structural descriptors for all compounds. The identified model was assessed using the predictivity approach and compared to one model previously reported. The best-identified classification model proved to have an accuracy of 69% in the training set (95%CI [58.53–78.37]) and of 73% in the test set (95%CI [58.32–84.77]). The predictive accuracy obtained on the external set proved to be of 73% (95%CI [67.58–77.39]). The classification model proved to have better abilities in the classification of inactive compounds (specificity of ~74% [59.20–85.15]) compared to abilities in the classification of active compounds (sensitivity of ~64% [48.47–77.70]) in the training and external sets. The overall accuracy of the previously reported model seems not to be statistically significantly better compared to the identified model (~81% [71.45–87.80] in the training set, ~93% [78.12–98.17] in the test set and ~79% [70.19–86.58] in the external set). In conclusion, our predictivity approach allowed us to characterize the model obtained on the investigated set of compounds as well as compare it with a previously reported model. According to the obtained results, the reported model should be chosen if a correct classification of inactive compounds is desired and the previously reported model should be chosen if a correct classification of active compounds is most wanted. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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651 KiB

Open AccessArticle

Chemometric Analysis of the Amino Acid Requirements of Antioxidant Food Protein Hydrolysates

by Chibuike C. Udenigwe and Rotimi E. Aluko

Int. J. Mol. Sci. 2011, 12(5), 3148-3161; https://doi.org/10.3390/ijms12053148 - 13 May 2011

Cited by 270 | Viewed by 12903

Abstract

The contributions of individual amino acid residues or groups of amino acids to antioxidant activities of some food protein hydrolysates were investigated using partial least squares (PLS) regression method. PLS models were computed with amino acid composition and 3-z scale descriptors in the [...] Read more.

The contributions of individual amino acid residues or groups of amino acids to antioxidant activities of some food protein hydrolysates were investigated using partial least squares (PLS) regression method. PLS models were computed with amino acid composition and 3-z scale descriptors in the X-matrix and antioxidant activities of the samples in the Y-matrix; models were validated by cross-validation and permutation tests. Based on coefficients of the resulting models, it was observed that sulfur-containing (SCAA), acidic and hydrophobic amino acids had strong positive effects on scavenging of 2,2-diphenyl-1-picrylhydrazyl (DPPH) and H₂O₂ radicals in addition to ferric reducing antioxidant power. For superoxide radicals, only lysine and leucine showed strong positive contributions while SCAA had strong negative contributions to scavenging by the protein hydrolysates. In contrast, positively-charged amino acids strongly contributed negatively to ferric reducing antioxidant power and scavenging of DPPH and H₂O₂ radicals. Therefore, food protein hydrolysates containing appropriate amounts of amino acids with strong contribution properties could be potential candidates for use as potent antioxidant agents. We conclude that information presented in this work could support the development of low cost methods that will efficiently generate potent antioxidant peptide mixtures from food proteins without the need for costly peptide purification. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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Open AccessArticle

In Vitro Antioxidant Activity of Selected 4-Hydroxy-chromene-2-one Derivatives—SAR, QSAR and DFT Studies

by Milan Mladenović, Mirjana Mihailović, Desanka Bogojević, Sanja Matić, Neda Nićiforović, Vladimir Mihailović, Nenad Vuković, Slobodan Sukdolak and Slavica Solujić

Int. J. Mol. Sci. 2011, 12(5), 2822-2841; https://doi.org/10.3390/ijms12052822 - 29 Apr 2011

Cited by 98 | Viewed by 11013

Abstract

The series of fifteen synthesized 4-hydroxycoumarin derivatives was subjected to antioxidant activity evaluation in vitro, through total antioxidant capacity, 1,1-diphenyl-2-picryl-hydrazyl (DPPH), hydroxyl radical, lipid peroxide scavenging and chelating activity. The highest activity was detected during the radicals scavenging, with 2b, 6b [...] Read more.

The series of fifteen synthesized 4-hydroxycoumarin derivatives was subjected to antioxidant activity evaluation in vitro, through total antioxidant capacity, 1,1-diphenyl-2-picryl-hydrazyl (DPPH), hydroxyl radical, lipid peroxide scavenging and chelating activity. The highest activity was detected during the radicals scavenging, with 2b, 6b, 2c, and 4c noticed as the most active. The antioxidant activity was further quantified by the quantitative structure-activity relationships (QSAR) studies. For this purpose, the structures were optimized using Paramethric Method 6 (PM6) semi-empirical and Density Functional Theory (DFT) B3LYP methods. Bond dissociation enthalpies of coumarin 4-OH, Natural Bond Orbital (NBO) gained hybridization of the oxygen, acidity of the hydrogen atom and various molecular descriptors obtained, were correlated with biological activity, after which we designed 20 new antioxidant structures, using the most favorable structural motifs, with much improved predicted activity in vitro. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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Open AccessArticle

3D-QSAR and Molecular Docking Studies on Derivatives of MK-0457, GSK1070916 and SNS-314 as Inhibitors against Aurora B Kinase

by Baidong Zhang, Yan Li, Huixiao Zhang and Chunzhi Ai

Int. J. Mol. Sci. 2010, 11(11), 4326-4347; https://doi.org/10.3390/ijms11114326 - 2 Nov 2010

Cited by 19 | Viewed by 9968

Abstract

Development of anticancer drugs targeting Aurora B, an important member of the serine/threonine kinases family, has been extensively focused on in recent years. In this work, by applying an integrated computational method, including comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis [...] Read more.

Development of anticancer drugs targeting Aurora B, an important member of the serine/threonine kinases family, has been extensively focused on in recent years. In this work, by applying an integrated computational method, including comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), homology modeling and molecular docking, we investigated the structural determinants of Aurora B inhibitors based on three different series of derivatives of 108 molecules. The resultant optimum 3D-QSAR models exhibited (q² = 0.605, r²_pred = 0.826), (q² = 0.52, r²_pred = 0.798) and (q² = 0.582, r²_pred = 0.971) for MK-0457, GSK1070916 and SNS-314 classes, respectively, and the 3D contour maps generated from these models were analyzed individually. The contour map analysis for the MK-0457 model revealed the relative importance of steric and electrostatic effects for Aurora B inhibition, whereas, the electronegative groups with hydrogen bond donating capacity showed a great impact on the inhibitory activity for the derivatives of GSK1070916. Additionally, the predictive model of the SNS-314 class revealed the great importance of hydrophobic favorable contour, since hydrophobic favorable substituents added to this region bind to a deep and narrow hydrophobic pocket composed of residues that are hydrophobic in nature and thus enhanced the inhibitory activity. Moreover, based on the docking study, a further comparison of the binding modes was accomplished to identify a set of critical residues that play a key role in stabilizing the drug-target interactions. Overall, the high level of consistency between the 3D contour maps and the topographical features of binding sites led to our identification of several key structural requirements for more potency inhibitors. Taken together, the results will serve as a basis for future drug development of inhibitors against Aurora B kinase for various tumors. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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612 KiB

Open AccessArticle

Molecular Modeling Studies of 4,5-Dihydro-1H-pyrazolo[4,3-h] quinazoline Derivatives as Potent CDK2/Cyclin A Inhibitors Using 3D-QSAR and Docking

by Yong Ai, Shao-Teng Wang, Ping-Hua Sun and Fa-Jun Song

Int. J. Mol. Sci. 2010, 11(10), 3705-3724; https://doi.org/10.3390/ijms11103705 - 28 Sep 2010

Cited by 27 | Viewed by 8191

Abstract

CDK2/cyclin A has appeared as an attractive drug targets over the years with diverse therapeutic potentials. A computational strategy based on comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) followed by molecular docking studies were performed on a series [...] Read more.

CDK2/cyclin A has appeared as an attractive drug targets over the years with diverse therapeutic potentials. A computational strategy based on comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) followed by molecular docking studies were performed on a series of 4,5-dihydro-1H-pyrazolo[4,3-h]quinazoline derivatives as potent CDK2/cyclin A inhibitors. The CoMFA and CoMSIA models, using 38 molecules in the training set, gave r²_cv values of 0.747 and 0.518 and r² values of 0.970 and 0.934, respectively. 3D contour maps generated by the CoMFA and CoMSIA models were used to identify the key structural requirements responsible for the biological activity. Molecular docking was applied to explore the binding mode between the ligands and the receptor. The information obtained from molecular modeling studies may be helpful to design novel inhibitors of CDK2/cyclin A with desired activity. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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Open AccessArticle

In Silico Prediction of Estrogen Receptor Subtype Binding Affinity and Selectivity Using Statistical Methods and Molecular Docking with 2-Arylnaphthalenes and 2-Arylquinolines

by Zhizhong Wang, Yan Li, Chunzhi Ai and Yonghua Wang

Int. J. Mol. Sci. 2010, 11(9), 3434-3458; https://doi.org/10.3390/ijms11093434 - 20 Sep 2010

Cited by 36 | Viewed by 10191

Abstract

Over the years development of selective estrogen receptor (ER) ligands has been of great concern to researchers involved in the chemistry and pharmacology of anticancer drugs, resulting in numerous synthesized selective ER subtype inhibitors. In this work, a data set of 82 ER [...] Read more.

Over the years development of selective estrogen receptor (ER) ligands has been of great concern to researchers involved in the chemistry and pharmacology of anticancer drugs, resulting in numerous synthesized selective ER subtype inhibitors. In this work, a data set of 82 ER ligands with ERα and ERβ inhibitory activities was built, and quantitative structure-activity relationship (QSAR) methods based on the two linear (multiple linear regression, MLR, partial least squares regression, PLSR) and a nonlinear statistical method (Bayesian regularized neural network, BRNN) were applied to investigate the potential relationship of molecular structural features related to the activity and selectivity of these ligands. For ERα and ERβ, the performances of the MLR and PLSR models are superior to the BRNN model, giving more reasonable statistical properties (ERα: for MLR, R_tr² = 0.72, Q_te² = 0.63; for PLSR, R_tr² = 0.92, Q_te² = 0.84. ERβ: for MLR, R_tr² = 0.75, Q_te² = 0.75; for PLSR, R_tr² = 0.98, Q_te² = 0.80). The MLR method is also more powerful than other two methods for generating the subtype selectivity models, resulting in R_tr² = 0.74 and Q_te² = 0.80. In addition, the molecular docking method was also used to explore the possible binding modes of the ligands and a relationship between the 3D-binding modes and the 2D-molecular structural features of ligands was further explored. The results show that the binding affinity strength for both ERα and ERβ is more correlated with the atom fragment type, polarity, electronegativites and hydrophobicity. The substitutent in position 8 of the naphthalene or the quinoline plane and the space orientation of these two planes contribute the most to the subtype selectivity on the basis of similar hydrogen bond interactions between binding ligands and both ER subtypes. The QSAR models built together with the docking procedure should be of great advantage for screening and designing ER ligands with improved affinity and subtype selectivity property. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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Open AccessArticle

3D-QSAR and Molecular Docking Studies on Fused Pyrazoles as p38α Mitogen-Activated Protein Kinase Inhibitors

by Ping Lan, Zhi-Jian Huang, Jun-Rong Sun and Wei-Min Chen

Int. J. Mol. Sci. 2010, 11(9), 3357-3374; https://doi.org/10.3390/ijms11093357 - 17 Sep 2010

Cited by 30 | Viewed by 10903

Abstract

The p38α mitogen-activated protein kinase (MAPK) has become an attractive target for the treatment of many diseases such as rheumatoid arthritis, inflammatory bowel disease and Crohn’s disease. In this paper, 3D-QSAR and molecular docking studies were performed on 59 p38α MAPK inhibitors. Comparative [...] Read more.

The p38α mitogen-activated protein kinase (MAPK) has become an attractive target for the treatment of many diseases such as rheumatoid arthritis, inflammatory bowel disease and Crohn’s disease. In this paper, 3D-QSAR and molecular docking studies were performed on 59 p38α MAPK inhibitors. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were applied to determine the structural requirements for potency in inhibiting p38α MAPK. The resulting model of CoMFA and CoMSIA exhibited good r²_cv values of 0.725 and 0.609, and r² values of 0.961 and 0.905, respectively. Molecular docking was used to explore the binding mode between the inhibitors and p38α MAPK. We have accordingly designed a series of novel p38α MAPK inhibitors by utilizing the structure-activity relationship (SAR) results revealed in the present study, which were predicted with excellent potencies in the developed models. The results provided a useful guide to design new compounds for p38α MAPK inhibitors. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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610 KiB

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2D Quantitative Structure-Property Relationship Study of Mycotoxins by Multiple Linear Regression and Support Vector Machine

by Roya Khosrokhavar, Jahan Bakhsh Ghasemi and Fereshteh Shiri

Int. J. Mol. Sci. 2010, 11(9), 3052-3068; https://doi.org/10.3390/ijms11093052 - 31 Aug 2010

Cited by 21 | Viewed by 9007

Abstract

In the present work, support vector machines (SVMs) and multiple linear regression (MLR) techniques were used for quantitative structure–property relationship (QSPR) studies of retention time (t_R) in standardized liquid chromatography–UV–mass spectrometry of 67 mycotoxins (aflatoxins, trichothecenes, roquefortines and ochratoxins) based on [...] Read more.

In the present work, support vector machines (SVMs) and multiple linear regression (MLR) techniques were used for quantitative structure–property relationship (QSPR) studies of retention time (t_R) in standardized liquid chromatography–UV–mass spectrometry of 67 mycotoxins (aflatoxins, trichothecenes, roquefortines and ochratoxins) based on molecular descriptors calculated from the optimized 3D structures. By applying missing value, zero and multicollinearity tests with a cutoff value of 0.95, and genetic algorithm method of variable selection, the most relevant descriptors were selected to build QSPR models. MLRand SVMs methods were employed to build QSPR models. The robustness of the QSPR models was characterized by the statistical validation and applicability domain (AD). The prediction results from the MLR and SVM models are in good agreement with the experimental values. The correlation and predictability measure by r² and q² are 0.931 and 0.932, repectively, for SVM and 0.923 and 0.915, respectively, for MLR. The applicability domain of the model was investigated using William’s plot. The effects of different descriptors on the retention times are described. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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456 KiB

Open AccessArticle

QSAR Studies on Andrographolide Derivatives as α-Glucosidase Inhibitors

by Jun Xu, Sichao Huang, Haibin Luo, Guoji Li, Jiaolin Bao, Shaohui Cai and Yuqiang Wang

Int. J. Mol. Sci. 2010, 11(3), 880-895; https://doi.org/10.3390/ijms11030880 - 2 Mar 2010

Cited by 21 | Viewed by 12805

Abstract

Andrographolide derivatives were shown to inhibit α-glucosidase. To investigate the relationship between activities and structures of andrographolide derivatives, a training set was chosen from 25 andrographolide derivatives by the principal component analysis (PCA) method, and a quantitative structure-activity relationship (QSAR) was established by [...] Read more.

Andrographolide derivatives were shown to inhibit α-glucosidase. To investigate the relationship between activities and structures of andrographolide derivatives, a training set was chosen from 25 andrographolide derivatives by the principal component analysis (PCA) method, and a quantitative structure-activity relationship (QSAR) was established by 2D and 3D QSAR methods. The cross-validation r² (0.731) and standard error (0.225) illustrated that the 2D-QSAR model was able to identify the important molecular fragments and the cross-validation r² (0.794) and standard error (0.127) demonstrated that the 3D-QSAR model was capable of exploring the spatial distribution of important fragments. The obtained results suggested that proposed combination of 2D and 3D QSAR models could be useful in predicting the α-glucosidase inhibiting activity of andrographolide derivatives. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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223 KiB

Open AccessArticle

Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

by Hua Yuan, Jianping Huang and Chenzhong Cao

Int. J. Mol. Sci. 2009, 10(7), 3237-3254; https://doi.org/10.3390/ijms10073237 - 17 Jul 2009

Cited by 19 | Viewed by 11117

Abstract

Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT) and murine local lymph [...] Read more.

Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT) and murine local lymph node assay (LLNA) are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs) are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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Open AccessArticle

Additive SMILES-Based Carcinogenicity Models: Probabilistic Principles in the Search for Robust Predictions

by Andrey A. Toropov, Alla P. Toropova and Emilio Benfenati

Int. J. Mol. Sci. 2009, 10(7), 3106-3127; https://doi.org/10.3390/ijms10073106 - 8 Jul 2009

Cited by 42 | Viewed by 15538

Abstract

Optimal descriptors calculated with the simplified molecular input line entry system (SMILES) have been utilized in modeling of carcinogenicity as continuous values (logTD₅₀). These descriptors can be calculated using correlation weights of SMILES attributes calculated by the Monte Carlo method. A [...] Read more.

Optimal descriptors calculated with the simplified molecular input line entry system (SMILES) have been utilized in modeling of carcinogenicity as continuous values (logTD₅₀). These descriptors can be calculated using correlation weights of SMILES attributes calculated by the Monte Carlo method. A considerable subset of these attributes includes rare attributes. The use of these rare attributes can lead to overtraining. One can avoid the influence of the rare attributes if their correlation weights are fixed to zero. A function, limS, has been defined to identify rare attributes. The limS defines the minimum number of occurrences in the set of structures of the training (subtraining) set, to accept attributes as usable. If an attribute is present less than limS, it is considered “rare”, and thus not used. Two systems of building up models were examined: 1. classic training-test system; 2. balance of correlations for the subtraining and calibration sets (together, they are the original training set: the function of the calibration set is imitation of a preliminary test set). Three random splits into subtraining, calibration, and test sets were analysed. Comparison of abovementioned systems has shown that balance of correlations gives more robust prediction of the carcinogenicity for all three splits (split 1: r_test²=0.7514, s_test=0.684; split 2: r_test²=0.7998, s_test=0.600; split 3: r_test²=0.7192, s_test=0.728). Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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237 KiB

Open AccessArticle

Predicting Complexation Thermodynamic Parameters of β-Cyclodextrin with Chiral Guests by Using Swarm Intelligence and Support Vector Machines

by Chakguy Prakasvudhisarn, Peter Wolschann and Luckhana Lawtrakul

Int. J. Mol. Sci. 2009, 10(5), 2107-2121; https://doi.org/10.3390/ijms10052107 - 14 May 2009

Cited by 13 | Viewed by 12118

Abstract

The Particle Swarm Optimization (PSO) and Support Vector Machines (SVMs) approaches are used for predicting the thermodynamic parameters for the 1:1 inclusion complexation of chiral guests with β-cyclodextrin. A PSO is adopted for descriptor selection in the quantitative structure-property relationships (QSPR) of a [...] Read more.

The Particle Swarm Optimization (PSO) and Support Vector Machines (SVMs) approaches are used for predicting the thermodynamic parameters for the 1:1 inclusion complexation of chiral guests with β-cyclodextrin. A PSO is adopted for descriptor selection in the quantitative structure-property relationships (QSPR) of a dataset of 74 chiral guests due to its simplicity, speed, and consistency. The modified PSO is then combined with SVMs for its good approximating properties, to generate a QSPR model with the selected features. Linear, polynomial, and Gaussian radial basis functions are used as kernels in SVMs. All models have demonstrated an impressive performance with R² higher than 0.8. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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QSAR Analysis of 2-Amino or 2-Methyl-1-Substituted Benzimidazoles Against Pseudomonas aeruginosa

by Sanja O. Podunavac-Kuzmanović, Dragoljub D. Cvetković and Dijana J. Barna

Int. J. Mol. Sci. 2009, 10(4), 1670-1682; https://doi.org/10.3390/ijms10041670 - 17 Apr 2009

Cited by 46 | Viewed by 13504

Abstract

A set of benzimidazole derivatives were tested for their inhibitory activities against the Gram-negative bacterium Pseudomonas aeruginosa and minimum inhibitory concentrations were determined for all the compounds. Quantitative structure activity relationship (QSAR) analysis was applied to fourteen of the abovementioned derivatives using a [...] Read more.

A set of benzimidazole derivatives were tested for their inhibitory activities against the Gram-negative bacterium Pseudomonas aeruginosa and minimum inhibitory concentrations were determined for all the compounds. Quantitative structure activity relationship (QSAR) analysis was applied to fourteen of the abovementioned derivatives using a combination of various physicochemical, steric, electronic, and structural molecular descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the antibacterial activity of the benzimidazole derivatives. The stepwise regression method was used to derive the most significant models as a calibration model for predicting the inhibitory activity of this class of molecules. The best QSAR models were further validated by a leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. To confirm the predictive power of the models, an external set of molecules was used. High agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the derived QSAR models. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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295 KiB

Open AccessArticle

Quantum-SAR Extension of the Spectral-SAR Algorithm. Application to Polyphenolic Anticancer Bioactivity

by Mihai V. Putz, Ana-Maria Putz, Marius Lazea, Luciana Ienciu and Adrian Chiriac

Int. J. Mol. Sci. 2009, 10(3), 1193-1214; https://doi.org/10.3390/ijms10031193 - 16 Mar 2009

Cited by 40 | Viewed by 13914

Abstract

Aiming to assess the role of individual molecular structures in the molecular mechanism of ligand-receptor interaction correlation analysis, the recent Spectral-SAR approach is employed to introduce the Quantum-SAR (QuaSAR) “wave” and “conversion factor” in terms of difference between inter-endpoint inter-molecular activities for a [...] Read more.

Aiming to assess the role of individual molecular structures in the molecular mechanism of ligand-receptor interaction correlation analysis, the recent Spectral-SAR approach is employed to introduce the Quantum-SAR (QuaSAR) “wave” and “conversion factor” in terms of difference between inter-endpoint inter-molecular activities for a given set of compounds; this may account for inter-conversion (metabolization) of molecular (concentration) effects while indicating the structural (quantum) based influential/detrimental role on bio-/eco- effect in a causal manner rather than by simple inspection of measured values; the introduced QuaSAR method is then illustrated for a study of the activity of a series of flavonoids on breast cancer resistance protein. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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Open AccessArticle

Quantitative Prediction of Solvation Free Energy in Octanol of Organic Compounds

by Eduardo J. Delgado and Gonzalo A. Jaña

Int. J. Mol. Sci. 2009, 10(3), 1031-1044; https://doi.org/10.3390/i10031031 - 11 Mar 2009

Cited by 7 | Viewed by 11744

Abstract

The free energy of solvation, ΔG_S⁰ , in octanol of organic compunds is quantitatively predicted from the molecular structure. The model, involving only three molecular descriptors, is obtained by multiple linear regression analysis from a data set of 147 compounds [...] Read more.

The free energy of solvation, ΔG_S⁰ , in octanol of organic compunds is quantitatively predicted from the molecular structure. The model, involving only three molecular descriptors, is obtained by multiple linear regression analysis from a data set of 147 compounds containing diverse organic functions, namely, halogenated and non-halogenated alkanes, alkenes, alkynes, aromatics, alcohols, aldehydes, ketones, amines, ethers and esters; covering a ΔG_S⁰ range from about –50 to 0 kJ·mol-1. The model predicts the free energy of solvation with a squared correlation coefficient of 0.93 and a standard deviation, 2.4 kJ·mol-1, just marginally larger than the generally accepted value of experimental uncertainty. The involved molecular descriptors have definite physical meaning corresponding to the different intermolecular interactions occurring in the bulk liquid phase. The model is validated with an external set of 36 compounds not included in the training set. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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Review

Jump to: Research

403 KiB

Open AccessReview

Recent Advances in Fragment-Based QSAR and Multi-Dimensional QSAR Methods

by Kyaw Zeyar Myint and Xiang-Qun Xie

Int. J. Mol. Sci. 2010, 11(10), 3846-3866; https://doi.org/10.3390/ijms11103846 - 8 Oct 2010

Cited by 124 | Viewed by 14543

Abstract

This paper provides an overview of recently developed two dimensional (2D) fragment-based QSAR methods as well as other multi-dimensional approaches. In particular, we present recent fragment-based QSAR methods such as fragment-similarity-based QSAR (FS-QSAR), fragment-based QSAR (FB-QSAR), Hologram QSAR (HQSAR), and top priority fragment [...] Read more.

This paper provides an overview of recently developed two dimensional (2D) fragment-based QSAR methods as well as other multi-dimensional approaches. In particular, we present recent fragment-based QSAR methods such as fragment-similarity-based QSAR (FS-QSAR), fragment-based QSAR (FB-QSAR), Hologram QSAR (HQSAR), and top priority fragment QSAR in addition to 3D- and nD-QSAR methods such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA), Topomer CoMFA, self-organizing molecular field analysis (SOMFA), comparative molecular moment analysis (COMMA), autocorrelation of molecular surfaces properties (AMSP), weighted holistic invariant molecular (WHIM) descriptor-based QSAR (WHIM), grid-independent descriptors (GRIND)-based QSAR, 4D-QSAR, 5D-QSAR and 6D-QSAR methods. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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359 KiB

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A Review on Progress in QSPR Studies for Surfactants

by Jiwei Hu, Xiaoyi Zhang and Zhengwu Wang

Int. J. Mol. Sci. 2010, 11(3), 1020-1047; https://doi.org/10.3390/ijms11031020 - 8 Mar 2010

Cited by 80 | Viewed by 15870

Abstract

This paper presents a review on recent progress in quantitative structure-property relationship (QSPR) studies of surfactants and applications of various molecular descriptors. QSPR studies on critical micelle concentration (cmc) and surface tension (γ) of surfactants are introduced. Studies on charge distribution in ionic [...] Read more.

This paper presents a review on recent progress in quantitative structure-property relationship (QSPR) studies of surfactants and applications of various molecular descriptors. QSPR studies on critical micelle concentration (cmc) and surface tension (γ) of surfactants are introduced. Studies on charge distribution in ionic surfactants by quantum chemical calculations and its effects on the structures and properties of the colloids of surfactants are also reviewed. The trends of QSPR studies on cloud point (for nonionic surfactants), biodegradation potential and some other properties of surfactants are evaluated . Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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186 KiB

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QSPR Studies on Aqueous Solubilities of Drug-Like Compounds

by Pablo R. Duchowicz and Eduardo A. Castro

Int. J. Mol. Sci. 2009, 10(6), 2558-2577; https://doi.org/10.3390/ijms10062558 - 3 Jun 2009

Cited by 53 | Viewed by 14773

Abstract

A rapidly growing area of modern pharmaceutical research is the prediction of aqueous solubility of drug-sized compounds from their molecular structures. There exist many different reasons for considering this physico-chemical property as a key parameter: the design of novel entities with adequate aqueous [...] Read more.

A rapidly growing area of modern pharmaceutical research is the prediction of aqueous solubility of drug-sized compounds from their molecular structures. There exist many different reasons for considering this physico-chemical property as a key parameter: the design of novel entities with adequate aqueous solubility brings many advantages to preclinical and clinical research and development, allowing improvement of the Absorption, Distribution, Metabolization, and Elimination/Toxicity profile and “screenability” of drug candidates in High Throughput Screening techniques. This work compiles recent QSPR linear models established by our research group devoted to the quantification of aqueous solubilities and their comparison to previous research on the topic. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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Current Mathematical Methods Used in QSAR/QSPR Studies

by Peixun Liu and Wei Long

Int. J. Mol. Sci. 2009, 10(5), 1978-1998; https://doi.org/10.3390/ijms10051978 - 29 Apr 2009

Cited by 192 | Viewed by 16986

Abstract

This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit [...] Read more.

This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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The Interplay between QSAR/QSPR Studiesand Partial Order Ranking and Formal Concept Analyses

by Lars Carlsen

Int. J. Mol. Sci. 2009, 10(4), 1628-1657; https://doi.org/10.3390/ijms10041628 - 17 Apr 2009

Cited by 12 | Viewed by 15515

Abstract

The often observed scarcity of physical-chemical and well as toxicological data hampers the assessment of potentially hazardous chemicals released to the environment. In such cases Quantitative Structure-Activity Relationships/Quantitative Structure-Property Relationships (QSAR/QSPR) constitute an obvious alternative for rapidly, effectively and inexpensively generatng missing experimental [...] Read more.

The often observed scarcity of physical-chemical and well as toxicological data hampers the assessment of potentially hazardous chemicals released to the environment. In such cases Quantitative Structure-Activity Relationships/Quantitative Structure-Property Relationships (QSAR/QSPR) constitute an obvious alternative for rapidly, effectively and inexpensively generatng missing experimental values. However, typically further treatment of the data appears necessary, e.g., to elucidate the possible relations between the single compounds as well as implications and associations between the various parameters used for the combined characterization of the compounds under investigation. In the present paper the application of QSAR/QSPR in combination with Partial Order Ranking (POR) methodologies will be reviewed and new aspects using Formal Concept Analysis (FCA) will be introduced. Where POR constitutes an attractive method for, e.g., prioritizing a series of chemical substances based on a simultaneous inclusion of a range of parameters, FCA gives important information on the implications associations between the parameters. The combined approach thus constitutes an attractive method to a preliminary assessment of the impact on environmental and human health by primary pollutants or possibly by a primary pollutant well as a possible suite of transformation subsequent products that may be both persistent in and bioaccumulating and toxic.The present review focus on the environmental – and human health impact by residuals of the rocket fuel 1,1-dimethyl- hydrazine (heptyl) and its transformation products as an illustrative example. Full article

(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)

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