QSAR Models (qsar + models)

Distribution by Scientific Domains

Selected Abstracts

QSAR Models for the Dermal Penetration of Polycyclic Aromatic Hydrocarbons Based on Gene Expression Programming

Tao Wang
Abstract Gene Expression Programming (GEP) is a novel machine learning technique. The GEP is used to build nonlinear quantitative structure activity relationship model for the prediction of the Percent of Applied Dose Dermally Absorbed (PADA) over 24,h for polycyclic aromatic hydrocarbons. This model is based on descriptors which are calculated from the molecular structure. Three descriptors are selected from the descriptors pool by Heuristic Method (HM) to build a multivariable linear model. The GEP method produced a nonlinear quantitative model with a correlation coefficient and a mean error of 0.92 and 4.70 for the training set, 0.91 and 7.65 for the test set, respectively. It is shown that the GEP predicted results are in good agreement with experimental ones. [source]

2D- and 3D-QSAR Models of Interaction between Flavor Compounds and beta-Lactoglobulin Using Catalyst and Cerius2

Anne Tromelin
Abstract The present paper describes an application of Catalyst to three aroma sets (35, 24 and 21 compounds respectively) to generate activities-based alignments, using the best significant generated hypotheses. The obtained Catalyst models confirmed the existence of at least two binding sites on the BLG. [source]

Structure,activity relationships for the mutagenicity and carcinogenicity of simple and ,-, unsaturated aldehydes

Romualdo Benigni
Abstract Aldehydes are important industrial compounds that are used for the synthesis of chemicals and pharmaceuticals and as solvents, food additives, and disinfectants. Because of their reactivity, aldehydes are able to interact with electron-rich biological macromolecules and adverse health effects have been reported, including general toxicity, allergenic reactions, mutagenicity, and carcinogenicity. The cost, time, and number of animals necessary to adequately screen these chemicals places serious limitations on the number of aldehydes whose health potential can be studied and points to the need of using alternative methods for assessing, at least in a preliminary way, the risks associated with the use of aldehydes. A method of choice is the study of quantitative structure,activity relationships (QSARs). In the present work, we present QSAR models for the mutagenicity and carcinogenicity of simple aldehydes and ,-, unsaturated aldehydes. The models point to the role of electrophilicity, bulkiness, and hydrophobicity in the genotoxic activity of the aldehydes and lend themselves to the prediction of the activity of other untested chemicals of the same class. Environ. Mol. Mutagen. 42:136,143, 2003. © 2003 Wiley-Liss, Inc. [source]

Toxicity assessment of mono-substituted benzenes and phenols using a Pseudomonas initial oxygen uptake assay

Ded-Shih Huang
Abstract A methodology is presented for assessing the toxicity of chemical substances through their inhibitory action toward the Pseudomonas initial oxygen uptake (PIOU) rate. The current studies reveal that the PIOU assay is rapid, cost-efficient, and easy to perform. The oxygen uptake rate was found to be associated with a putative benzoate transporter and highly dependent on benzoate concentration. The putative benzoate transporter has been shown to follow Michaelis,Menten kinetics. Most phenols were found to be noncompetitive inhibitors of the benzoate transporter. The inhibition constant (Ki) of these noncompetitive inhibitors can be related to the concentration causing 50% oxygen uptake inhibition in Pseudomonas putida. Modeling these data by using the response,surface approach leads to the development of a quantitative structure,activity relationship (QSAR) for the toxicity of phenols ((1/Ki) = ,0.435 (±0.038) lowest-unoccupied-molecular orbital + 0.517 (±0.027)log KOW ,2.340 (±0.068), n = 49, r2 = 0.930, s = 0.107, r2adj = 0.926, F = 303.1). A comparison of QSAR models derived from the Ki data of the PIOU method and the toxicity data of 40-h Tetrahymena pyrifomis growth inhibition assay (Tetratox) indicated that there was a high correlation between the two approaches (r2 = 0.925). [source]

Quantitative structure-activity relationship methods: Perspectives on drug discovery and toxicology

