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Diagonal Method to Measure Synergy Among Any Number of Drugs

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CrossSG-DTA: Synergizing Sequence Semantics and Graph Structures via Cross-Attention for Drug-Target Affinity

Wei Lan, Tian Huang, Guohang He

IEEE Journal of Biomedical and Health Informatics

|July 6, 2026

Summary

Predicting drug-target affinity (DTA) is crucial for drug discovery. Our CrossSG-DTA deep learning model integrates sequence and graph data, significantly improving DTA prediction accuracy.

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Area of Science:

Computational chemistry
Bioinformatics
Drug discovery

Background:

Accurate drug-target affinity (DTA) prediction is vital for efficient drug discovery.
Modeling complex interactions between small molecules (ligands) and large biomolecules (targets) presents a significant computational challenge.

Purpose of the Study:

To develop a novel multi-modal deep learning framework, CrossSG-DTA, for enhanced drug-target affinity prediction.
To integrate diverse data modalities, including sequence semantics and graph structural information, for a more comprehensive interaction modeling.

Main Methods:

Utilized ChemBERTa and ESM-2 for extracting semantic features from drug and target sequences.
Employed a modified Graph Convolutional Network (GCN) to capture structural information.
Designed a symmetric dual cross-attention fusion mechanism to integrate sequence and structural features.
Concatenated fused features and processed through a Multi-Layer Perceptron (MLP) for final affinity prediction.

Main Results:

CrossSG-DTA demonstrated superior performance compared to existing state-of-the-art methods on the Davis and KIBA datasets.
The model effectively captures complex dependencies between global sequence representations and local topological structures.
A case study on a glaucoma-related target validated the model's practical utility for in silico DTA tasks.

Conclusions:

The proposed CrossSG-DTA framework offers a powerful and accurate approach for drug-target affinity prediction.
Integrating multi-modal data through advanced deep learning techniques enhances the predictive capabilities for drug discovery.
CrossSG-DTA serves as a valuable in silico tool for accelerating the identification of potential drug candidates.