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

Meshari Alazmi, Hiroyuki Kuwahara, Othman Soufan, Lizhong Ding, Xin Gao

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Motivation \nAccurate and wide-ranging prediction of thermodynamic parameters for biochemical reactions can facilitate deeper insights into the workings and the design of metabolic systems. \n \nResults \nHere, 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 structurebased 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 convenient means to identify relevant 2D fingerprint-based features. By comparing our method with state-of-the-art linear regression-based methods for the standard Gibbs free energy prediction, we demonstrated that its prediction accuracy and prediction coverage are most favorable. Our results show direct evidence that a number of 2D fingerprints collectively provide useful information about the Gibbs free energy of biochemical reactions and that our systematic feature selection procedure provides a convenient way to identify them.
Original languageEnglish (US)
Pages (from-to)2634-2643
Number of pages10
JournalBioinformatics
Volume35
Issue number15
DOIs
StatePublished - Dec 24 2018

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