title: Data Mining of Chemogenomics Data Using Bi-Modal PLS Methods and Chemical Interpretation for Molecular Design.
abstract: Chemogenomics is a new strategy in drug discovery for interrogating all molecules capable of interacting with all biological targets. Because of the almost infinite number of drug-like organic molecules, bench-based experimental chemogenomics methods are not generally feasible. Several in silico chemogenomics models have therefore been developed for high-throughput screening of large numbers of drug candidate compounds and target proteins. In previous studies, we described two novel bi-modal PLS approaches. These methods provide a significant advantage in that they enable direct connections to be made between biological activities and ligand and protein descriptors. In this special issue, we review these two PLS-based approaches using two different chemogenomics datasets for illustration. We then compare the predictive and interpretive performance of the two methods using the same congeneric data set.
authors.affiliation: Chugai Pharmaceutical Company, Kamakura Research Laboratories, Kajiwara 200, Kamakura, Kanagawa 247-8530, Japan.
authors.forename: Kiyoshi
authors.initials: K
authors.lastname: Hasegawa
authors.affiliation: Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan phone: (+81) 03-5841-7751; fax: (+81) 03-5841-7771. funatsu@chemsys.t.u-tokyo.ac.jp.
authors.forename: Kimito
authors.initials: K
authors.lastname: Funatsu
keywords: Bi-modal PLS; Chemical interpretation; Chemogenomics; Data mining; Molecular design