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Special Issue "QSAR and Its Applications"

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A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Theoretical Chemistry".

Deadline for manuscript submissions: closed (30 June 2012)

Special Issue Editor

Guest Editor
Dr. Lim Kok Hwa

Division of Chemical and Biomolecular Engineering(CBE), School of Chemical and Biomedical Engineering(SCBE), Nanyang Technological University(NTU), N1.2-B1-13,62 Nanyang Drive, 637459 , Singapore
Interests: ab-initio, semi-empirical quantum chemical methods; topological, physicochemical, electronic descriptors; lead compound design, docking, pharmacophore mapping

Special Issue Information

Dear Colleagues,

QSAR modeling is an integral part of rational drug design (RDD). Despite the prediction of biological activities, QSAR models help to identify the parameters responsible for biological response that is essential for lead compound optimization. In addition, recent developments in molecular docking have been successful to provide information such relative orientation of drug molecules binding to their targeted receptor leading to optimization of lead compound to achieve more potent and selective analogs. Despite the successful application of QSAR to predict biological activities, few QSAR studies have been reported on biological activities of metal-complexes, probably due to the lack of specific metal ligand parameters. Recently, the successful use of density functional theory (DFT) to calculate chemical descriptors of metal complexes also open-up new era for QSAR studies on metal complexes. This special issue of Molecules will consider submissions related to QSAR of biological activities. For examples, prediction of biological activities of metal-complexes or molecular entities using physicochemical, steric, topological as well as ab-initio quantum chemical, pharmacophore mapping and molecular docking descriptors.

Dr. Lim Kok Hwa
Guest Editor

Keywords

  • Ab-initio
  • semi-empirical quantum chemical methods
  • topological
  • physicochemical
  • electronic descriptors
  • metal complexes
  • pharmacophore mapping
  • molecular docking
  • lead compound optimization

Published Papers (15 papers)