Roger Perkins
Abstract Quantitative structure,activity relationships (QSARs) attempt to correlate chemical structure with activity using statistical approaches. The QSAR models are useful for various purposes including the prediction of activities of untested chemicals. Quantitative structure,activity relationships and other related approaches have attracted broad scientific interest, particularly in the pharmaceutical industry for drug discovery and in toxicology and environmental science for risk assessment. An assortment of new QSAR methods have been developed during the past decade, most of them focused on drug discovery. Besides advancing our fundamental knowledge of QSARs, these scientific efforts have stimulated their application in a wider range of disciplines, such as toxicology, where QSARs have not yet gained full appreciation. In this review, we attempt to summarize the status of QSAR with emphasis on illuminating the utility and limitations of QSAR technology. We will first review two-dimensional (2D) QSAR with a discussion of the availability and appropriate selection of molecular descriptors. We will then proceed to describe three-dimensional (3D) QSAR and key issues associated with this technology, then compare the relative suitability of 2D and 3D QSAR for different applications. Given the recent technological advances in biological research for rapid identification of drug targets, we mention several examples in which QSAR approaches are employed in conjunction with improved knowledge of the structure and function of the target receptor. The review will conclude by discussing statistical validation of QSAR models, a topic that has received sparse attention in recent years despite its critical importance. [source]

Controlling coverage of D-optimal onion designs and selections

Ing-Marie Olsson
Abstract Statistical molecular design (SMD) is a powerful approach for selection of compound sets in medicinal chemistry and quantitative structure,activity relationships (QSARs) as well as other areas. Two techniques often used in SMD are space-filling and D-optimal designs. Both on occasions lead to unwanted redundancy and replication. To remedy such shortcomings, a generalization of D-optimal selection was recently developed. This new method divides the compound candidate set into a number of subsets (,layers' or ,shells'), and a D-optimal selection is made from each layer. This improves the possibility to select representative molecular structures throughout any property space independently of requested sample size. This is important in complex situations where any given model is unlikely to be valid over the whole investigated domain of experimental conditions. The number of selected molecules can be controlled by varying the number of subsets or by altering the complexity of the model equation in each layer and/or the dependency of previous layers. The new method, called D-optimal onion design (DOOD), will allow the user to choose the model equation complexity independently of sample size while still avoiding unwarranted redundancy. The focus of the present work is algorithmic improvements of DOOD in comparison with classical D-optimal design. As illustrations, extended DOODs have been generated for two applications by in-house programming, including some modifications of the D-optimal algorithm. The performances of the investigated approaches are expected to differ depending on the number of principal properties of the compounds in the design, sample sizes and the investigated model, i.e. the aim of the design. QSAR models have been generated from the selected compound sets, and root mean squared error of prediction (RMSEP) values have been used as measures of performance of the different designs. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Development of a general quantum-chemical descriptor for steric effects: Density functional theory based QSAR study of herbicidal sulfonylurea analogues

Zhen Xi
Abstract Quantitative structure-activity relationship (QSAR) analysis has become one of the most effective approaches for optimizing lead compounds and designing new drugs. Although large number of quantum-chemical descriptors were defined and applied successfully, it is still a big challenge to develop a general quantum-chemical descriptor describing the bulk effects more directly and effectively. In this article, we defined a general quantum-chemical descriptor by characterizing the volume of electron cloud for specific substituent using the method of density functional theory. The application of our defined steric descriptors in the QSAR analysis of sulfonylurea analogues resulted in four QSAR models with high quality (the best model: q2 = 0.881, r2 = 0.901, n = 35, s = 0.401, F = 68.44), which indicated that this descriptor may provide an effective way for solving the problem how to directly describe steric effect in quantum chemistry-based QSAR studies. © 2006 Wiley Periodicals, Inc. J Comput Chem 27: 1571,1576, 2006 [source]

QSAR analysis of interstudy variable skin permeability based on the "latent membrane permeability" concept

Shin-Ichi Fujiwara
Abstract A number of QSAR models for skin permeability have been proposed, but these models lack consistency due to interspecies and interlaboratory differences. This study was initiated to extract an essential QSAR from the multiplicity of data sets of skin permeability by using a novel statistical approach. Ten data sets were collected from the literature, which include a total of 111 permeability coefficients in human, hairless mouse, or hairless rat skin for 94 structurally diverse compounds. Following a Potts and Guy's approach, the octanol/water partition coefficient and molecular weight were chosen as molecular descriptors. All of the data sets were analyzed simultaneously, assuming that all of the sets share a latent, common factor as far as the structure/permeability relationship is concerned. Despite the fact that the degree-of-freedom for the present analysis was limited compared with that for individual regression analyses, the determination coefficients (R2) were high enough for all the 10 data sets, with an average R2 of 0.815 (average R2,=,0.825 for individual analyses). Thus, skin permeability of compounds can be well explained from the log P and M.W., where the ratio of the contribution to skin permeability was approximately 1:1. © 2003 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 92:1939,1946, 2003 [source]

Are Mechanistic and Statistical QSAR Approaches Really Different?

