QSAR studies on 1-phenylbenzimidazoles as inhibitors of the platelet-derived growth factor.

This work is devoted to the development of quantitative structure-activity relationship (QSAR) models of the biological activity of 123 1-phenylbenzimidazoles as inhibitors of the PDGF receptor. The molecular features are represented by chemical descriptors that have been calculated on geometrical, topological, quantum mechanical, and electronic basis by using CODESSA PRO. The obtained models, linear (multilinear regression) and nonlinear (artificial neural network), are aimed to link the structures to their reported activity log 1/IC50. The former model can be used for physico-chemical interpretation, while the latter possesses a superior predictive ability.

[1]  Jian-hui Jiang,et al.  Quantitative structure-activity relationships (QSAR): studies of inhibitors of tyrosine kinase. , 2003, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[2]  W. Denny,et al.  Structure-activity relationships for 5-substituted 1-phenylbenzimidazoles as selective inhibitors of the platelet-derived growth factor receptor. , 1999, Journal of medicinal chemistry.

[3]  Johann Gasteiger,et al.  Neural Networks for Chemists: An Introduction , 1993 .

[4]  Tingjun Hou,et al.  3D QSAR Analyses of Novel Tyrosine Kinase Inhibitors Based on Pharmacophore Alignment , 2001, J. Chem. Inf. Comput. Sci..

[5]  C. Heldin,et al.  Selective platelet-derived growth factor receptor kinase blockers reverse sis-transformation. , 1994, Cancer research.

[6]  M. Karelson Molecular descriptors in QSAR/QSPR , 2000 .

[7]  L. Claesson-Welsh Mechanism of action of platelet-derived growth factor. , 1996, The international journal of biochemistry & cell biology.

[8]  Chongli Zhong,et al.  A QSAR study on inhibitory activities of 1-phenylbenzimidazoles against the platelet-derived growth factor receptor. , 2004, Bioorganic & medicinal chemistry.

[9]  C. Hansch,et al.  Comparative QSAR study of tyrosine kinase inhibitors. , 2001, Chemical reviews.

[10]  N. Lydon,et al.  Selective inhibition of the platelet-derived growth factor signal transduction pathway by a protein-tyrosine kinase inhibitor of the 2-phenylaminopyrimidine class. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Peter Traxler,et al.  Phenylamino-pyrimidine (PAP) — derivatives: a new class of potent and highly selective PDGF-receptor autophosphorylation inhibitors , 1996 .

[12]  W. Meyer-Ingold,et al.  Platelet‐derived growth factor. , 1995, Cell biology international.

[13]  M. Karelson,et al.  QSPR as a means of predicting and understanding chemical and physical properties in terms of structure , 1997 .

[14]  George M. Whitesides,et al.  FEED-FORWARD NEURAL NETWORKS IN CHEMISTRY : MATHEMATICAL SYSTEMS FOR CLASSIFICATION AND PATTERN RECOGNITION , 1993 .

[15]  Alan R. Katritzky,et al.  QSPR and QSAR Models Derived Using Large Molecular Descriptor Spaces. A Review of CODESSA Applications , 1999 .

[16]  W A Denny,et al.  Structure-activity relationships for 1-phenylbenzimidazoles as selective ATP site inhibitors of the platelet-derived growth factor receptor. , 1998, Journal of medicinal chemistry.

[17]  A. Zilberstein,et al.  A new series of PDGF receptor tyrosine kinase inhibitors: 3-substituted quinoline derivatives. , 1994, Journal of medicinal chemistry.

[18]  Takahiro Suzuki,et al.  Quantitative Structure-Property Relationships for the Estimation of Boiling Point and Flash Point Using a Radial Basis Function Neural Network , 1999, J. Chem. Inf. Comput. Sci..

[19]  R. Dolle,et al.  5,7-Dimethoxy-3-(4-pyridinyl)quinoline is a potent and selective inhibitor of human vascular beta-type platelet-derived growth factor receptor tyrosine kinase. , 1994, Journal of medicinal chemistry.

[20]  Eamonn F. Healy,et al.  Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model , 1985 .

[21]  Dan C. Fara,et al.  Quantitative Structure-Property Relationship Modeling of beta-Cyclodextrin Complexation Free Energies , 2004, J. Chem. Inf. Model..

[22]  M. Karelson,et al.  Correlation of Boiling Points with Molecular Structure. 1. A Training Set of 298 Diverse Organics and a Test Set of 9 Simple Inorganics , 1996 .

[23]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[24]  S. Agatonovic-Kustrin,et al.  Molecular descriptors that influence the amount of drugs transfer into human breast milk. , 2002, Journal of pharmaceutical and biomedical analysis.

[25]  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..

[26]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[27]  Uko Maran,et al.  The present utility and future potential for medicinal chemistry of QSAR/QSPR with whole molecule descriptors. , 2002, Current topics in medicinal chemistry.

[28]  P. Ducrot,et al.  3D-QSAR CoMFA on cyclin-dependent kinase inhibitors. , 2000, Journal of medicinal chemistry.

[29]  D. W. Fry Chapter 16. Recent Advances in Tyrosine Kinase Inhibitors , 1996 .

[30]  M. Karelson,et al.  QSPR: the correlation and quantitative prediction of chemical and physical properties from structure , 1995 .

[31]  Milan Randic,et al.  Quantitative Structure-Activity Relationship of Flavonoid Analogues. 3. Inhibition of p56lck Protein Tyrosine Kinase , 2000, J. Chem. Inf. Comput. Sci..

[32]  Peter C. Jurs,et al.  Prediction of Vapor Pressures of Hydrocarbons and Halohydrocarbons from Molecular Structure with a Computational Neural Network Model , 1999, J. Chem. Inf. Comput. Sci..

[33]  Daniel Svozil,et al.  Introduction to multi-layer feed-forward neural networks , 1997 .

[34]  Mati Karelson,et al.  QSPR of 3-aryloxazolidin-2-one antibacterials. , 2004, Bioorganic & medicinal chemistry.

[35]  Mati Karelson,et al.  QSAR treatment of drugs transfer into human breast milk. , 2005, Bioorganic & medicinal chemistry.