Similarity-based SIBAR descriptors for classification of chemically diverse hERG blockers

There is an increasing interest in computational models for the classification and prediction of the human ether-a-go-go-related-gene (hERG) potassium channel affinity in the early phase of drug discovery and development. In this study, similarity-based SIBAR descriptors were applied in order to develop and validate in silico binary QSAR and counter-propagation neural network models for the classification of hERG activity. The SIBAR descriptors were calculated based on four reference datasets using four sets of 2D- and 3D-descriptors including 3D-grid-based VolSurf, 3D ‘inductive’ QSAR, Van der Waals surface area (P_VSA) and a set of 11 hERG relevant 2D descriptors devised from feature selection methods. The results indicate that the reference data set tailored to the specific problem, together with a set of hERG relevant descriptors, provides highly predictive models.

[1]  Antranig Basman,et al.  HERG binding specificity and binding site structure: evidence from a fragment-based evolutionary computing SAR study. , 2004, Progress in biophysics and molecular biology.

[2]  François Petitet,et al.  In Silico Classification of hERG Channel Blockers: a Knowledge‐Based Strategy , 2006, ChemMedChem.

[3]  J. Gasteiger,et al.  Chemoinformatics: A Textbook , 2003 .

[4]  Marvin Johnson,et al.  Concepts and applications of molecular similarity , 1990 .

[5]  T. Lundstedt,et al.  Presentation of a structurally diverse and commercially available drug data set for correlation and benchmarking studies. , 2006, Journal of medicinal chemistry.

[6]  W. Gansterer,et al.  Predictive QSAR Models for Polyspecific Drug Targets: The Importance of Feature Selection , 2008 .

[7]  B Testa,et al.  Predicting blood-brain barrier permeation from three-dimensional molecular structure. , 2000, Journal of medicinal chemistry.

[8]  Hongmao Sun,et al.  Support vector machines classification of hERG liabilities based on atom types. , 2008, Bioorganic & medicinal chemistry.

[9]  Johann Gasteiger,et al.  Neural networks in chemistry and drug design , 1999 .

[10]  Barbara Zdrazil,et al.  Similarity-based descriptors (SIBAR) – A tool for safe exchange of chemical information? , 2005, J. Comput. Aided Mol. Des..

[11]  Brian B. Goldman,et al.  A model for identifying HERG K+ channel blockers. , 2004, Bioorganic & medicinal chemistry.

[12]  Rosella Ombrato,et al.  General and independent approaches to predict HERG affinity values , 2004 .

[13]  A. Cherkasov.  Inductive Descriptors: 10 Successful Years in QSAR , 2005 .

[14]  Paul Labute,et al.  Binary QSAR: A New Method for the Determination of Quantitative Structure Activity Relationships , 1998, Pacific Symposium on Biocomputing.

[15]  Emilio Benfenati,et al.  Classification of Potential Endocrine Disrupters on the Basis of Molecular Structure Using a Nonlinear Modeling Method , 2004, J. Chem. Inf. Model..

[16]  W G Richards,et al.  Molecular similarity, quantitative chirality, and QSAR for chiral drugs. , 1994, Journal of medicinal chemistry.

[17]  Tudor I. Oprea,et al.  hERG classification model based on a combination of support vector machine method and GRIND descriptors. , 2008, Molecular pharmaceutics.

[18]  Alex M Aronov,et al.  Predictive in silico modeling for hERG channel blockers. , 2005, Drug discovery today.

[19]  Hongmao Sun,et al.  An Accurate and Interpretable Bayesian Classification Model for Prediction of hERG Liability , 2006, ChemMedChem.

[20]  T. Nishikawa,et al.  A discriminant model constructed by the support vector machine method for HERG potassium channel inhibitors. , 2005, Bioorganic & medicinal chemistry letters.

[21]  Gisbert Schneider,et al.  A Virtual Screening Method for Prediction of the hERG Potassium Channel Liability of Compound Libraries , 2002, Chembiochem : a European journal of chemical biology.

[22]  G. Ecker,et al.  A binary QSAR model for classification of hERG potassium channel blockers. , 2008, Bioorganic & medicinal chemistry.

[23]  Gerhard F. Ecker,et al.  Classification Models for hERG Inhibitors by Counter‐Propagation Neural Networks , 2008, Chemical biology & drug design.

[24]  P. Labute.  A widely applicable set of descriptors. , 2000, Journal of molecular graphics & modelling.

[25]  S. Ekins.  Predicting undesirable drug interactions with promiscuous proteins in silico. , 2004, Drug discovery today.

[26]  Tudor I. Oprea,et al.  Chemography: the Art of Navigating in Chemical Space , 2000 .

[27]  Sean Ekins,et al.  Shape signatures: new descriptors for predicting cardiotoxicity in silico. , 2008, Chemical research in toxicology.

[28]  S. O'Brien,et al.  Greater than the sum of its parts: combining models for useful ADMET prediction. , 2005, Journal of medicinal chemistry.

[29]  Tudor I. Oprea,et al.  Pharmacokinetically based mapping device for chemical space navigation. , 2002, Journal of combinatorial chemistry.

[30]  H. Kubinyi,et al.  Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices. , 1998, Journal of medicinal chemistry.

[31]  Gerhard F Ecker,et al.  Predictive models for HERG channel blockers: ligand-based and structure-based approaches. , 2007, Current medicinal chemistry.

[32]  P. J. Green,et al.  Probability and Statistical Inference , 1978 .

[33]  Stephan Kopp,et al.  Similarity based SAR (SIBAR) as tool for early ADME profiling , 2002, J. Comput. Aided Mol. Des..

[34]  M. Kendall.  Probability and Statistical Inference , 1956, Nature.

[35]  M. Sanguinetti,et al.  hERG potassium channels and cardiac arrhythmia , 2006, Nature.

[36]  G. Keserü.  Prediction of hERG potassium channel affinity by traditional and hologram qSAR methods. , 2003, Bioorganic & medicinal chemistry letters.

[37]  G. Ecker,et al.  The Similarity Principle - New Trends and Applications in Ligand-Based Drug Discovery and ADMET Profiling , 2008 .

[38]  Barbara Zdrazil,et al.  Similarity‐Based Descriptors (SIBAR) as Tool for QSAR Studies on P‐Glycoprotein Inhibitors: Influence of the Reference Set , 2007 .

[39]  B. Fermini,et al.  The impact of drug-induced QT interval prolongation on drug discovery and development , 2003, Nature Reviews Drug Discovery.

[40]  Ying Xue,et al.  Identifying hERG Potassium Channel Inhibitors by Machine Learning Methods , 2008 .

[41]  Artem Cherkasov,et al.  Successful in silico discovery of novel nonsteroidal ligands for human sex hormone binding globulin. , 2005, Journal of medicinal chemistry.

[42]  U. Burkard.  Methods for Data Analysis , 2004 .

[43]  Marjan Vracko,et al.  Kohonen Artificial Neural Network and Counter Propagation Neural Network in Molecular Structure-Toxicity Studies , 2005 .

[44]  Sean Ekins,et al.  Insights for human ether-a-go-go-related gene potassium channel inhibition using recursive partitioning and Kohonen and Sammon mapping techniques. , 2006, Journal of medicinal chemistry.

[45]  Gerhard F. Ecker,et al.  Computational models for prediction of interactions with ABC-transporters. , 2008, Drug discovery today.

[46]  Alex M Aronov,et al.  Tuning out of hERG. , 2008, Current opinion in drug discovery & development.

[47]  Harry J Witchel,et al.  The hERG potassium channel as a therapeutic target , 2007, Expert opinion on therapeutic targets.

[48]  A. Cavalli,et al.  QT prolongation through hERG K+ channel blockade: Current knowledge and strategies for the early prediction during drug development , 2005, Medicinal research reviews.