GastroDL-Fusion: A Dual-Modal Deep Learning Framework Integrating Protein-Ligand Complexes and Gene Sequences for Gastrointestinal Disease Drug Discovery

2025-11-07

AI Summary

Accurate prediction of protein–ligand binding affinity remains challenging in gastrointestinal disease (e.g., gastric ulcers, Crohn’s disease, ulcerative colitis) drug and vaccine development. Method: We propose the first bimodal deep learning framework integrating both protein–ligand structural data and disease-associated gene sequences. It employs Graph Isomorphism Networks (GIN) to encode molecular graph structures and leverages pre-trained protein language models (ProtBERT/ESM) to represent pathogenic gene sequences, augmented by a cross-modal interaction module for joint structural–sequential modeling. Contribution/Results: Evaluated on a gastrointestinal disease target dataset, our framework achieves a mean absolute error (MAE) of 1.12 and root-mean-square error (RMSE) of 1.75—substantially outperforming unimodal baselines (e.g., CNN, BiLSTM). This advance establishes a mechanism-informed paradigm for precision drug discovery.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Accurate prediction of protein-ligand binding affinity plays a pivotal role in accelerating the discovery of novel drugs and vaccines, particularly for gastrointestinal (GI) diseases such as gastric ulcers, Crohn's disease, and ulcerative colitis. Traditional computational models often rely on structural information alone and thus fail to capture the genetic determinants that influence disease mechanisms and therapeutic responses. To address this gap, we propose GastroDL-Fusion, a dual-modal deep learning framework that integrates protein-ligand complex data with disease-associated gene sequence information for drug and vaccine development. In our approach, protein-ligand complexes are represented as molecular graphs and modeled using a Graph Isomorphism Network (GIN), while gene sequences are encoded into biologically meaningful embeddings via a pre-trained Transformer (ProtBERT/ESM). These complementary modalities are fused through a multi-layer perceptron to enable robust cross-modal interaction learning. We evaluate the model on benchmark datasets of GI disease-related targets, demonstrating that GastroDL-Fusion significantly improves predictive performance over conventional methods. Specifically, the model achieves a mean absolute error (MAE) of 1.12 and a root mean square error (RMSE) of 1.75, outperforming CNN, BiLSTM, GIN, and Transformer-only baselines. These results confirm that incorporating both structural and genetic features yields more accurate predictions of binding affinities, providing a reliable computational tool for accelerating the design of targeted therapies and vaccines in the context of gastrointestinal diseases.

Problem

• Predicts protein-ligand binding affinity for gastrointestinal disease drug discovery
• Integrates protein-ligand complexes with gene sequence information
• Overcomes limitations of structure-only models by capturing genetic determinants

Innovation

• Integrates protein-ligand complexes with gene sequences
• Uses Graph Isomorphism Network for molecular graphs
• Fuses modalities via multi-layer perceptron for interactions