Application of non-parametric regression to quantitative structure-activity relationships.
Several non-parametric regressors have been applied to modelling quantitative structure-activity relationship (QSAR) data. Performances were benchmarked against multilinear regression and the nonlinear method of smoothing splines. Variable selection was explored through systematic combinations of different variables and combinations of principal components. For the training set examined--539 inhibitors of the tyrosine kinase, Syk--the best two-descriptor model had a 5-fold cross-validated q2 of 0.43. This was generated by a multi-variate Nadaraya-Watson kernel estimator. A subsequent, independent, test set of 371 similar chemical entities showed the model had some predictive power. Other approaches did not perform as well. A modest increase in predictive ability can be achieved with three descriptors, but the resulting model is less easy to visualise. We conclude that non-parametric regression offers a potentially powerful approach to identifying predictive, low-dimensional QSARs.
Hirst
Jonathan D
JD
School of Chemistry, University of Nottingham, University Park, NG7 2RD, Nottingham, UK. jonathan.hirst@nottingham.ac.uk
McNeany
T John
TJ
Howe
Trevor
T
Whitehead
Lewis
L
D016208:Databases, Factual; D004791:Enzyme Inhibitors / Q000737:chemistry*; D004792:Enzyme Precursors / Q000037:antagonists & inhibitors*; D047908:Intracellular Signaling Peptides and Proteins; D008956:Models, Chemical; D011505:Protein-Tyrosine Kinases / Q000037:antagonists & inhibitors*; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D000072377:Syk Kinase
D004791:Enzyme Inhibitors; D004792:Enzyme Precursors; D047908:Intracellular Signaling Peptides and Proteins; D011505:Protein-Tyrosine Kinases; D000072377:Syk Kinase