Target prediction utilising negative bioactivity data covering large chemical space.

ABSTRACT:

Background

In silico analyses are increasingly being used to support mode-of-action investigations; however many such approaches do not utilise the large amounts of inactive data held in chemogenomic repositories. The objective of this work is concerned with the integration of such bioactivity data in the target prediction of orphan compounds to produce the probability of activity and inactivity for a range of targets. To this end, a novel human bioactivity data set was constructed through the assimilation of over 195 million bioactivity data points deposited in the ChEMBL and PubChem repositories, and the subsequent application of a sphere-exclusion selection algorithm to oversample presumed inactive compounds.

Results

A Bernoulli Naïve Bayes algorithm was trained using the

SUBMITTER: Mervin LH 

PROVIDER: S-EPMC4619454 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature