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This is a Quantitative Study study.

September 20, 2025Open Access

MIF–DTI: a multimodal information fusion method for drug–target interaction prediction

JSJuan ShanPace University JSJinhua SunNingbo University HZHaoran ZhengBeijing University of Posts and Telecommunications

Key Points

MIF-DTI enhances predictive accuracy in drug-target interaction predictions by integrating multiple data sources.
The study shows that MIF-DTI and its ensemble version outperform existing methods in predictive performance.
By employing sequence and graph encoding, the method effectively combines various molecular information.
Experimental results were validated using three DTI datasets, showcasing the robustness of the approach.

Abstract

Abstract Drug–target interaction (DTI) prediction is essential for drug discovery and repurposing. To overcome the limitations of current DTI prediction methods that rely on single-source encoding and inadequately fuse multimodal information, this study proposes a DTI prediction method based on multimodal information fusion (MIF–DTI) and further designs an ensemble version (MIF–DTI-B). MIF–DTI encodes the SMILES sequences of drugs and the amino acid sequences of targets via a sequence encoding module to extract their 1D sequence features. It conducts dual-view representation encoding on the hierarchical molecular graphs of drugs and the contact graphs of targets through a graph encoding module, aiming to capture their 2D topological structure information. A decoding module is utilized to fuse information from different modalities. MIF–DTI-B ensembles several MIF–DTI models through cross-validation strategy to improve predictive accuracy. This study evaluates the proposed models on three publicly accessible DTI datasets. Experimental results demonstrate that fully integrating multimodal information enables both MIF–DTI and MIF–DTI-B to consistently outperform state-of-the-art methods.

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