ArticleBMC biology2025
SMFF-DTA: using a sequential multi-feature fusion method with multiple attention mechanisms to predict drug-target binding affinity.
Article in BMC biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Drug-Target Interaction Prediction with PIGLET.bioRxiv : the preprint server for biology · 2026Article
- MGF-DTA: A Multi-Granularity Fusion Model for Drug-Target Binding Affinity Prediction.International journal of molecular sciences · 2026Article
- A survey on deep learning for drug-target binding prediction: models, benchmarks, evaluation, and case studies.Briefings in bioinformatics · 2025Review
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Authors and funding
7 authors.
Funding
Abstract
backgroundDrug-target binding affinity (DTA) prediction can accelerate the drug screening process, and deep learning techniques have been used in all facets of drug research. Affinity prediction based on deep learning methods has proven crucial to drug discovery, design, and reuse. Among these, the sequence-based approach using 1D sequences of drugs and targets as inputs typically results in the loss of structural information, whereas the structure-based method frequently results in increased computing costs due to the intricate structure of the molecule graph.
resultsWe propose a sequential multifeature fusion method (SMFF-DTA) to achieve efficient and accurate prediction. SMFF-DTA uses sequential methods to represent the structural information and physicochemical properties of drugs and targets and introduces multiple attention blocks to capture interaction features closely.
conclusionsAs demonstrated by our extensive studies, SMFF-DTA outperforms the other methods in terms of various metrics, showing its advantages and effectiveness as a drug-target binding affinity predictor.
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