title: Orthogonal PLS (OPLS) Modeling for Improved Analysis and Interpretation in Drug Design.
abstract: Partial least squares (PLS) regression is a flexible data analytical approach, which can be made even more versatile and useful by various modifications. In this article we describe the extension into orthogonal PLS modeling, in terms of two new methods, called OPLS and O2PLS, with similar prediction capacity but improved model interpretation.
authors.affiliation: MKS Umetrics AB, SE-907 19 Umeå, Sweden. lennart.eriksson@umetrics.com.
authors.forename: Lennart
authors.initials: L
authors.lastname: Eriksson
authors.affiliation: MKS Umetrics AB, Stortorget 21, SE-211 34 Malmö, Sweden.
authors.forename: Josefin
authors.initials: J
authors.lastname: Rosén
authors.affiliation: MKS Umetrics AB, SE-907 19 Umeå, Sweden.
authors.forename: Erik
authors.initials: E
authors.lastname: Johansson
authors.affiliation: Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, SE-901 87 Umeå, Sweden.
authors.forename: Johan
authors.initials: J
authors.lastname: Trygg
keywords: Interpretability; Latent variables; Orthogonal variation; Predictive variation