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CM-MTL-DTI: Drug-Target Interaction Prediction via Cross-Modal Alignment and Multi-Task Learning.

Yizhao Zhao¹, Shiwei Gao¹, Yifan Liu²

¹College of Artificial Intelligence and Computing (Software School), Northwest Normal University, Lanzhou, Gansu 730070, China.

Journal of Chemical Information and Modeling

|June 1, 2026

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This study introduces CM-MTL-DTI, a novel framework for drug-target interaction (DTI) prediction. It effectively models complex drug-protein relationships, improving accuracy in drug discovery and repurposing.

Area of Science:

Computational chemistry
Bioinformatics
Drug discovery

Background:

Drug-target interaction (DTI) prediction is vital for identifying drug candidates and understanding their mechanisms.
Existing DTI prediction methods struggle with heterogeneous data and fine-grained dependencies between drug structures and protein sequences.
Current approaches often use unimodal representations or simplistic fusion techniques, limiting their ability to capture complex interactions.

Purpose of the Study:

To develop an advanced framework for accurate drug-target interaction prediction.
To address limitations in existing methods by modeling heterogeneous and fine-grained dependencies.
To enhance drug discovery and repurposing through improved DTI prediction.

Main Methods:

Proposed CM-MTL-DTI, a DTI-oriented collaborative alignment framework.

Related Experiment Videos

Utilized independent 1D convolutional neural networks for drug and protein sequence encoding.

Incorporated a GIN-based graph encoder for drug structural information.

Implemented an asymmetric bidirectional cross-modal attention mechanism.

Introduced three collaborative objectives: cross-modal masked reconstruction (XMR), graph-sequence consistency learning (GSC), and supervised contrastive learning (SupCon).

Main Results:

CM-MTL-DTI demonstrated stable and competitive performance on three benchmark datasets.
The framework effectively captured direction-sensitive dependencies between drug substructures and protein residues.
Collaborative objectives enhanced local semantic recovery, multiview alignment, and interaction representation discrimination.

Conclusions:

The proposed CM-MTL-DTI framework significantly improves drug-target interaction prediction.
The DTI-oriented collaborative design effectively models complex drug-protein relationships.
This approach offers a promising tool for accelerating drug discovery and repurposing efforts.