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Research

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Open AccessArticle Identification of Electronic and Structural Descriptors of Adenosine Analogues Related to Inhibition of Leishmanial Glyceraldehyde-3-Phosphate Dehydrogenase
Molecules 2013, 18(5), 5032-5050; doi:10.3390/molecules18055032
Received: 10 September 2012 / Revised: 27 April 2013 / Accepted: 28 April 2013 / Published: 29 April 2013
Cited by 5 | PDF Full-text (538 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Quantitative structure–activity relationship (QSAR) studies were performed in order to identify molecular features responsible for the antileishmanial activity of 61 adenosine analogues acting as inhibitors of the enzyme glyceraldehyde 3-phosphate dehydrogenase of Leishmania mexicana (LmGAPDH). Density functional theory (DFT) was [...] Read more.
Quantitative structure–activity relationship (QSAR) studies were performed in order to identify molecular features responsible for the antileishmanial activity of 61 adenosine analogues acting as inhibitors of the enzyme glyceraldehyde 3-phosphate dehydrogenase of Leishmania mexicana (LmGAPDH). Density functional theory (DFT) was employed to calculate quantum-chemical descriptors, while several structural descriptors were generated with Dragon 5.4. Variable selection was undertaken with the ordered predictor selection (OPS) algorithm, which provided a set with the most relevant descriptors to perform PLS, PCR and MLR regressions. Reliable and predictive models were obtained, as attested by their high correlation coefficients, as well as the agreement between predicted and experimental values for an external test set. Additional validation procedures were carried out, demonstrating that robust models were developed, providing helpful tools for the optimization of the antileishmanial activity of adenosine compounds. Full article
(This article belongs to the Special Issue QSAR and Its Applications)
Open AccessArticle Benchmarking Ligand-Based Virtual High-Throughput Screening with the PubChem Database
Molecules 2013, 18(1), 735-756; doi:10.3390/molecules18010735
Received: 26 September 2012 / Revised: 11 October 2012 / Accepted: 17 December 2012 / Published: 8 January 2013
Cited by 20 | PDF Full-text (268 KB) | Supplementary Files
Abstract
With the rapidly increasing availability of High-Throughput Screening (HTS) data in the public domain, such as the PubChem database, methods for ligand-based computer-aided drug discovery (LB-CADD) have the potential to accelerate and reduce the cost of probe development and drug discovery efforts [...] Read more.
With the rapidly increasing availability of High-Throughput Screening (HTS) data in the public domain, such as the PubChem database, methods for ligand-based computer-aided drug discovery (LB-CADD) have the potential to accelerate and reduce the cost of probe development and drug discovery efforts in academia. We assemble nine data sets from realistic HTS campaigns representing major families of drug target proteins for benchmarking LB-CADD methods. Each data set is public domain through PubChem and carefully collated through confirmation screens validating active compounds. These data sets provide the foundation for benchmarking a new cheminformatics framework BCL::ChemInfo, which is freely available for non-commercial use. Quantitative structure activity relationship (QSAR) models are built using Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Decision Trees (DTs), and Kohonen networks (KNs). Problem-specific descriptor optimization protocols are assessed including Sequential Feature Forward Selection (SFFS) and various information content measures. Measures of predictive power and confidence are evaluated through cross-validation, and a consensus prediction scheme is tested that combines orthogonal machine learning algorithms into a single predictor. Enrichments ranging from 15 to 101 for a TPR cutoff of 25% are observed. Full article
(This article belongs to the Special Issue QSAR and Its Applications)
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Open AccessArticle QSPR Models for Predicting Log Pliver Values for Volatile Organic Compounds Combining Statistical Methods and Domain Knowledge
Molecules 2012, 17(12), 14937-14953; doi:10.3390/molecules171214937
Received: 10 September 2012 / Revised: 12 December 2012 / Accepted: 13 December 2012 / Published: 17 December 2012
Cited by 2 | PDF Full-text (530 KB) | Supplementary Files
Abstract
Volatile organic compounds (VOCs) are contained in a variety of chemicals that can be found in household products and may have undesirable effects on health. Thereby, it is important to model blood-to-liver partition coefficients (log Pliver) for VOCs in a [...] Read more.
Volatile organic compounds (VOCs) are contained in a variety of chemicals that can be found in household products and may have undesirable effects on health. Thereby, it is important to model blood-to-liver partition coefficients (log Pliver) for VOCs in a fast and inexpensive way. In this paper, we present two new quantitative structure-property relationship (QSPR) models for the prediction of log Pliver, where we also propose a hybrid approach for the selection of the descriptors. This hybrid methodology combines a machine learning method with a manual selection based on expert knowledge. This allows obtaining a set of descriptors that is interpretable in physicochemical terms. Our regression models were trained using decision trees and neural networks and validated using an external test set. Results show high prediction accuracy compared to previous log Pliver models, and the descriptor selection approach provides a means to get a small set of descriptors that is in agreement with theoretical understanding of the target property. Full article
(This article belongs to the Special Issue QSAR and Its Applications)
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Open AccessArticle Computational Prediction of Blood-Brain Barrier Permeability Using Decision Tree Induction
Molecules 2012, 17(9), 10429-10445; doi:10.3390/molecules170910429
Received: 8 June 2012 / Revised: 17 August 2012 / Accepted: 27 August 2012 / Published: 31 August 2012
Cited by 22 | PDF Full-text (269 KB) | Supplementary Files
Abstract
Predicting blood-brain barrier (BBB) permeability is essential to drug development, as a molecule cannot exhibit pharmacological activity within the brain parenchyma without first transiting this barrier. Understanding the process of permeation, however, is complicated by a combination of both limited passive diffusion [...] Read more.
Predicting blood-brain barrier (BBB) permeability is essential to drug development, as a molecule cannot exhibit pharmacological activity within the brain parenchyma without first transiting this barrier. Understanding the process of permeation, however, is complicated by a combination of both limited passive diffusion and active transport. Our aim here was to establish predictive models for BBB drug permeation that include both active and passive transport. A database of 153 compounds was compiled using in vivo surface permeability product (logPS) values in rats as a quantitative parameter for BBB permeability. The open source Chemical Development Kit (CDK) was used to calculate physico-chemical properties and descriptors. Predictive computational models were implemented by machine learning paradigms (decision tree induction) on both descriptor sets. Models with a corrected classification rate (CCR) of 90% were established. Mechanistic insight into BBB transport was provided by an Ant Colony Optimization (ACO)-based binary classifier analysis to identify the most predictive chemical substructures. Decision trees revealed descriptors of lipophilicity (aLogP) and charge (polar surface area), which were also previously described in models of passive diffusion. However, measures of molecular geometry and connectivity were found to be related to an active drug transport component. Full article
(This article belongs to the Special Issue QSAR and Its Applications)
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Open AccessArticle BCL::EMAS — Enantioselective Molecular Asymmetry Descriptor for 3D-QSAR
Molecules 2012, 17(8), 9971-9989; doi:10.3390/molecules17089971
Received: 29 June 2012 / Revised: 15 August 2012 / Accepted: 15 August 2012 / Published: 20 August 2012
Cited by 3 | PDF Full-text (803 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Stereochemistry is an important determinant of a molecule’s biological activity. Stereoisomers can have different degrees of efficacy or even opposing effects when interacting with a target protein. Stereochemistry is a molecular property difficult to represent in 2D-QSAR as it is an inherently [...] Read more.
Stereochemistry is an important determinant of a molecule’s biological activity. Stereoisomers can have different degrees of efficacy or even opposing effects when interacting with a target protein. Stereochemistry is a molecular property difficult to represent in 2D-QSAR as it is an inherently three-dimensional phenomenon. A major drawback of most proposed descriptors for 3D-QSAR that encode stereochemistry is that they require a heuristic for defining all stereocenters and rank-ordering its substituents. Here we propose a novel 3D-QSAR descriptor termed Enantioselective Molecular ASymmetry (EMAS) that is capable of distinguishing between enantiomers in the absence of such heuristics. The descriptor aims to measure the deviation from an overall symmetric shape of the molecule. A radial-distribution function (RDF) determines a signed volume of tetrahedrons of all triplets of atoms and the molecule center. The descriptor can be enriched with atom-centric properties such as partial charge. This descriptor showed good predictability when tested with a dataset of thirty-one steroids commonly used to benchmark stereochemistry descriptors (r2 = 0.89, q2 = 0.78). Additionally, EMAS improved enrichment of 4.38 versus 3.94 without EMAS in a simulated virtual high-throughput screening (vHTS) for inhibitors and substrates of cytochrome P450 (PUBCHEM AID891). Full article
(This article belongs to the Special Issue QSAR and Its Applications)
Open AccessArticle Hologram QSAR Models of 4-[(Diethylamino)methyl]-phenol Inhibitors of Acetyl/Butyrylcholinesterase Enzymes as Potential Anti-Alzheimer Agents
Molecules 2012, 17(8), 9529-9539; doi:10.3390/molecules17089529
Received: 25 June 2012 / Revised: 17 July 2012 / Accepted: 26 July 2012 / Published: 9 August 2012
Cited by 11 | PDF Full-text (337 KB) | XML Full-text
Abstract
Hologram QSAR models were developed for a series of 36 inhibitors (29 training set and seven test set compounds) of acetyl/butyrylcholinesterase (AChE/BChE) enzymes, an attractive molecular target for Alzheimer’s disease (AD) treatment. The HQSAR models (N = 29) exhibited significant cross-validated (AChE, [...] Read more.
Hologram QSAR models were developed for a series of 36 inhibitors (29 training set and seven test set compounds) of acetyl/butyrylcholinesterase (AChE/BChE) enzymes, an attractive molecular target for Alzheimer’s disease (AD) treatment. The HQSAR models (N = 29) exhibited significant cross-validated (AChE, q2 = 0.787; BChE, q2 = 0. 904) and non-cross-validated (AChE, r2 = 0.965; BChE, r2 = 0.952) correlation coefficients. The models were used to predict the inhibitory potencies of the test set compounds, and agreement between the experimental and predicted values was verified, exhibiting a powerful predictive capability. Contribution maps show that structural fragments containing aromatic moieties and long side chains increase potency. Both the HQSAR models and the contribution maps should be useful for the further design of novel, structurally related cholinesterase inhibitors. Full article
(This article belongs to the Special Issue QSAR and Its Applications)
Open AccessArticle Prediction of Acute Mammalian Toxicity Using QSAR Methods: A Case Study of Sulfur Mustard and Its Breakdown Products
Molecules 2012, 17(8), 8982-9001; doi:10.3390/molecules17088982
Received: 6 June 2012 / Revised: 19 July 2012 / Accepted: 23 July 2012 / Published: 27 July 2012
Cited by 9 | PDF Full-text (550 KB) | HTML Full-text | XML Full-text
Abstract
Predicting toxicity quantitatively, using Quantitative Structure Activity Relationships (QSAR), has matured over recent years to the point that the predictions can be used to help identify missing comparison values in a substance’s database. In this manuscript we investigate using the lethal dose [...] Read more.
Predicting toxicity quantitatively, using Quantitative Structure Activity Relationships (QSAR), has matured over recent years to the point that the predictions can be used to help identify missing comparison values in a substance’s database. In this manuscript we investigate using the lethal dose that kills fifty percent of a test population (the LD50) for determining relative toxicity of a number of substances. In general, the smaller the LD50 value, the more toxic the chemical, and the larger the LD50 value, the lower the toxicity. When systemic toxicity and other specific toxicity data are unavailable for the chemical(s) of interest, during emergency responses, LD50 values may be employed to determine the relative toxicity of a series of chemicals. In the present study, a group of chemical warfare agents and their breakdown products have been evaluated using four available rat oral QSAR LD50 models. The QSAR analysis shows that the breakdown products of Sulfur Mustard (HD) are predicted to be less toxic than the parent compound as well as other known breakdown products that have known toxicities. The QSAR estimated break down products LD50 values ranged from 299 mg/kg to 5,764 mg/kg. This evaluation allows for the ranking and toxicity estimation of compounds for which little toxicity information existed; thus leading to better risk decision making in the field. Full article
(This article belongs to the Special Issue QSAR and Its Applications)
Open AccessArticle Application of 4D-QSAR Studies to a Series of Raloxifene Analogs and Design of Potential Selective Estrogen Receptor Modulators
Molecules 2012, 17(6), 7415-7439; doi:10.3390/molecules17067415
Received: 12 April 2012 / Revised: 4 June 2012 / Accepted: 5 June 2012 / Published: 15 June 2012
Cited by 3 | PDF Full-text (753 KB)
Abstract
Four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis was applied on a series of 54 2-arylbenzothiophene derivatives, synthesized by Grese and coworkers, based on raloxifene (an estrogen receptor-alpha antagonist), and evaluated as ERa ligands and as inhibitors of estrogen-stimulated proliferation of MCF-7 breast cancer [...] Read more.
Four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis was applied on a series of 54 2-arylbenzothiophene derivatives, synthesized by Grese and coworkers, based on raloxifene (an estrogen receptor-alpha antagonist), and evaluated as ERa ligands and as inhibitors of estrogen-stimulated proliferation of MCF-7 breast cancer cells. The conformations of each analogue, sampled from a molecular dynamics simulation, were placed in a grid cell lattice according to three trial alignments, considering two grid cell sizes (1.0 and 2.0 Å). The QSAR equations, generated by a combined scheme of genetic algorithms (GA) and partial least squares (PLS) regression, were evaluated by “leave-one-out” cross-validation, using a training set of 41 compounds. External validation was performed using a test set of 13 compounds. The obtained 4D-QSAR models are in agreement with the proposed mechanism of action for raloxifene. This study allowed a quantitative prediction of compounds’ potency and supported the design of new raloxifene analogs. Full article
(This article belongs to the Special Issue QSAR and Its Applications)
Open AccessArticle A QSAR Study of Environmental Estrogens Based on a Novel Variable Selection Method
Molecules 2012, 17(5), 6126-6145; doi:10.3390/molecules17056126
Received: 10 March 2012 / Revised: 19 April 2012 / Accepted: 26 April 2012 / Published: 21 May 2012
Cited by 5 | PDF Full-text (718 KB) | Supplementary Files
Abstract
A large number of descriptors were employed to characterize the molecular structure of 53 natural, synthetic, and environmental chemicals which are suspected of disrupting endocrine functions by mimicking or antagonizing natural hormones and may thus pose a serious threat to the health [...] Read more.
A large number of descriptors were employed to characterize the molecular structure of 53 natural, synthetic, and environmental chemicals which are suspected of disrupting endocrine functions by mimicking or antagonizing natural hormones and may thus pose a serious threat to the health of humans and wildlife. In this work, a robust quantitative structure-activity relationship (QSAR) model with a novel variable selection method has been proposed for the effective estrogens. The variable selection method is based on variable interaction (VSMVI) with leave-multiple-out cross validation (LMOCV) to select the best subset. During variable selection, model construction and assessment, the Organization for Economic Co-operation and Development (OECD) principles for regulation of QSAR acceptability were fully considered, such as using an unambiguous multiple-linear regression (MLR) algorithm to build the model, using several validation methods to assessment the performance of the model, giving the define of applicability domain and analyzing the outliers with the results of molecular docking. The performance of the QSAR model indicates that the VSMVI is an effective, feasible and practical tool for rapid screening of the best subset from large molecular descriptors. Full article
(This article belongs to the Special Issue QSAR and Its Applications)
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Open AccessArticle QSAR Modeling on Benzo[c]phenanthridine Analogues as Topoisomerase I Inhibitors and Anti-cancer Agents
Molecules 2012, 17(5), 5690-5712; doi:10.3390/molecules17055690
Received: 5 April 2012 / Revised: 25 April 2012 / Accepted: 4 May 2012 / Published: 11 May 2012
Cited by 5 | PDF Full-text (762 KB) | Supplementary Files
Abstract
Benzo[c]phenanthridine (BCP) derivatives were identified as topoisomerase I (TOP-I) targeting agents with pronounced antitumor activity. In this study, hologram-QSAR, 2D-QSAR and 3D-QSAR models were developed for BCPs on topoisomerase I inbibitory activity and cytotoxicity against seven tumor cell lines including [...] Read more.
Benzo[c]phenanthridine (BCP) derivatives were identified as topoisomerase I (TOP-I) targeting agents with pronounced antitumor activity. In this study, hologram-QSAR, 2D-QSAR and 3D-QSAR models were developed for BCPs on topoisomerase I inbibitory activity and cytotoxicity against seven tumor cell lines including RPMI8402, CPT-K5, P388, CPT45, KB3-1, KBV-1and KBH5.0. The hologram, 2D, and 3D-QSAR models were obtained with the square of correlation coefficient R2 = 0.58 − 0.77, the square of the crossvalidation coefficient q2 = 0.41 − 0.60 as well as the external set’s square of predictive correlation coefficient r2 = 0.51 − 0.80. Moreover, the assessment method based on reliability test with confidence level of 95% was used to validate the predictive power of QSAR models and to prevent over-fitting phenomenon of classical QSAR models. Our QSAR model could be applied to design new analogues of BCPs with higher antitumor and topoisomerase I inhibitory activity. Full article
(This article belongs to the Special Issue QSAR and Its Applications)
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Open AccessArticle Comparison of Different Approaches to Define the Applicability Domain of QSAR Models
Molecules 2012, 17(5), 4791-4810; doi:10.3390/molecules17054791
Received: 14 March 2012 / Revised: 13 April 2012 / Accepted: 16 April 2012 / Published: 25 April 2012
Cited by 68 | PDF Full-text (317 KB)
Abstract
One of the OECD principles for model validation requires defining the Applicability Domain (AD) for the QSAR models. This is important since the reliable predictions are generally limited to query chemicals structurally similar to the training compounds used to build the model. [...] Read more.
One of the OECD principles for model validation requires defining the Applicability Domain (AD) for the QSAR models. This is important since the reliable predictions are generally limited to query chemicals structurally similar to the training compounds used to build the model. Therefore, characterization of interpolation space is significant in defining the AD and in this study some existing descriptor-based approaches performing this task are discussed and compared by implementing them on existing validated datasets from the literature. Algorithms adopted by different approaches allow defining the interpolation space in several ways, while defined thresholds contribute significantly to the extrapolations. For each dataset and approach implemented for this study, the comparison analysis was carried out by considering the model statistics and relative position of test set with respect to the training space. Full article
(This article belongs to the Special Issue QSAR and Its Applications)
Open AccessArticle Modeling Chemical Interaction Profiles: I. Spectral Data-Activity Relationship and Structure-Activity Relationship Models for Inhibitors and Non-inhibitors of Cytochrome P450 CYP3A4 and CYP2D6 Isozymes
Molecules 2012, 17(3), 3383-3406; doi:10.3390/molecules17033383
Received: 20 January 2012 / Revised: 27 February 2012 / Accepted: 28 February 2012 / Published: 15 March 2012
Cited by 6 | PDF Full-text (251 KB) | Supplementary Files
Abstract
An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals—drugs, pesticides, and environmental pollutants—interact at the level of metabolic biotransformations mediated by cytochrome P450 [...] Read more.
An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals—drugs, pesticides, and environmental pollutants—interact at the level of metabolic biotransformations mediated by cytochrome P450 (CYP) enzymes. In the present work, spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches were used to develop machine-learning classifiers of inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference pharmaceutical compounds whose interactions have been deduced from clinical data, and 100 additional chemicals that were used to evaluate model performance in an external validation (EV) test. SDAR is an innovative modeling approach that relies on discriminant analysis applied to binned nuclear magnetic resonance (NMR) spectral descriptors. In the present work, both 1D 13C and 1D 15N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D 13C-NMR and 15N-NMR spectra caused an increase in the tenfold cross-validation (CV) performance in terms of both the rate of correct classification and sensitivity. The results of SDAR modeling were verified using SAR. For SAR modeling, a decision forest approach involving from 6 to 17 Mold2 descriptors in a tree was used. Average rates of correct classification of SDAR and SAR models in a hundred CV tests were 60% and 61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4, and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data. Based on unique NMR structural descriptors, the new SDAR modeling method complements the existing SAR techniques, providing an independent estimator that can increase confidence in a structure-activity assessment. When modeling was applied to hazardous environmental chemicals, it was found that up to 20% of them may be substrates and up to 10% of them may be inhibitors of the CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for the environmental health branch of the public health service to extrapolate to hazardous chemicals directly from human clinical data. Therefore, the pharmacological and environmental health branches are both expected to benefit from these reported models. Full article
(This article belongs to the Special Issue QSAR and Its Applications)
Open AccessArticle Modeling Chemical Interaction Profiles: II. Molecular Docking, Spectral Data-Activity Relationship, and Structure-Activity Relationship Models for Potent and Weak Inhibitors of Cytochrome P450 CYP3A4 Isozyme
Molecules 2012, 17(3), 3407-3460; doi:10.3390/molecules17033407
Received: 20 January 2012 / Revised: 27 February 2012 / Accepted: 28 February 2012 / Published: 15 March 2012
Cited by 5 | PDF Full-text (1209 KB) | Supplementary Files
Abstract
Polypharmacy increasingly has become a topic of public health concern, particularly as the U.S. population ages. Drug labels often contain insufficient information to enable the clinician to safely use multiple drugs. Because many of the drugs are bio-transformed by cytochrome P450 (CYP) [...] Read more.
Polypharmacy increasingly has become a topic of public health concern, particularly as the U.S. population ages. Drug labels often contain insufficient information to enable the clinician to safely use multiple drugs. Because many of the drugs are bio-transformed by cytochrome P450 (CYP) enzymes, inhibition of CYP activity has long been associated with potentially adverse health effects. In an attempt to reduce the uncertainty pertaining to CYP-mediated drug-drug/chemical interactions, an interagency collaborative group developed a consensus approach to prioritizing information concerning CYP inhibition. The consensus involved computational molecular docking, spectral data-activity relationship (SDAR), and structure-activity relationship (SAR) models that addressed the clinical potency of CYP inhibition. The models were built upon chemicals that were categorized as either potent or weak inhibitors of the CYP3A4 isozyme. The categorization was carried out using information from clinical trials because currently available in vitro high-throughput screening data were not fully representative of the in vivo potency of inhibition. During categorization it was found that compounds, which break the Lipinski rule of five by molecular weight, were about twice more likely to be inhibitors of CYP3A4 compared to those, which obey the rule. Similarly, among inhibitors that break the rule, potent inhibitors were 2–3 times more frequent. The molecular docking classification relied on logistic regression, by which the docking scores from different docking algorithms, CYP3A4 three-dimensional structures, and binding sites on them were combined in a unified probabilistic model. The SDAR models employed a multiple linear regression approach applied to binned 1D 13C-NMR and 1D 15N-NMR spectral descriptors. Structure-based and physical-chemical descriptors were used as the basis for developing SAR models by the decision forest method. Thirty-three potent inhibitors and 88 weak inhibitors of CYP3A4 were used to train the models. Using these models, a synthetic majority rules consensus classifier was implemented, while the confidence of estimation was assigned following the percent agreement strategy. The classifier was applied to a testing set of 120 inhibitors not included in the development of the models. Five compounds of the test set, including known strong inhibitors dalfopristin and tioconazole, were classified as probable potent inhibitors of CYP3A4. Other known strong inhibitors, such as lopinavir, oltipraz, quercetin, raloxifene, and troglitazone, were among 18 compounds classified as plausible potent inhibitors of CYP3A4. The consensus estimation of inhibition potency is expected to aid in the nomination of pharmaceuticals, dietary supplements, environmental pollutants, and occupational and other chemicals for in-depth evaluation of the CYP3A4 inhibitory activity. It may serve also as an estimate of chemical interactions via CYP3A4 metabolic pharmacokinetic pathways occurring through polypharmacy and nutritional and environmental exposures to chemical mixtures. Full article
(This article belongs to the Special Issue QSAR and Its Applications)
Open AccessArticle 3D-QSAR Studies of Dihydropyrazole and Dihydropyrrole Derivatives as Inhibitors of Human Mitotic Kinesin Eg5 Based on Molecular Docking
Molecules 2012, 17(2), 2015-2029; doi:10.3390/molecules17022015
Received: 7 February 2012 / Revised: 7 February 2012 / Accepted: 13 February 2012 / Published: 17 February 2012
Cited by 5 | PDF Full-text (501 KB)
Abstract
Human mitotic kinesin Eg5 plays an essential role in mitoses and is an interesting drug target against cancer. To find the correlation between Eg5 and its inhibitors, structure-based 3D-quantitative structure–activity relationship (QSAR) studies were performed on a series of dihydropyrazole and dihydropyrrole [...] Read more.
Human mitotic kinesin Eg5 plays an essential role in mitoses and is an interesting drug target against cancer. To find the correlation between Eg5 and its inhibitors, structure-based 3D-quantitative structure–activity relationship (QSAR) studies were performed on a series of dihydropyrazole and dihydropyrrole derivatives using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. Based on the LigandFit docking results, predictive 3D-QSAR models were established, with cross-validated coefficient values (q2) up to 0.798 for CoMFA and 0.848 for CoMSIA, respectively. Furthermore, the CoMFA and CoMSIA models were mapped back to the binding sites of Eg5, which could provide a better understanding of vital interactions between the inhibitors and the kinase. Ligands binding in hydrophobic part of the inhibitor-binding pocket were found to be crucial for potent ligand binding and kinases selectivity. The analyses may be used to design more potent EG5 inhibitors and predict their activities prior to synthesis. Full article
(This article belongs to the Special Issue QSAR and Its Applications)

Review

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Open AccessReview Insights on Cytochrome P450 Enzymes and Inhibitors Obtained Through QSAR Studies
Molecules 2012, 17(8), 9283-9305; doi:10.3390/molecules17089283
Received: 29 June 2012 / Revised: 24 July 2012 / Accepted: 26 July 2012 / Published: 3 August 2012
Cited by 17 | PDF Full-text (509 KB) | HTML Full-text | XML Full-text
Abstract
The cytochrome P450 (CYP) superfamily of heme enzymes play an important role in the metabolism of a large number of endogenous and exogenous compounds, including most of the drugs currently on the market. Inhibitors of CYP enzymes have important roles in the [...] Read more.
The cytochrome P450 (CYP) superfamily of heme enzymes play an important role in the metabolism of a large number of endogenous and exogenous compounds, including most of the drugs currently on the market. Inhibitors of CYP enzymes have important roles in the treatment of several disease conditions such as numerous cancers and fungal infections in addition to their critical role in drug-drug interactions. Structure activity relationships (SAR), and three-dimensional quantitative structure activity relationships (3D-QSAR) represent important tools in understanding the interactions of the inhibitors with the active sites of the CYP enzymes. A comprehensive account of the QSAR studies on the major human CYPs 1A1, 1A2, 1B1, 2A6, 2B6, 2C9, 2C19, 2D6, 2E1, 3A4 and a few other CYPs are detailed in this review which will provide us with an insight into the individual/common characteristics of the active sites of these enzymes and the enzyme-inhibitor interactions. Full article
(This article belongs to the Special Issue QSAR and Its Applications)

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