QSAR study of angiotensin II antagonists using robust boosting partial least squares regression.

[1]  E. G. Erdös,et al.  Angiotensin I converting enzyme. , 1975, Circulation research.

[2]  Harald Martens,et al.  A multivariate calibration problem in analytical chemistry solved by partial least-squares models in latent variables , 1983 .

[3]  R. Chang,et al.  Nonpeptide angiotensin II antagonists derived from 4H-1,2,4-triazoles and 3H-imidazo[1,2-b][1,2,4]triazoles. , 1993, Journal of medicinal chemistry.

[4]  Roberto Todeschini,et al.  A 3D QSAR approach to the search for geometrical similarity in a series of nonpeptide angiotensin II receptor antagonists , 1994, J. Comput. Aided Mol. Des..

[5]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[6]  Harris Drucker,et al.  Improving Regressors using Boosting Techniques , 1997, ICML.

[7]  K. Yi,et al.  A comparative molecular field analysis and molecular modelling studies on pyridylimidazole type of angiotensin II antagonists. , 1999, Bioorganic & medicinal chemistry.

[8]  S. Kim,et al.  The conformation and activity relationship of benzofuran type of angiotensin II receptor antagonists. , 2000, Bioorganic & medicinal chemistry.

[9]  C. Hansch,et al.  Comparative QSAR: angiotensin II antagonists. , 2001, Chemical reviews.

[10]  J. Friedman Stochastic gradient boosting , 2002 .

[11]  Prashant V. Desai,et al.  A 3D-QSAR of Angiotensin II (AT1) Receptor Antagonists Based on Receptor Surface Analysis , 2004, J. Chem. Inf. Model..

[12]  R. Schapire The Strength of Weak Learnability , 1990, Machine Learning.

[13]  Yi-Zeng Liang,et al.  A generalized boosting algorithm and its application to two-class chemical classification problem , 2005 .

[14]  Jian-Hui Jiang,et al.  Modified Ant Colony Optimization Algorithm for Variable Selection in QSAR Modeling: QSAR Studies of Cyclooxygenase Inhibitors , 2005, J. Chem. Inf. Model..

[15]  Mohsen Kompany-Zareh,et al.  Application of radial basis function networks and successive projections algorithm in a QSAR study of anti‐HIV activity for a large group of HEPT derivatives , 2006 .

[16]  Yukihiro Ozaki,et al.  Dry film method with ytterbium as the internal standard for near infrared spectroscopic plasma glucose assay coupled with boosting support vector regression , 2006 .

[17]  Wei-Qi Lin,et al.  Artificial neural network-based transformation for nonlinear partial least-square regression with application to QSAR studies. , 2007, Talanta.