Predicting mTOR inhibitors with a classifier using recursive partitioning and Naive Bayesian approaches.

ABSTRACT:

Background

Mammalian target of rapamycin (mTOR) is a central controller of cell growth, proliferation, metabolism, and angiogenesis. Thus, there is a great deal of interest in developing clinical drugs based on mTOR. In this paper, in silico models based on multi-scaffolds were developed to predict mTOR inhibitors or non-inhibitors.

Methods

First 1,264 diverse compounds were collected and categorized as mTOR inhibitors and non-inhibitors. Two methods, recursive partitioning (RP) and naïve Bayesian (NB), were used to build combinatorial classification models of mTOR inhibitors versus non-inhibitors using physicochemical descriptors, fingerprints, and atom center fragments (ACFs).

Results

A total of 253 models were constructed and the overall predictive accuracies of th

SUBMITTER: Wang L 

PROVIDER: S-EPMC4018356 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature