A Triple-Modal Contrastive Learning Framework with Sequence, Graph, and 3D Features for Drug-Target Interaction Prediction
arXiv:2605.29926v1 Announce Type: new Abstract: Accurate prediction of drug-target interactions (DTI) is critical for drug discovery. Existing methods often rely on single-modal representations (e.g., sequences or graphs) or combine only two modalities, overlooking 3D structural features. To address this challenge, we propose TriMod-DTI, a triple-modal contrastive learning framework that incorporates 1D sequences, 2D graphs, and 3D structures of drugs and proteins, obtaining the universal and complementary feature representations for DTI prediction. We design a Feature Extractor to capture dru
The increasing availability of diverse biological datasets and advancements in multi-modal AI architectures enable more sophisticated drug discovery approaches at this time.
Improved DTI prediction significantly accelerates drug discovery, crucial for addressing emerging health crises and reducing R&D costs in the pharmaceutical industry.
Drug discovery pipelines can now integrate a richer, multi-dimensional understanding of molecular interactions, moving beyond simpler sequence or graph-based methods.
- · Pharmaceuticals
- · Biotech startups
- · AI-driven drug discovery platforms
- · Traditional drug discovery methods
- · Companies slow to adopt AI
More efficient and faster identification of promising drug candidates for various diseases.
Reduced R&D costs and shortened timelines for bringing new drugs to market, increasing profitability for innovators.
A potential increase in the number of novel therapeutics approved, impacting public health and longevity.
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