A model for identifying HERG K+ channel blockers

Molecular mechanisms for drug interactions with hERG that cause long QT syndrome

The human ether-a-go-go-related gene (hERG) encodes the pore-forming alpha-subunit of a voltage-gated potassium (K(+)) channel. A variety of unrelated compounds reduce K(+ )current in the heart by blocking the pore or disrupting trafficking of the hERG channel to the membrane surface. This induces a syndrome known as long QT, which arises from abnormalities in action potential repolarisation and can degenerate into lethal cardiac arrhythmias. As a result, this undesirable side effect has severely hindered safe drug development. This review describes progress in understanding the molecular basis for drug binding to hERG, outlines the characteristics of hERG ligands and discusses experimental and in silico approaches for identifying compounds with QT liabilities. Recent developments should enable recognition of hERG-positive compounds at the early stages of their development.

Discovery of 1-[1-(1-methylcyclooctyl)-4-piperidinyl]-2-[(3R)-3-piperidinyl]-1H-benzimidazole: integrated drug-design and structure-activity relationships for orally potent, metabolically stable and potential-risk reduced novel non-peptide nociceptin/orphanin FQ receptor agonist as antianxiety drug

Anxiety disorders, caused by continuous or acute stress or fear, have been highly prevailing psychiatric disorders. For the acute treatment of the disorders, benzodiazepines have been widely used despite having liabilities that limit their utility. Alternatively, endogenous nociceptin/orphanin FQ and nociceptin/orphanin FQ peptide receptor (or opioid-receptor-like-1 receptor) have important roles in the integration of emotional components, e.g. anxiolytic activity is the key behavioral action of nociceptin/orphanin FQ in brain. In our preceding study, various structurally novel 1,2-disubstituted benzimidazole derivatives were designed and synthesized as highly potent nociceptin/orphanin FQ peptide receptor selective full agonists in vitro with high or moderate nociceptin/orphanin FQ peptide receptor occupancy in the mice brain per os based on appropriate physicochemical properties for the oral brain activity [Hayashi et al. (2009) J Med Chem;52:610-625]. In the present study, drug design and structure-activity relationships for Vogel anticonflict activities in mice per os, metabolic stabilities in human liver microsome, CYP2D6 inhibitions, serum protein bindings, and human ether-a-go-go related gene binding affinities of novel nociceptin/orphanin FQ peptide receptor agonists were investigated. Through the series of coherent drug discovery studies, the strongest nociceptin/orphanin FQ peptide receptor agonist, 1-[1-(1-methylcyclooctyl)-4-piperidinyl]-2-[(3R)-3-piperidinyl]-1H-benzimidazole was designed and identified as a new-class orally potent anxiolytic with little side-effects, as significant findings.

Collation, assessment and analysis of literature in vitro data on hERG receptor blocking potency for subsequent modeling of drugs' cardiotoxic properties

The assessment of the torsadogenic potency of a new chemical entity is a crucial issue during lead optimization and the drug development process. It is required by the regulatory agencies during the registration process. In recent years, there has been a considerable interest in developing in silico models, which allow prediction of drug-hERG channel interaction at the early stage of a drug development process. The main mechanism underlying an acquired QT syndrome and a potentially fatal arrhythmia called torsades de pointes is the inhibition of potassium channel encoded by hERG (the human ether-a-go-go-related gene). The concentration producing half-maximal block of the hERG potassium current (IC(50)) is a surrogate marker for proarrhythmic properties of compounds and is considered a test for cardiac safety of drugs or drug candidates. The IC(50) values, obtained from data collected during electrophysiological studies, are highly dependent on experimental conditions (i.e. model, temperature, voltage protocol). For the in silico models' quality and performance, the data quality and consistency is a crucial issue. Therefore the main objective of our work was to collect and assess the hERG IC(50) data available in accessible scientific literature to provide a high-quality data set for further studies.

