TY - JOUR
AU - Nachbar, Robert
AB - Three-dimensional molecular modeling can provide an unlimited number m of structural properties. Comparative Molecular Field Analysis (CoMFA), for example, may calculate thousands of field values for each model structure. When m is large, partial least squares (PLS) is the statistical method of choice for fitting and predicting biological responses. Yet PLS is usually implemented in a property-based fashion which is optimal only for small m. We describe here a sample-based formulation of PLS which can be used to fit any single response (bioactivity). SAMPLS reduces all explanatory data to the pairwise ‘distances’ among n sample (molecules), or equivalently to an n-by-n covariance matrix C. This matrix, unmodified, can be used to fit all PLS components. Furthermore, SAMPLS will validate the model by modern resampling techniques, at a cost independent of m. We have implemented SAMPLS as a Fortran program and have reproduced conventional and cross-validated PLS analyses of data from two published studies. Full (leaveach-out) cross-validation of a typical CoMFA takes 0.2 CPU s. SAMPLS is thus ideally suited to structure-activity analysis based on CoMFA fields or bonded topology. The sample-distance formulation also relates PLS to methods like cluster analysis and nonlinear mapping, and shows how drastically PLS simplifies the information in CoMFA fields.
TI - Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA
JF - Journal of Computer-Aided Molecular Design
DO - 10.1007/BF00124364
DA - 2004-05-25
UR - https://www.deepdyve.com/lp/springer-journals/sample-distance-partial-least-squares-pls-optimized-for-many-variables-Hka5JdPzFd
SP - 587
EP - 619
VL - 7
IS - 5
DP - DeepDyve
ER -