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Abstract <p>Accurate prediction of drug-target affinity (DTA) is crucial for advancing drug discovery and optimizing experimental processes. Traditional DTA models often rely on handcrafted features or structural data, which can limit their generalizability and scalability. In this study, we propose a novel, sequence-centric approach for DTA prediction that leverages pretrained large language models (LLMs), namely ChemBERTa and ESM2, to encode protein and molecule sequences. These models produce semantically rich embeddings without the need for structural data. We introduce a customized Residual Inception architecture that efficiently integrates these sequence embeddings through multi-scale convolutions and residual connections, significantly improving prediction accuracy. Our method is evaluated on benchmark datasets Davis, KIBA, and BindingDB, achieving state-of-the-art performance with MSE = 0.182 and CI = 0.915 on Davis, MSE = 0.135 and CI = 0.902 on KIBA, and MSE = 0.467 and CI = 0.888 on BindingDB. These results highlight the potential of sequence-based approaches to provide scalable, accurate, and robust solutions for DTA prediction, offering valuable insights into drug-target interactions even in data-sparse settings.</p> Scientific contribution <p>The combination of pretrained language models and a lightweight neural architecture paves the way for more effective and adaptable DTA frameworks in real-world drug discovery applications.</p>

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Structure-free drug–target affinity prediction using protein and molecule language models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月3日

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  • Amir Hallaji Bidgoli,
  • Morteza Mahdavi,
  • Hamed Malek

摘要

Abstract

Accurate prediction of drug-target affinity (DTA) is crucial for advancing drug discovery and optimizing experimental processes. Traditional DTA models often rely on handcrafted features or structural data, which can limit their generalizability and scalability. In this study, we propose a novel, sequence-centric approach for DTA prediction that leverages pretrained large language models (LLMs), namely ChemBERTa and ESM2, to encode protein and molecule sequences. These models produce semantically rich embeddings without the need for structural data. We introduce a customized Residual Inception architecture that efficiently integrates these sequence embeddings through multi-scale convolutions and residual connections, significantly improving prediction accuracy. Our method is evaluated on benchmark datasets Davis, KIBA, and BindingDB, achieving state-of-the-art performance with MSE = 0.182 and CI = 0.915 on Davis, MSE = 0.135 and CI = 0.902 on KIBA, and MSE = 0.467 and CI = 0.888 on BindingDB. These results highlight the potential of sequence-based approaches to provide scalable, accurate, and robust solutions for DTA prediction, offering valuable insights into drug-target interactions even in data-sparse settings.

Scientific contribution

The combination of pretrained language models and a lightweight neural architecture paves the way for more effective and adaptable DTA frameworks in real-world drug discovery applications.