Systematic selection of chemical fingerprint features improves the Gibbs energy prediction of biochemical reactions.

ABSTRACT:

Motivation

Accurate and wide-ranging prediction of thermodynamic parameters for biochemical reactions can facilitate deeper insights into the workings and the design of metabolic systems.

Results

Here, we introduce a machine learning method with chemical fingerprint-based features for the prediction of the Gibbs free energy of biochemical reactions. From a large pool of 2D fingerprint-based features, this method systematically selects a small number of relevant ones and uses them to construct a regularized linear model. Since a manual selection of 2D structure-based features can be a tedious and time-consuming task, requiring expert knowledge about the structure-activity relationship of chemical compounds, the systematic feature selection step in our method offers a convenie

SUBMITTER: Alazmi M 

PROVIDER: S-EPMC6662295 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature