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.

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.

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