Accurate and interpretable computational modeling of chemical mutagenicity

We describe a method for modeling chemical mutagenicity in terms of simple rules based on molecular features. A classification model was built using a rule-based ensemble method called RuleFit, developed by Friedman and Popescu. We show how performance compares favorably against literature methods. Performance was measured through the use of cross-validation and testing on external test sets. All data sets used are publicly available. The method automatically generated transparent rules in terms of molecular structure that agree well with known toxicology. While we have focused on chemical mutagenicity in demonstrating this method, we anticipate that it may be more generally useful in modeling other molecular properties such as other types of chemical toxicity.

A composite model for HERG blockade

hERG blockade is one of the major toxicological problems in lead structure optimization. Reliable ligand-based in silico models for predicting hERG blockade therefore have considerable potential for saving time and money, as patch-clamp measurements are very expensive and no crystal structures of the hERG-encoded channel are available. Herein we present a predictive QSAR model for hERG blockade that differentiates between specific and nonspecific binding. Specific binders are identified by preliminary pharmacophore scanning. In addition to descriptor-based models for the compounds selected as hitting one of two different pharmacophores, we also use a model for nonspecific binding that reproduces blocking properties of molecules that do not fit either of the two pharmacophores. PLS and SVR models based on interpretable quantum mechanically derived descriptors on a literature dataset of 113 molecules reach overall R(2) values between 0.60 and 0.70 for independent validation sets and R(2) values between 0.39 and 0.76 after partitioning according to the pharmacophore search for the test sets. Our findings suggest that hERG blockade may occur through different types of binding, so that several different models may be necessary to assess hERG toxicity.

Strategies to reduce the risk of drug-induced QT interval prolongation: a pharmaceutical company perspective

Drug-induced prolongation of the QT interval is having a significant impact on the ability of the pharmaceutical industry to develop new drugs. The development implications for a compound causing a significant effect in the 'Thorough QT/QTc Study' -- as defined in the clinical regulatory guidance (ICH E14) -- are substantial. In view of this, and the fact that QT interval prolongation is linked to direct inhibition of the hERG channel, in the early stages of drug discovery the focus is on testing for and screening out hERG activity. This has led to understanding of how to produce low potency hERG blockers whilst retaining desirable properties. Despite this, a number of factors mean that when an integrated risk assessment is generated towards the end of the discovery phase (by conducting at least an in vivo QT assessment) a QT interval prolongation risk is still often apparent; inhibition of hERG channel trafficking and partitioning into cardiac tissue are just two confounding factors. However, emerging information suggests that hERG safety margins have high predictive value and that when hERG and in vivo non-clinical data are combined, their predictive value to man, whilst not perfect, is >80%. Although understanding the anomalies is important and is being addressed, of greater importance is developing a better understanding of TdP, with the aim of being able to predict TdP rather than using an imperfect surrogate marker (QT interval prolongation). Without an understanding of how to predict TdP risk, high-benefit drugs for serious indications may never be marketed.

Shape signatures: new descriptors for predicting cardiotoxicity in silico

Shape Signatures is a new computational tool that is being evaluated for applications in computational toxicology and drug discovery. The method employs a customized ray-tracing algorithm to explore the volume enclosed by the surface of a molecule and then uses the output to construct compact histograms (i.e., signatures) that encode for molecular shape and polarity. In the present study, we extend the application of the Shape Signatures methodology to the domain of computational models for cardiotoxicity. The Shape Signatures method is used to generate molecular descriptors that are then utilized with widely used classification techniques such as k nearest neighbors ( k-NN), support vector machines (SVM), and Kohonen self-organizing maps (SOM). The performances of these approaches were assessed by applying them to a data set of compounds with varying affinity toward the 5-HT(2B) receptor as well as a set of human ether-a-go-go-related gene (hERG) potassium channel inhibitors. Our classification models for 5-HT(2B) represented the first attempt at global computational models for this receptor and exhibited average accuracies in the range of 73-83%. This level of performance is comparable to using commercially available molecular descriptors. The overall accuracy of the hERG Shape Signatures-SVM models was 69-73%, in line with other computational models published to date. Our data indicate that Shape Signatures descriptors can be used with SVM and Kohonen SOM and perform better in classification problems related to the analysis of highly clustered and heterogeneous property spaces. Such models may have utility for predicting the potential for cardiotoxicity in drug discovery mediated by the 5-HT(2B) receptor and hERG.

Computer-aided prediction of QT-prolongation

Drug-induced cardiac arrhythmia is acknowledged as a serious obstacle in successful development of new drugs. Several methods for in silico prediction of acquired long QT syndrome (LQTS) caused by the pharmacological blockade of human hERG K+ channels are discussed in literature. We propose to use the computer program PASS, which estimates the probabilities of about 3000 biological activities, not only for prediction of hERG blockade and QT-prolongation but also for the analysis of indirect mechanisms of these actions. After addition in the PASS training set of 163 compounds with data on QT-Prolongation and re-training, it was shown that accuracy of prediction was 87.1% and 81.8% for hERG blockade and QT-prolongation, respectively. Using computer program PharmaExpert we found that in the predicted biological activity spectra there was a certain correlation between the hERG blockade and some other molecular mechanisms of action. Possible role of 1-phosphatidylinositol-4-phospate 5-kinase, dimethylargininase and progesterone 11 alpha-monooxygenase inhibition in hERG blockade was discussed.

Exploring QSTR and toxicophore of hERG K+ channel blockers using GFA and HypoGen techniques

Predictive quantitative structure-toxicity and toxicophore models were developed for a diverse series of hERG K+ channel blockers, acting as anti-arrhythmic agents using QSAR+ module in Cerius2 and HypoGen module in Catalyst software, respectively. The 2D-QSTR analysis has been performed on a dataset of 68 molecules carefully selected from literature for which IC50 values measured on hERG K+ channels expressed in mammalian cells lines using the voltage patch clamp assay technique were reported. Their biological data, expressed as IC50, spanned from 7.0nM to 1.4mM, with 7 orders difference. Several types of descriptors including electrotopological, thermodynamic, ADMET, graph theoretical (topological and information content) were used to derive a quantitative relationship between the channel blockers and its physico-chemical properties. Statistically significant QSTR model was obtained using genetic function approximation methodology, having seven descriptors, with a correlation coefficient (r2) of 0.837, cross-validated correlation coefficient (q2) of 0.776 and predictive correlation coefficient (r2 pred) of 0.701, indicating the robustness of the model. Toxicophore model generated using HypoGen module in Catalyst, on these datasets, showed three important features for hERG K+ channel blockers, (i) hydrophobic group (HP), (ii) ring aromatic group (RA) and (iii) hydrogen bond acceptor lipid group (HBAl). The most predictive hypothesis (Hypo 1), consisting of these three features had a best correlation coefficient of 0.820, a low rms deviation of 1.740, and a high cost difference of 113.50, which represents a true correlation and a good predictivity. The hypothesis, Hypo 1 was validated by a test set consisting of 12 molecules and by a cross-validation of 95% confidence level. Accordingly, our 2D-QSTR and toxicophore model has strong predictivity to identify structurally diverse hERG K+ channel blockers with desired biological activity. These models provide a useful framework for understanding binding, and gave structural insight into the specific protein-ligand interactions responsible for affinity, and how one might modify any given structure to mitigate binding.

Estimation of hERG inhibition of drug candidates using multivariate property and pharmacophore SAR

We describe the development of a computational model for the prediction of the inhibition of K(+) flow through the hERG ion channel. Using a collection of 1075 discovery compounds with hERG inhibition measured in our standard patch-clamp electrophysiology assay, molecular features important for drug-induced inhibition were identified using a combination of statistical inference algorithms and manual hypothesis generation and testing. While many of the features used in the model reflect those referenced in the literature, several aspects of the model provide new insight into the role of physicochemical properties, electrostatics, and novel pharmacophores in hERG inhibition. Coefficients for these 10 features were then determined by least median squares regression, resulting in a model with an R(2) approximately 0.66 and RMS error (RMSe) of 0.47 log units for an external test set. Significant additional validation performed using a large collection of subsequent discovery data has been very encouraging with an R(2)=0.54 and an RMSe of 0.63 log units. The performance of the model across several different chemotypes is demonstrated and discussed.

Understanding hERG inhibition with QSAR models based on a one-dimensional molecular representation

Blockage of the potassium channel encoded by the human ether-a-go-go related gene (hERG) is well understood to be the root cause of the cardio-toxicity of numerous approved and investigational drugs. As such, a cascade of in vitro and in vivo assays have been developed to filter compounds with hERG inhibitory activity. Quantitative structure activity relationship (QSAR) models are used at the very earliest part of this cascade to eliminate compounds that are likely to have this undesirable activity prior to synthesis. Here a new QSAR technique based on the one-dimensional representation is described in the context of the development of a model to predict hERG inhibition. The model is shown to perform close to the limits of the quality of the data used for model building. In order to make optimal use of the available data, a general robust mathematical scheme was developed and is described to simultaneously incorporate quantitative data, such as IC50 = 50 nM, and qualitative data, such as inactive or IC50 > 30 microM into QSAR models without discarding any experimental information.

Development, interpretation and temporal evaluation of a global QSAR of hERG electrophysiology screening data

A 'global' model of hERG K(+) channel was built to satisfy three basic criteria for QSAR models in drug discovery: (1) assessment of the applicability domain, (2) assuring that model decisions can be interpreted by medicinal chemists and (3) assessment of model performance after the model was built. A combination of D-optimal onion design and hierarchical partial least squares modelling was applied to construct a global model of hERG blockade in order to maximize the applicability domain of the model and to enhance its interpretability. Additionally, easily interpretable hERG specific fragment-based descriptors were developed. Model performance was monitored, throughout a time period of 15 months, after model implementation. It was found that after this time duration a greater proportion of molecules were outside the model's applicability domain and that these compounds had a markedly higher average prediction error than those from molecules within the model's applicability domain. The model's predictive performance deteriorated within 4 months after building, illustrating the necessity of regular updating of global models within a drug discovery environment.

Common pharmacophores for uncharged human ether-a-go-go-related gene (hERG) blockers

In silico approaches are widely used to predict human ether-a-go-go-related gene (hERG) channel blockade. Published pharmacophore models of hERG blockers typically contain a basic nitrogen center flanked by aromatic or hydrophobic groups. However, hERG blockade has been observed in series lacking the basic nitrogen. By utilizing screening data for 194 potent uncharged hERG actives, we propose a pharmacophore for neutral hERG blockers, and provide guidance on eliminating hERG liability in an uncharged hERG active chemical series.

Optimisation and validation of a medium-throughput electrophysiology-based hERG assay using IonWorks™ HT

INTRODUCTION

Regulatory and competitive pressure to reduce the QT interval prolongation risk of potential new drugs has led to focus on methods to test for inhibition of the human ether-a-go-go-related gene (hERG)-encoded K+ channel, the primary molecular target underlying this safety issue. Here we describe the validation of a method that combines medium-throughput with direct assessment of channel function.

METHODS

The electrophysiological and pharmacological properties of hERG were compared using two methods: conventional, low-throughput electrophysiology and planar-array-based, medium-throughput electrophysiology (IonWorks HT). A pharmacological comparison was also made between IonWorks HT and an indirect assay (Rb+ efflux).

RESULTS

Basic electrophysiological properties of hERG were similar whether recorded conventionally (HEK cells) or using IonWorks HT (CHO cells): for example, tail current V1/2 -12.1+/-5.0 mV (32) for conventional and -9.5+/-6.0 mV (46) for IonWorks HT (mean+/-S.D. (n)). A key finding was that as the number of cells per well was increased in IonWorks HT, the potency reported for a given compound decreased. Using the lowest possible cell concentration (250,000 cells/ml) and 89 compounds spanning a broad potency range, the pIC50 values from IonWorks HT (CHO-hERG) were found to correlate well with those obtained using conventional methodology (HEK-hERG)(r=0.90; p<0.001). Further validation using CHO-hERG cells with both methods confirmed the correlation (r=0.94; p<0.001). In contrast, a comparison of IonWorks HT and Rb+ efflux data with 649 compounds using CHO-hERG cells showed that the indirect assay consistently reported compounds as being, on average, 6-fold less potent, though the differences varied depending on chemical series.

DISCUSSION

The main finding of this work is that providing a relatively low cell concentration is used in IonWorks HT, the potency information generated correlates well with that determined using conventional electrophysiology. The effect on potency of increasing cell concentration may relate to a reduced free concentration of test compound owing to partitioning into cell membranes. In summary, the IonWorks HT hERG assay can generate pIC50 values based on a direct assessment of channel function in a timeframe short enough to influence chemical design.

Insights for Human Ether-a-Go-Go-Related Gene Potassium Channel Inhibition Using Recursive Partitioning and Kohonen and Sammon Mapping Techniques

The human ether-a-go-go related gene (hERG) can be inhibited by marketed drugs, and this inhibition may lead to QT prolongation and possibly fatal cardiac arrhythmia. We have collated literature data for 99 diverse hERG inhibitors to generate Kohonen maps, Sammon maps, and recursive partitioning models. Our aim was to investigate whether these computational models could be used either individually or together in a consensus approach to predict the binding of a prospectively selected test set of 35 diverse molecules and at the same time to offer further insights into hERG inhibition. The recursive partitioning model provided a quantitative prediction, which was markedly improved when Tanimoto similarity was included as a filter to remove molecules from the test set that were too dissimilar to the training set (r2 = 0.83, Spearman rho = 0.75, p = 0.0003 for the 18 remaining molecules, >0.77 similarity). This model was also used to screen and prioritize a database of drugs, recovering several hERG inhibitors not used in model building. The mapping approaches used molecular descriptors required for hERG inhibition that were not reported previously and in particular highlighted the importance of molecular shape. The Sammon map model provided the best qualitative classification of the test set (95% correct) compared with the Kohonen map model (81% correct), and this result was also superior to the consensus approach. This study illustrates that patch clamping data from various literature sources can be combined to generate valid models of hERG inhibition for prospective predictions.

Determination of hERG channel blockers using a decision tree

A decision tree approach for the in silico prediction of Torsade de Pointes (TdP)-causing drugs is presented. As TdP is frequently associated with QT-interval prolongation due to inhibition of the rapid activating delayed rectifier potassium channel in the heart (hERG channel), the properties of such blockers were investigated by molecular modeling and semi-empirical AM1 molecular orbital calculations. In addition, we derived a pharmacophoric SMARTS string using structural information from high affinity compounds. A corresponding search in the PubChem database identified several compounds that exhibit QT-interval prolonging activity that were not among our data set. This SMARTS string furthermore showed to be the most significant descriptor in the decision tree approach from which guidelines for the design of safe compounds are suggested.

Screening the receptorome

The term 'receptorome' is now being used to describe receptors, ion channels and transporters in the human genome that are potential drug targets. These proteins comprise a considerable fraction of the human genome, and include the G protein-coupled receptors, which are the targets for many medications. In this review, we summarize recent advances in the field, including the concept that the ultimate goal of drug discovery may not be the development of highly selective single-target drugs, the idea that potential side-effects can also be the goal of multi-target drug screening, and a discussion of the application of computational screening and public domain databases available to interested investigators.

