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Predicting protein-ligand binding affinity (PLBA) is a crucial task in drug discovery. However, the performance and practicality of existing deep learning models are limited due to the scarcity of high-quality data. Protein language models (PLMs) and chemical language models (CLMs) offer a promising alternative for molecular representation, with the potential to significantly improve predictive accuracy. Nevertheless, the lack of effective integration frameworks has limited the full potential of PLMs and CLMs for PLBA and existing models often overlook the heterogeneity between language models and downstream tasks. To address these issues, we propose two frameworks utilizing parameter-efficient fine-tuning (PEFT) methods for PLBA prediction. The first framework (KLG) integrates Knowledge learned from Language models into advanced Geometric graph networks. The second framework (LCB) leverages Language models to generate representations of proteins and ligands, employing Cross-attention mechanisms and a four-Branch neural network to process the features. To thoroughly explore the potential of PLMs and CLMs in the PLBA task, we fine-tune them in both frameworks using various PEFT methods, including Adapters, LoRA, BitFit, and QLoRA. Experimental results demonstrate the effectiveness and practicality of both frameworks, and we hope our work will inspire further applications of PLMs and CLMs in drug discovery.

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Enhancing Protein-Ligand Binding Affinity Prediction via Parameter-Efficient Fine-Tuning of Protein and Chemical Language Models

  • 会议论文

  • 发表日期：

    2026年

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  • Ruikang Li,
  • Jiaxian Yan,
  • Kai Zhang,
  • Yanjiang Chen,
  • Qi Liu,
  • Min Gao,
  • Enhong Chen

摘要

Predicting protein-ligand binding affinity (PLBA) is a crucial task in drug discovery. However, the performance and practicality of existing deep learning models are limited due to the scarcity of high-quality data. Protein language models (PLMs) and chemical language models (CLMs) offer a promising alternative for molecular representation, with the potential to significantly improve predictive accuracy. Nevertheless, the lack of effective integration frameworks has limited the full potential of PLMs and CLMs for PLBA and existing models often overlook the heterogeneity between language models and downstream tasks. To address these issues, we propose two frameworks utilizing parameter-efficient fine-tuning (PEFT) methods for PLBA prediction. The first framework (KLG) integrates Knowledge learned from Language models into advanced Geometric graph networks. The second framework (LCB) leverages Language models to generate representations of proteins and ligands, employing Cross-attention mechanisms and a four-Branch neural network to process the features. To thoroughly explore the potential of PLMs and CLMs in the PLBA task, we fine-tune them in both frameworks using various PEFT methods, including Adapters, LoRA, BitFit, and QLoRA. Experimental results demonstrate the effectiveness and practicality of both frameworks, and we hope our work will inspire further applications of PLMs and CLMs in drug discovery.