GTFKAN: A Novel Microbe-drug Association Prediction Model Based on Graph Transformer and Fourier Kolmogorov-Arnold Networks

• Published in: Journal of molecular biology Vol. 437; no. 17; p. 169201
• Main Authors: Lai, Jiacheng, Zhang, Zhen, Zeng, Bin, Wang, Lei
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 01.09.2025
• Subjects: Algorithms; Bacteria - drug effects; Computational Biology - methods; data collection; Fourier Analysis; Fourier Kolmogorov-Arnold Network; Graph Transformer Network; human health; Humans; microbe-drug association; molecular biology; prediction; prediction model; the Gaussian kernel and functional similarities; the Gaussian kernel and functional similarities; Fourier Kolmogorov-Arnold Network; prediction model; Graph Transformer Network; microbe-drug association
• ISSN: 0022-2836; 1089-8638; 1089-8638
• DOI: 10.1016/j.jmb.2025.169201

[Display omitted] •GTFKAN combines GTN with FKAN, solving the problem that traditional methods are difficult to capture high-frequency features.•The study proposed a ‘data-algorithm co-evolution’ framework, providing a new direction for paradigm shift in the field.•Both extensive comparative experiments and case studies validated the superiority of GTFKAN. Microbes have been shown to be closely related to human health. In recent years, lots of computational methods for predicting microbial-drug association have been proposed. In this manuscript, we introduced a novel predictive model, called GTFKAN, to identify potential microbe-drug associations by combining Graph Transformation Networks (GTN) with Fourier Kolmogorov-Arnold Networks (FKAN). In GTFKAN, we would first compute the Gaussian kernel and functional similarity of microbes and drugs respectively, and then adopt random walk and restart (RWR) methods to enhance these similar features to construct a new microbe-drug heterogeneous network HN. At the same time, we would further calculate the cosine similarity of microbes and diseases to construct another microbe-drug heterogeneous network LDIM. Next, we would input HN into GTN to derive the location and structural features of microorganisms and drugs, and input LDIM into FKAN to extract the hidden higher-order features of microorganisms and drugs, respectively. Finally, we would integrate these two features extracted by GTN and FKAN and feed the integrated features into the MLP classifier to infer potential microbial-drug associations. Moreover, to evaluate the performance of GTFKAN, we compared it with state-of-the-art methods based on well-known public datasets, and the experimental results show that GTFKAN can achieve satisfactory predictive performance. In addition, the results of ablation experiments and case studies also demonstrated the superiority of GTFKAN, which means that GTFKAN may be a useful microbial-drug association prediction tool in the future.