Extending in Silico Protein Target Prediction Models to Include Functional Effects.

ABSTRACT: In silico protein target deconvolution is frequently used for mechanism-of-action investigations; however existing protocols usually do not predict compound functional effects, such as activation or inhibition, upon binding to their protein counterparts. This study is hence concerned with including functional effects in target prediction. To this end, we assimilated a bioactivity training set for 332 targets, comprising 817,239 active data points with unknown functional effect (binding data) and 20,761,260 inactive compounds, along with 226,045 activating and 1,032,439 inhibiting data points from functional screens. Chemical space analysis of the data first showed some separation between compound sets (binding and inhibiting compounds were more similar to each other than both binding and a

SUBMITTER: Mervin LH 

PROVIDER: S-EPMC6004408 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature