Quantitative Series Enrichment Analysis (QSEA): a novel procedure for 3D-QSAR analysis

A novel procedure is proposed for 3D-QSAR analysis. The composition of 16 published QSAR datasets has been examined using Quantitative Series Enrichment Analysis (QSEA). The procedure is based on topomer technologies. A heatmap display in combination with topomer CoMFA and a novel series trajectory analysis revealed critical information for the assembly of structures into meaningful series. Global and local centroid structures can be determined from a similarity distance matrix and build the origins for stepwise model building by increasing the similarity radius around the centroid nucleus. The results indicate that the new procedure allows determination of whether compounds belong to an emerging structure-activity relationship and which compounds can be predicted within reliable limits.

[1]  J. Hulten,et al.  Synthesis and comparative molecular field analysis (CoMFA) of symmetric and nonsymmetric cyclic sulfamide HIV-1 protease inhibitors. , 2001, Journal of medicinal chemistry.

[2]  V. Kulkarni,et al.  Three-dimensional quantitative structure-activity relationship of interleukin 1-beta converting enzyme inhibitors: A comparative molecular field analysis study. , 1999, Journal of medicinal chemistry.

[3]  D. E. Nichols,et al.  Quantitative structure-activity relationship modeling of dopamine D(1) antagonists using comparative molecular field analysis, genetic algorithms-partial least-squares, and K nearest neighbor methods. , 1999, Journal of medicinal chemistry.

[4]  Jana Sopkova-de Oliveira Santos,et al.  Association of Two 3D QSAR Analyses. Application to the Study of Partial Agonist Serotonin-3 Ligands , 2001, J. Chem. Inf. Comput. Sci..

[5]  P J Goodford,et al.  Physicochemical-activity relationship in practice. 2. Rational selection of benzenoid substituents. , 1975, Journal of medicinal chemistry.

[6]  Peter C. Jurs,et al.  Development of Quantitative Structure-Activity Relationship and Classification Models for a Set of Carbonic Anhydrase Inhibitors , 2002, J. Chem. Inf. Comput. Sci..

[7]  Keith Abe,et al.  Identification of orally active, potent, and selective 4-piperazinylquinazolines as antagonists of the platelet-derived growth factor receptor tyrosine kinase family. , 2002, Journal of medicinal chemistry.

[8]  Yu Chen,et al.  Evaluation of Quantitative Structure-Activity Relationship Methods for Large-Scale Prediction of Chemicals Binding to the Estrogen Receptor , 1998, J. Chem. Inf. Comput. Sci..

[9]  G Klebe,et al.  Three-dimensional quantitative structure-activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa. , 1999, Journal of medicinal chemistry.

[10]  Alan B. Forsythe,et al.  Strategy in drug design. Cluster anlysis as an aid in the selection of substituents , 1973 .

[11]  Hans-Dieter Höltje,et al.  A molecular graphics study on structure-action relationships of calcium-antagonistic and agonistic 1,4-dihydropyridines , 1987, J. Comput. Aided Mol. Des..

[12]  P. Khadikar,et al.  Topological designing of 4-piperazinylquinazolines as antagonists of PDGFR tyrosine kinase family. , 2003, Bioorganic & medicinal chemistry letters.

[13]  C. Supuran,et al.  Carbonic anhydrase inhibitors: perfluoroalkyl/aryl-substituted derivatives of aromatic/heterocyclic sulfonamides as topical intraocular pressure-lowering agents with prolonged duration of action. , 2000, Journal of medicinal chemistry.

[14]  Bernd Wendt,et al.  Pushing the boundaries of 3D-QSAR , 2007, J. Comput. Aided Mol. Des..

[15]  G. Pei,et al.  3D-QSAR model of flavonoids binding at benzodiazepine site in GABAA receptors. , 2001, Journal of medicinal chemistry.

[16]  D. Livingstone,et al.  Structure-activity relationships of antifilarial antimycin analogues: a multivariate pattern recognition study. , 1990, Journal of medicinal chemistry.

[17]  N. Stiefl,et al.  Mapping property distributions of molecular surfaces: algorithm and evaluation of a novel 3D quantitative structure-activity relationship technique. , 2003, Journal of medicinal chemistry.

[18]  L. Hall,et al.  Molecular connectivity in chemistry and drug research , 1976 .

[19]  J. Linden,et al.  Design, synthesis, and evaluation of novel A2A adenosine receptor agonists. , 2001, Journal of medicinal chemistry.

[20]  W. Richards,et al.  Self-organizing molecular field analysis: a tool for structure-activity studies. , 1999, Journal of medicinal chemistry.

[21]  M Karplus,et al.  Evolutionary optimization in quantitative structure-activity relationship: an application of genetic neural networks. , 1996, Journal of medicinal chemistry.

[22]  B Testa,et al.  Inhibition of monoamine oxidases by functionalized coumarin derivatives: biological activities, QSARs, and 3D-QSARs. , 2000, Journal of medicinal chemistry.

[23]  Supa Hannongbua,et al.  3D-Quantitative Structure-Activity Relationships of HEPT Derivatives as HIV-1 Reverse Transcriptase Inhibitors, Based on Ab Initio Calculations , 2001, J. Chem. Inf. Comput. Sci..

[24]  Robert J. Jilek,et al.  Topomers: A Validated Protocol for Their Self-Consistent Generation , 2004, J. Chem. Inf. Model..

[25]  Angelo Carotti,et al.  QSAR and QSPR Studies of a Highly Structured Physicochemical Domain , 2006, J. Chem. Inf. Model..

[26]  H. Kubinyi Variable Selection in QSAR Studies. II. A Highly Efficient Combination of Systematic Search and Evolution , 1994 .

[27]  Rajarshi Guha,et al.  Development of Linear, Ensemble, and Nonlinear Models for the Prediction and Interpretation of the Biological Activity of a Set of PDGFR Inhibitors , 2004, J. Chem. Inf. Model..

[28]  Anton J. Hopfinger,et al.  Application of Genetic Function Approximation to Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships , 1994, J. Chem. Inf. Comput. Sci..

[29]  Robert J. Jilek,et al.  "Lead hopping". Validation of topomer similarity as a superior predictor of similar biological activities. , 2004, Journal of medicinal chemistry.

[30]  Alexander Tropsha,et al.  Application of validated QSAR models of D1 dopaminergic antagonists for database mining. , 2005, Journal of medicinal chemistry.

[31]  Glen Eugene Kellogg,et al.  HINT: A new method of empirical hydrophobic field calculation for CoMFA , 1991, J. Comput. Aided Mol. Des..

[32]  I V Tetko,et al.  Volume learning algorithm artificial neural networks for 3D QSAR studies. , 2001, Journal of medicinal chemistry.

[33]  G. Klebe,et al.  Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. , 1994, Journal of medicinal chemistry.

[34]  R. Cramer,et al.  Topomer CoMFA: a design methodology for rapid lead optimization. , 2003, Journal of medicinal chemistry.

[35]  Robert D Clark,et al.  Bioisosterism as a molecular diversity descriptor: steric fields of single "topomeric" conformers. , 1996, Journal of medicinal chemistry.

[36]  Yvonne C. Martin,et al.  A fast new approach to pharmacophore mapping and its application to dopaminergic and benzodiazepine agonists , 1993, J. Comput. Aided Mol. Des..

[37]  Hxugo Kubiny Variable Selection in QSAR Studies. I. An Evolutionary Algorithm , 1994 .

[38]  Robert D Clark,et al.  Neighborhood behavior: a useful concept for validation of "molecular diversity" descriptors. , 1996, Journal of medicinal chemistry.

[39]  R. Cramer,et al.  Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. , 1988, Journal of the American Chemical Society.

[40]  A. N. Jain,et al.  Compass: predicting biological activities from molecular surface properties. Performance comparisons on a steroid benchmark. , 1994, Journal of medicinal chemistry.

[41]  Wesley Schaal,et al.  Improved CoMFA Modeling by Optimization of Settings , 2006, J. Chem. Inf. Model..

[42]  Svante Wold,et al.  Pattern recognition by means of disjoint principal components models , 1976, Pattern Recognit..

[43]  Stefan H. Unger,et al.  Model building in structure-activity relations. Reexamination of adrenergic blocking activity of .beta.-halo-.beta.-arylalkylamines , 1973 .

[44]  Garland R. Marshall,et al.  3D-QSAR of angiotensin-converting enzyme and thermolysin inhibitors: A comparison of CoMFA models based on deduced and experimentally determined active site geometries , 1993 .

[45]  Richard A. Lewis A general method for exploiting QSAR models in lead optimization. , 2005, Journal of medicinal chemistry.

[46]  H. Lanig,et al.  Comparative molecular field analysis of dopamine D4 receptor antagonists including 3-[4-(4-chlorophenyl)piperazin-1-ylmethyl]pyrazolo[1,5-a]pyridine (FAUC 113), 3-[4-(4-chlorophenyl)piperazin-1-ylmethyl]-1H-pyrrolo-[2,3-b]pyridine (L-745,870), and clozapine. , 2001, Journal of medicinal chemistry.

[47]  Wolfgang Sippl,et al.  Structure-based 3D QSAR and design of novel acetylcholinesterase inhibitors , 2001, J. Comput. Aided Mol. Des..