Structure-activity models of oral clearance, cytotoxicity, and LD50: a screen for promising anticancer compounds.

ABSTRACT:

Background

Quantitative structure-activity relationship (QSAR) models have become popular tools to help identify promising lead compounds in anticancer drug development. Few QSAR studies have investigated multitask learning, however. Multitask learning is an approach that allows distinct but related data sets to be used in training. In this paper, a suite of three QSAR models is developed to identify compounds that are likely to (a) exhibit cytotoxic behavior against cancer cells, (b) exhibit high rat LD50 values (low systemic toxicity), and (c) exhibit low to modest human oral clearance (favorable pharmacokinetic characteristics). Models were constructed using Kernel Multitask Latent Analysis (KMLA), an approach that can effectively handle a large number of correlated data feature

SUBMITTER: Boik JC 

PROVIDER: S-EPMC2442056 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature