Name
Title | ||
|---|---|---|
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 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. Availability and implementation Our software is freely available for download at http://sfb.kaust.edu.sa/Pages/Software.aspx. Supplementary information Supplementary data are available at Bioinformatics online. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1093/bioinformatics/bty1035 | BIOINFORMATICS |
Field | DocType | Volume |
Data mining,Computer science,Fingerprint,Biochemical reactions,Gibbs free energy | Journal | 35 |
Issue | ISSN | Citations |
15 | 1367-4803 | 0 |
PageRank | References | Authors |
0.34 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meshari Alazmi | 1 | 3 | 1.76 |
| Hiroyuki Kuwahara | 2 | 13 | 2.66 |
| Othman Soufan | 3 | 3 | 2.25 |
| Lizhong Ding | 4 | 31 | 8.36 |
| Xin Gao | 5 | 598 | 64.98 |