MLR Studies on 158 Cycloalkyl-Pyranones
Abstract Two parallel approaches for quantitative structure-activity relationships (QSAR) are predominant in literature, one guided by mechanistic methods (including read-across) and another by the use of statistical methods. To bridge the gap between these two approaches and to verify their main differences, a comparative study of mechanistically relevant and statistically relevant QSAR models, developed on a case study of 158 cycloalkyl-pyranones, biologically active on inhibition (Ki) of HIV protease, was performed. Firstly, Multiple Linear Regression (MLR) based models were developed starting from a limited amount of molecular descriptors which were widely proven to have mechanistic interpretation. Then robust and predictive MLR models were developed on the same set using two different statistical approaches unbiased of input descriptors. Development of models based on Statistical I method was guided by stepwise addition of descriptors while Genetic Algorithm based selection of descriptors was used for the Statistical II. Internal validation, the standard error of the estimate, and Fisher's significance test were performed for both the statistical models. In addition, external validation was performed for Statistical II model, and Applicability Domain was verified as normally practiced in this approach. The relationships between the activity and the important descriptors selected in all the models were analyzed and compared. It is concluded that, despite the different type and number of input descriptors, and the applied descriptor selection tools or the algorithms used for developing the final model, the mechanistical and statistical approach are comparable to each other in terms of quality and also for mechanistic interpretability of modelling descriptors. Agreement can be observed between these two approaches and the better result could be a consensus prediction from both the models. [source]

Molecular Modeling and Receptor-Dependent (RD) 3D-QSAR Approach to a Set of Antituberculosis Derivatives

Fernanda, Kerly, Mesquita Pasqualoto
Abstract In this study, receptor-dependent (RD) 3D-QSAR models were built for a set of thirty-seven isoniazid derivatives bound to the enoyl-acp reductase from M. tuberculosis, called InhA (PDB entry code 1zid). Ligand-receptor (L-R) molecular dynamics (MD) simulations [500,000 steps; the step size was 0.001,ps (1,fs)] were carried out at 310,K (biological assay temperature). The hypothesized active conformations resulting from a previously reported receptor-independent (IR) 4D-QSAR analysis were used as the molecular geometries of each ligand in this structure-based L-R binding research. The dependent variable is the reported MIC values against M. tuberculosis var. bovis. The independent variables (descriptors) are energy terms of a modified first-generation AMBER force field combined with a hydration shell aqueous solvation model. Genetic function approximation (GFA) formalism and partial least squares (PLS) regression were employed as the fitting functions to develop 3D-QSAR models. The bound ligand solvation energy, the sum of electrostatic and hydrogen bonding energies of the unbound ligand, the bending energy of the unbound ligand, the electrostatic intermolecular L-R energy, and the change in hydrogen bonding energy upon binding were found as important energy contributions to the binding process. The 3D-QSAR model at 310,K has good internal and external predictability and may be regarded as representative of the binding process of ligands to InhA. [source]

The QSAR Modeling of Cytotoxicity on Anthraquinones

Kalev Takkis
Abstract A QSAR analysis was carried out on a dataset of 126 anthraquinone-based cytotoxic compounds. A PCA of the molecular descriptors was used to cluster the dataset into smaller subsets according to their structural features and QSAR models were derived for the selected sets. During the modeling, protonated states of molecules and nonlinear transformations of the descriptors were considered. The developed models have been interpreted in the context of cytotoxicity and validated with leave-one-out, and leave-many-out cross-validation. The descriptors in the resulting models describe the size and charge distribution of molecules although in different clusters their proportions vary. [source]

A Study of CDK2 Inhibitors Using a Novel 3D-QSAR Method Exploiting Receptor Flexibility

Abstract A new 3D-QSAR method based on the novel molecular dynamics methodology, Active Site Pressurization (ASP), has been validated using two cyclin-dependent kinase 2 data sets containing 65 purines and 91 oxindoles. ASP allows the construction of cavity casts that represent the maximal energetically feasible 3D distortion of protein binding sites potentially achievable by induced fit upon binding of ligands. The ASP-QSAR method entails many components of traditional 3D-QSAR strategies but additionally correlates the biological activity of ligand sets with features of ASP-derived binding site cavity casts, thus taking target protein flexibility into account implicitly. Both of the data sets used to validate the ASP-QSAR method resulted in QSAR models that were of exceptional quality and predictivity. A non-cross-validated variance coefficient (R2) between 0.959 and 0.99 and a cross-validated variance coefficient (Q2) of between 0.927 and 0.929 were obtained for these ASP-QSAR models. [source]

A 3-D QSAR Study of Catechol- O -Methyltransferase Inhibitors Using CoMFA and CoMSIA

Chunzhi Ai
Abstract Inhibitors of Catechol- O -Methyltransferase (COMT) play an important role in the treatment of Parkinson's Disease (PD). A new Three-Dimensional Quantitative Structure,Activity Relationship (3-D QSAR) analysis was performed on 36 previously reported COMT inhibitors employing Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methodologies to correlate the molecular fields and percent inhibition values and three predictive models were derived. The CoMFA and CoMSIA models with steric and electrostatic field yielded cross-validated rs of 0.585 and 0.528, respectively whereas the conventional rs were 0.979 and 0.891, respectively. The CoMSIA model with hydrophobic field exhibited a r of 0.544 and a r of 0.930. The individual inspection of 3-D contours generated from these models helps in understanding the possible region for structural modification of molecules to improve the inhibitory bioactivity. These 3-D QSAR models are also useful for designing and predicting novel COMT inhibitors. [source]

Principles of QSAR models validation: internal and external

Paola Gramatica
Abstract The recent REACH Policy of the European Union has led to scientists and regulators to focus their attention on establishing general validation principles for QSAR models in the context of chemical regulation (previously known as the Setubal, nowadays, the OECD principles). This paper gives a brief analysis of some principles: unambiguous algorithm, Applicability Domain (AD), and statistical validation. Some concerns related to QSAR algorithm reproducibility and an example of a fast check of the applicability domain for MLR models are presented. Common myths and misconceptions related to popular techniques for verifying internal predictivity, particularly for MLR models (for instance cross-validation, bootstrap), are commented on and compared with commonly used statistical techniques for external validation. The differences in the two validating approaches are highlighted, and evidence is presented that only models that have been validated externally, after their internal validation, can be considered reliable and applicable for both external prediction and regulatory purposes. [source]

Status of HTS Data Mining Approaches

Alexander Böcker
Abstract High-throughput screening of large compound collections results in large sets of data. This review gives an overview of the most frequently employed computational techniques for the analysis of such data and the establishment of first QSAR models. Various methods for descriptor selection, classification and data mining are discussed. Recent trends include the application of kernel-based machine learning methods for the design of focused libraries and compilation of target-family biased compound collections. [source]

Prediction of Tyrosinase Inhibition Activity Using Atom-Based Bilinear Indices

CHEMMEDCHEM, Issue 4 2007
Yovani Marrero-Ponce Prof.
Abstract A set of novel atom-based molecular fingerprints is proposed based on a bilinear map similar to that defined in linear algebra. These molecular descriptors (MDs) are proposed as a new means of molecular parametrization easily calculated from 2D molecular information. The nonstochastic and stochastic molecular indices match molecular structure provided by molecular topology by using the kth nonstochastic and stochastic graph-theoretical electronic-density matrices, Mk and Sk, respectively. Thus, the kth nonstochastic and stochastic bilinear indices are calculated using Mk and Sk as matrix operators of bilinear transformations. Chemical information is coded by using different pair combinations of atomic weightings (mass, polarizability, vdW volume, and electronegativity). The results of QSAR studies of tyrosinase inhibitors using the new MDs and linear discriminant analysis (LDA) demonstrate the ability of the bilinear indices in testing biological properties. A database of 246 structurally diverse tyrosinase inhibitors was assembled. An inactive set of 412 drugs with other clinical uses was used; both active and inactive sets were processed by hierarchical and partitional cluster analyses to design training and predicting sets. Twelve LDA-based QSAR models were obtained, the first six using the nonstochastic total and local bilinear indices and the last six with the stochastic MDs. The discriminant models were applied; globally good classifications of 99.58 and 89.96,% were observed for the best nonstochastic and stochastic bilinear indices models in the training set along with high Matthews correlation coefficients (C) of 0.99 and 0.79, respectively, in the learning set. External prediction sets used to validate the models obtained were correctly classified, with accuracies of 100 and 87.78,%, respectively, yielding C values of 1.00 and 0.73. This subset contains 180 active and inactive compounds not considered to fit the models. A simulated virtual screen demonstrated this approach in searching tyrosinase inhibitors from compounds never considered in either training or predicting series. These fitted models permitted the selection of new cycloartane compounds isolated from herbal plants as new tyrosinase inhibitors. A good correspondence between theoretical and experimental inhibitory effects on tyrosinase was observed; compound CA6 (IC50=1.32,,M) showed higher activity than the reference compounds kojic acid (IC50=16.67,,M) and L -mimosine (IC50=3.68,,M). [source]