In Silico Classification of hERG Channel Blockers: a Knowledge-Based Strategy

The blockage of the hERG potassium channel by a wide number of diverse compounds has become a major pharmacological safety concern as it can lead to sudden cardiac death. In silico models can be potent tools to screen out potential hERG blockers as early as possible during the drug-discovery process. In this study, predictive models developed using the recursive partitioning method and created using diverse datasets from 203 molecules tested on the hERG channel are described. The first model was built with hERG compounds grouped into two classes, with a separation limit set at an IC50 value of 1 microm, and reaches an overall accuracy of 81%. The misclassification of molecules having a range of activity between 1 and 10 microM led to the generation of a tri-class model able to correctly classify high, moderate, and weak hERG blockers with an overall accuracy of 90%. Another model, constructed with the high and weak hERG-blocker categories, successfully increases the accuracy to 96%. The results reported herein indicate that a combination of precise, knowledge management resources and powerful modeling tools are invaluable to assessing potential cardiotoxic side effects related to hERG blockage.

New insights about HERG blockade obtained from protein modeling, potential energy mapping, and docking studies

We created a homology model of the homo-tetrameric pore domain of HERG using the crystal structure of the bacterial potassium channel, KvAP, as a template. We docked a set of known blockers with well-characterized effects on channel function into the lumen of the pore between the selectivity filter and extracellular entrance using a novel docking and refinement procedure incorporating Glide and Prime. Key aromatic groups of the blockers are predicted to form multiple simultaneous ring stacking and hydrophobic interactions among the eight aromatic residues lining the pore. Furthermore, each blocker can achieve these interactions via multiple docking configurations. To further interpret the docking results, we mapped hydrophobic and hydrophilic potentials within the lumen of each refined docked complex. Hydrophilic iso-potential contours define a 'propeller-shaped' volume at the selectivity filter entrance. Hydrophobic contours define a hollow 'crown-shaped' volume located above the 'propeller', whose hydrophobic 'rim' extends along the pore axis between Tyr652 and Phe656. Blockers adopt conformations/binding orientations that closely mimic the shapes and properties of these contours. Blocker basic groups are localized in the hydrophilic 'propeller', forming electrostatic interactions with Ser624 rather than a generally accepted pi-cation interaction with Tyr652. Terfenadine, cisapride, sertindole, ibutilide, and clofilium adopt similar docked poses, in which their N-substituents bridge radially across the hollow interior of the 'crown' (analogous to the hub and spokes of a wheel), and project aromatic/hydrophobic portions into the hydrophobic 'rim'. MK-499 docks with its longitudinal axis parallel to the axis of the pore and 'crown', and its hydrophobic groups buried within the hydrophobic 'rim'.

A QSAR Model of hERG Binding Using a Large, Diverse, and Internally Consistent Training Set

Over the past decade, the pharmaceutical industry has begun to address an addition to ADME/Tox profiling--the ability of a compound to bind to and inhibit the human ether-a-go-go-related gene (hERG)-encoded cardiac potassium channel. With the compilation of a large and diverse set of compounds measured in a single, consistent hERG channel inhibition assay, we recognized a unique opportunity to attempt to construct predictive QSAR models. Early efforts with classification models built from this training set were very encouraging. Here, we report a systematic evaluation of regression models based on neural network ensembles in conjunction with a variety of structure representations and feature selection algorithms. The combination of these modeling techniques (neural networks to capture non-linear relationships in the data, feature selection to prevent over-fitting, and aggregation to minimize model instability) was found to produce models with very good internal cross-validation statistics and good predictivity on external data.

Development and Evaluation of an in Silico Model for hERG Binding

It has been recognized that drug-induced QT prolongation is related to blockage of the human ether-a-go-go-related gene (hERG) ion channel. Therefore, it is prudent to evaluate the hERG binding of active compounds in early stages of drug discovery. In silico approaches provide an economic and quick method to screen for potential hERG liability. A diverse set of 90 compounds with hERG IC(50) inhibition data was collected from literature references. Fragment-based QSAR descriptors and three different statistical methods, support vector regression, partial least squares, and random forests, were employed to construct QSAR models for hERG binding affinity. Important fragment descriptors relevant to hERG binding affinity were identified through an efficient feature selection method based on sparse linear support vector regression. The support vector regression predictive model built upon selected fragment descriptors outperforms the other two statistical methods in this study, resulting in an r(2) of 0.912 and 0.848 for the training and testing data sets, respectively. The support vector regression model was applied to predict hERG binding affinities of 20 in-house compounds belonging to three different series. The model predicted the relative binding affinity well for two out of three compound series. The hierarchical clustering and dendrogram results show that the compound series with the best prediction has much higher structural similarity and more neighbors of training compounds than the other two compound series, demonstrating the predictive scope of the model. The combination of a QSAR model and postprocessing analysis, such as clustering and visualization, provides a way to assess the confidence level of QSAR prediction results on the basis of similarity to the training set.

