Bimodal Partial Least‐squares Approach and Its Application to Chemogenomics Studies for Molecular Design

Hasegawa, Kiyoshi; Funatsu, Kimito

doi:10.1002/9781118742785.ch4

Cited by 5 publications

(1 citation statement)

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“…Chemical interpretation is invaluable for generating hypotheses and knowledge, which are the final goal of molecular design. In previous studies, we applied two novel bi‐modal PLS approaches for this purpose,10–12 because these methods enable direct connections to be drawn between biological activities and ligand and protein descriptors.…”

Section: Introductionmentioning

confidence: 99%

Data Mining of Chemogenomics Data Using Bi‐Modal PLS Methods and Chemical Interpretation for Molecular Design

Hasegawa¹,

Funatsu

2014

Molecular Informatics

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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.

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Section: Introductionmentioning

confidence: 99%