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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 4

Explainable lipid nanoparticles transfection efficiency prediction model based on multimodal feature fusion

Pang Guojun1,2
Lin Bangjiang1,2
Zheng Bowen2
Chen Jian2
Xu Bohui2
1. College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China
2. Quanzhou Equipment Manufacturing Research Center, Haixi Institute of Chinese Academy of Sciences, Quanzhou Fujian 362200, China
DOI: 10.19734/j.issn.1001-3695.2025.08.0282

Abstract

In recent years, research on lipid nanoparticles (LNPs) for drug delivery systems usually focuses on a single feature and neglects the role of auxiliary lipids. This study aimed to construct an explainable deep learning model based on multimodal feature fusion, named MolGraphNet, for accurate prediction and interpretability analysis of auxiliary lipid transfection efficiency. MolGraphNet used neural network convolution (NNConv) to extract molecular structural graph features and applied a multilayer perceptron (MLP) to extract physicochemical numerical features. A multimodal fusion module integrated structural and property information at a deep level. The model also combined molecular structure encoding with SHAP analysis to perform interpretability and visualization of key features, revealing the intrinsic relationship between auxiliary lipid structures and transfection efficiency. Results on six cell-type tasks from public datasets showed that MolGraphNet achieved prediction errors of 6%–11% and Pearson and Spearman correlation coefficients between 0.75 and 0.92, which outperformed baseline models. MolGraphNet provides high prediction accuracy and robustness and offers interpretable visualization of key features through SHAP-based analysis, giving valuable guidance for auxiliary lipid design and LNP formulation optimization.

Foundation Support

福建省海洋与渔业产业高质量发展专项资金资助项目(FJHYF-ZH-2023-09)
中国科学院与福建省STS项目(2024T3038、2024T3060、2024T3062)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.08.0282
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 4

Publish History

[2025-12-11] Accepted Paper

Cite This Article

庞国君, 林邦姜, 郑博文, 等. 基于多模态特征融合的可解释性脂质纳米颗粒转染效率预测模型 [J]. 计算机应用研究, 2026, 43 (4). (2025-12-11). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0282. (Pang Guojun, Lin Bangjiang, Zheng Bowen, et al. Explainable lipid nanoparticles transfection efficiency prediction model based on multimodal feature fusion [J]. Application Research of Computers, 2026, 43 (4). (2025-12-11). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0282. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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