ArticleBioinformatics (Oxford, England)2019
Systematic selection of chemical fingerprint features improves the Gibbs energy prediction of biochemical reactions.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- A deep learning architecture for metabolic pathway prediction.Bioinformatics (Oxford, England) · 2024Article
- A novel graph mining approach to predict and evaluate food-drug interactions.Scientific reports · 2022Article
- dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design.PLoS computational biology · 2021Article
- Analysis of the effects of related fingerprints on molecular similarity using an eigenvalue entropy approach.Journal of cheminformatics · 2021Article
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5 authors.
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Abstract
motivationAccurate and wide-ranging prediction of thermodynamic parameters for biochemical reactions can facilitate deeper insights into the workings and the design of metabolic systems.
resultsHere, 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.
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