A Multiple Environmental Parameters and Molecular Fingerprints Contribution model for prediction of Gibbs free energy

IF 3.1 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry Pub Date : 2025-07-17 DOI:10.1016/j.compbiolchem.2025.108583

Xin Zhao, Kang Li, Tao Zhang, Shuxin Cui, Yahui Cao, Xue Jia

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引用次数: 0

Abstract

Accurate prediction of thermodynamic parameters in biochemical reactions is essential for understanding and designing metabolic systems. Most existing methods for predicting the Gibbs free energy of biochemical reactions often neglect the environmental influences on Gibbs free energy such as pH, temperature and ionic strength, and lack efficient feature selection mechanisms, resulting in suboptimal predictive accuracy. In this paper, a Convolutional Neural Network Based Model with Multiple Environmental Parameters and Molecular Fingerprint Contribution (MEFC-CNN) is proposed to address these problems. Firstly, an encoding method that incorporates environmental factors is proposed to improve the ability to represent features. Secondly, a convolutional neural network with multiple parallel feature inputs is designed to efficiently select the key features, thereby improving the accuracy of Gibbs free energy prediction of biochemical reactions. Experimental results demonstrate that the MEFC-CNN model achieves superior predictive accuracy compared to existing methods.

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吉布斯自由能预测的多环境参数和分子指纹贡献模型

准确预测生化反应中的热力学参数对于理解和设计代谢系统至关重要。现有的生化反应吉布斯自由能预测方法大多忽略了环境对吉布斯自由能的影响，如pH、温度、离子强度等，且缺乏有效的特征选择机制，导致预测精度不理想。本文提出了一种基于多环境参数和分子指纹贡献的卷积神经网络模型（MEFC-CNN）来解决这些问题。首先，提出了一种结合环境因素的编码方法，提高了特征表示能力；其次，设计具有多个并行特征输入的卷积神经网络，有效地选择关键特征，从而提高生化反应吉布斯自由能预测的准确性。实验结果表明，与现有方法相比，MEFC-CNN模型具有更高的预测精度。

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来源期刊

Computational Biology and Chemistry 生物-计算机：跨学科应用

CiteScore

6.10

自引率

3.20%

发文量

142

审稿时长

24 days

期刊介绍： Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.