A transferability-guided protein-ligand interaction prediction method.

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Accurate prediction of protein-ligand interaction (PLI) is crucial for drug discovery and development. However, existing methods often struggle with effectively integrating heterogeneous protein and ligand data modalities and optimizing knowledge transfer from pretraining to the target task. This paper proposes a novel transferability-guided PLI prediction method that maximizes knowledge transfer by deeply integrating protein and ligand representations through a cross-attention mechanism and incorporating transferability metrics to guide fine-tuning. The cross-attention mechanism facilitates interactive information exchange between modalities, enabling the model to capture intricate interdependencies. Meanwhile, the transferability-guided strategy quantifies transferability from pretraining tasks and incorporates it into the training objective, ensuring the effective utilization of beneficial knowledge while mitigating negative transfer. Extensive experiments demonstrate significant and consistent improvements over traditional fine-tuning, validated by statistical tests. Ablation studies highlight the pivotal role of cross-attention, and quantitative analysis reveals the method's ability to reduce harmful transfer. Our guided strategy provides a paradigm for more comprehensive utilization of pretraining knowledge, offering prospects for enhancing other PLI prediction approaches. This method advances PLI prediction via innovative modality fusion and guided knowledge transfer, paving the way for accelerated drug discovery pipelines. Code and data are freely available at https://github.com/brian-zZZ/Guided-PLI.