# MGF-DTA: A Multi-Granularity Fusion Model for Drug–Target Binding Affinity Prediction

## TL;DR

This paper introduces MGF-DTA, a new model that improves drug-target binding affinity prediction by combining multiple data sources and advanced fusion techniques.

## Contribution

The novel MGF-DTA model integrates multi-granularity fusion and hierarchical attention for enhanced DTA prediction.

## Key findings

- MGF-DTA outperforms existing methods on Davis, KIBA, and BindingDB datasets.
- Ablation studies confirm the effectiveness of the model's fusion components.
- The model demonstrates robust generalization through case studies.

## Abstract

Drug–target affinity (DTA) prediction is one of the core components of drug discovery. Despite considerable advances in previous research, DTA tasks still face several limitations with insufficient multi-modal information of drugs, the inherent sequence length limitation of protein language models, and single attention mechanisms that fail to capture critical multi-scale features. To alleviate the above limitations, we developed a multi-granularity fusion model for drug–target binding affinity prediction, termed MGF-DTA. This model is composed of three fusion modules, specifically as follows. First, the model extracts deep semantic features of SMILES strings through ChemBERTa-2 and integrates them with molecular fingerprints by using gated fusion to enhance the multi-modal information of drugs. In addition, it employs a residual fusion mechanism to integrate the global embeddings from ESM-2 with the local features obtained by the k-mer and principal component analysis (PCA) method. Finally, a hierarchical attention mechanism is employed to extract multi-granularity features from both drug SMILES strings and protein sequences. Comparative analysis with other mainstream methods on the Davis, KIBA, and BindingDB datasets reveals that the MGF-DTA model exhibits outstanding performance advantages. Further, ablation studies confirm the effectiveness of the model components and case study illustrates its robust generalization capability.