title: Advanced PLS Techniques in Chemoinformatics Studies.
abstract: Multivariate statistical methods are commonly used in the analysis of quantitative structure-activity/property relationships (QSAR and QSPR, respectively). The partial least squares (PLS) method is of particular interest because it can analyze data containing numerous X variables with strongly collinear and noisy characteristics and can simultaneously model several response variables Y. Furthermore, it can provide us with several prediction regions and diagnostic plots as statistical measures. PLS has evolved or changed for coping with the severe demands associated with the complex data structures of X and Y variables. In this review article, we selected five advanced PLS techniques and outlined their algorithms with representative examples. In particular, we made efforts to describe how to disclose the inner relations embedded in data and how to use this information for molecular design.
authors.forename: Kiyoshi
authors.initials: K
authors.lastname: Hasegawa
authors.affiliation: Department of Chemical System Engineering, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan. funatsu@chemsys.t.u-tokyo.ac.jp.
authors.forename: Kimito
authors.initials: K
authors.lastname: Funatsu
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D015195:Drug Design; D016018:Least-Squares Analysis; D008956:Models, Chemical; D021281:Quantitative Structure-Activity Relationship