Hou Zhanwei, Xu Zhenhan, Yan Chaokun, Luo Huimin, Luo Junwei
School of Software, Henan Polytechnic University, Jiaozuo 454003, China.
School of Computer and Information Engineering, Henan University, Kaifeng 475001, China.
Comput Biol Chem. 2025 Apr;115:108326. doi: 10.1016/j.compbiolchem.2024.108326. Epub 2024 Dec 29.
Compound-protein interaction (CPI) is essential to drug discovery and design, where traditional methods are often costly and have low success rates. Recently, the integration of machine learning and deep learning in CPI research has shown potential to reduce costs and enhance discovery efficiency by improving protein target identification accuracy. Additionally, with an urgent need for novel therapies against complex diseases, CPI investigation could lead to the identification of effective new drugs. Since drug-target interactions involve complex biological processes, refined models are necessary for precise feature extraction and analysis. Nevertheless, current CPI prediction methods still face significant limitations: predictions lack sufficient accuracy, models require improved generalization ability, and further validation across diverse datasets remains essential.
To address some issues at the current stage, this paper proposes a combined deep learning method, CPI-GGS, for predicting and analyzing compound-protein interactions. The source code is available on GitHub at https://github.com/xingjie321/CPI-GGS.
The experimental results demonstrate improved accuracy in predicting compound-protein interactions and enhance the understanding of how compounds and proteins interact, providing a valuable new tool for drug discovery and development.
化合物 - 蛋白质相互作用（CPI）对于药物发现和设计至关重要，而传统方法往往成本高昂且成功率较低。最近，机器学习和深度学习在CPI研究中的整合显示出通过提高蛋白质靶点识别准确性来降低成本和提高发现效率的潜力。此外，鉴于迫切需要针对复杂疾病的新型疗法，CPI研究可能会导致发现有效的新药。由于药物 - 靶点相互作用涉及复杂的生物过程，因此需要精细的模型来进行精确的特征提取和分析。然而，当前的CPI预测方法仍然面临重大局限性：预测缺乏足够的准确性，模型需要提高泛化能力，并且在不同数据集上进行进一步验证仍然至关重要。
为了解决当前阶段的一些问题，本文提出了一种用于预测和分析化合物 - 蛋白质相互作用的组合深度学习方法CPI - GGS。源代码可在GitHub上获取，网址为https://github.com/xingjie321/CPI - GGS。
实验结果表明，在预测化合物 - 蛋白质相互作用方面准确性有所提高，并增强了对化合物与蛋白质如何相互作用的理解，为药物发现和开发提供了一种有价值的新工具。