SitePrint: three-dimensional pharmacophore descriptors derived from protein binding sites for family based active site analysis, classification, and drug design

Integrating biological and chemical information is one key task in drug discovery, and one approach to attaining this goal is via three-dimensional pharmacophore descriptors derived from protein binding sites. The SitePrint program generates, aligns, scores, and classifies three-dimensional pharmacophore descriptors, active site grids, and ligand surfaces. The descriptors are formed from molecular fragments that have been docked, minimized, filtered, and clustered in protein active sites. The descriptors have geometric coordinates derived from the fragment positions, and they capture the shape, electrostatics, locations, and angles of entry into pockets of the recognition sites: they also provide a direct link to databases of organic molecules. The descriptors have been shown to be robust with respect to small changes in protein structure observed when multiple compounds are cocrystallized in a protein. Five aligned thrombin cocrystals with an average core alpha-carbon RMSD of 0.7 A gave three-dimensional pharmacophore descriptors with an average RMSD of 1.1 A. On a larger test set, alignment and scoring of the descriptors using clique-based alignment, and a best first search strategy with an adapted forward-looking Ullmann heuristic was able to select the global minimum three-dimensional alignment in twenty-nine out of thirty cases in less than one CPU second on a workstation. A protein family based analysis was then performed to demonstrate the usefulness of the method in producing a correlation of active site pharmacophore descriptors to protein function. Each protein in a test set of thirty was assigned membership to a family based on computed active site similarity to the following families: kinases, nuclear receptors, the aspartyl, cysteine, serine, and metallo proteases. This method of classifying proteins is complementary to approaches based on sequence or fold homology. The values within protein families for correctly assigning membership of a protein to a family ranged from 25% to 80%.

Predictive models for hERG potassium channel blockers

We report here a general method for the prediction of hERG potassium channel blockers using computational models generated from correlation analyses of a large dataset and pharmacophore-based GRIND descriptors. These 3D-QSAR models are compared favorably with other traditional and chemometric based HQSAR methods.

A discriminant model constructed by the support vector machine method for HERG potassium channel inhibitors

HERG attracts attention as a risk factor for arrhythmia, which might trigger torsade de pointes. A highly accurate classifier of chemical compounds for inhibition of the HERG potassium channel is constructed using support vector machine. For two test sets, our discriminant models achieved 90% and 95% accuracy, respectively. The classifier is even applied for the prediction of cardio vascular adverse effects to achieve about 70% accuracy. While modest inhibitors are partly characterized by properties linked to global structure of a molecule including hydrophobicity and diameter, strong inhibitors are exclusively characterized by properties linked to substructures of a molecule.

A QSAR toxicity study of a series of alkaloids with the lycoctonine skeleton

A QSAR toxicity analysis has been performed for a series of 19 alkaloids with the lycoctonine skeleton. GA-MLRA (Genetic Algorithm combined with Multiple Linear Regression Analysis) technique was applied for the generation of two types of QSARs: first, models containing exclusively 3D-descriptors and second, models consisting of physicochemical descriptors. As expected, 3D-descriptor QSARs have better statistical fits. Physicochemical-descriptor containing models, that are in a good agreement with the mode of toxic action exerted by the alkaloids studied, have also been identified and discussed. In particular, TPSA (Topological Polar Surface Area) and nC=O (number of -C(O)- fragments) parameters give the best statistically significant mono- and bidescriptor models (when combined with lipophilicity, MlogP) confirming the importance of H-bonding capability of the alkaloids for binding at the receptor site.