Deng Moping, Wang Jian, Zhao Yiming, Zhao Yongjia, Cao Hao, Wang Zhuo
Shenyang Institute of Automation, Chinese Academy of Science, Shenyang, 110016, China.
University of Chinese Academy of Sciences, Beijing, 100049, China.
Sci Rep. 2025 Jan 20;15(1):2579. doi: 10.1038/s41598-025-86918-8.
Predicting drug-target interaction (DTI) stands as a pivotal and formidable challenge in pharmaceutical research. Many existing deep learning methods only learn the high-dimensional representation of ligands and targets on a small scale. However, it is difficult for the model to obtain the potential law of combining pockets or multiple binding sites on a large scale. To address this lacuna, we designed a large-kernel convolutional block for extracting large-scale sequence information and proposed a novel DTI prediction framework, named Rep-ConvDTI. The reparameterization method is introduced to help large-kernel convolutions capture small-scale information. We have also developed a gated attention mechanism to more efficiently characterize the interaction of drugs and targets. Extensive experiments demonstrate that Rep-ConvDTI achieves the most competitive performance against state-of-the-art baselines on the three benchmark datasets. Furthermore, we validated the potential of Rep-ConvDTI as a drug screening tool through model interpretative studies and drug screening experiments with cystathionine-β-synthase.
预测药物-靶点相互作用（DTI）是药物研究中一项关键且艰巨的挑战。许多现有的深度学习方法仅在小尺度上学习配体和靶点的高维表示。然而，模型很难在大尺度上获取结合口袋或多个结合位点的潜在结合规律。为了解决这一空白，我们设计了一个大内核卷积块来提取大规模序列信息，并提出了一种名为Rep-ConvDTI的新型DTI预测框架。引入了重参数化方法来帮助大内核卷积捕获小尺度信息。我们还开发了一种门控注意力机制，以更有效地表征药物与靶点的相互作用。大量实验表明，在三个基准数据集上，Rep-ConvDTI相对于最先进的基线方法取得了最具竞争力的性能。此外，我们通过模型解释性研究和针对胱硫醚-β-合酶的药物筛选实验，验证了Rep-ConvDTI作为药物筛选工具的潜力。
