School of Pharmacy, Lanzhou University, Gansu 730000, China.
Huawei Technologies Co., Ltd., Hangzhou 310000, China.
J Chem Inf Model. 2024 Jul 22;64(14):5646-5656. doi: 10.1021/acs.jcim.4c00403. Epub 2024 Jul 8.
Predicting drug-target interactions (DTIs) is one of the crucial tasks in drug discovery, but traditional wet-lab experiments are costly and time-consuming. Recently, deep learning has emerged as a promising tool for accelerating DTI prediction due to its powerful performance. However, the models trained on limited known DTI data struggle to generalize effectively to novel drug-target pairs. In this work, we propose a strategy to train an ensemble of models by capturing both domain-generic and domain-specific features (E-DIS) to learn diverse domain features and adapt them to out-of-distribution data. Multiple experts were trained on different domains to capture and align domain-specific information from various distributions without accessing any data from unseen domains. E-DIS provides a comprehensive representation of proteins and ligands by capturing diverse features. Experimental results on four benchmark data sets in both in-domain and cross-domain settings demonstrated that E-DIS significantly improved model performance and domain generalization compared to existing methods. Our approach presents a significant advancement in DTI prediction by combining domain-generic and domain-specific features, enhancing the generalization ability of the DTI prediction model.
预测药物-靶标相互作用（DTIs）是药物发现的关键任务之一，但传统的湿实验验 证是昂贵且耗时的。最近，深度学习由于其强大的性能而成为加速 DTI 预测的有前景的工具。然而，在有限的已知 DTI 数据上训练的模型难以有效地推广到新的药物-靶标对。在这项工作中，我们提出了一种通过捕捉领域通用和特定领域特征（E-DIS）来训练模型集合的策略，以学习不同的领域特征并将其适应于分布外的数据。多个专家在不同的领域进行训练，以从各种分布中捕捉和对齐特定于领域的信息，而无需访问任何来自未见领域的数据。E-DIS 通过捕捉多种特征，提供了对蛋白质和配体的全面表示。在四个基准数据集上进行的实验结果表明，在域内和跨域设置中，E-DIS 显著提高了模型性能和领域泛化能力，优于现有方法。我们的方法通过结合领域通用和特定领域的特征，在 DTI 预测中取得了重大进展，提高了 DTI 预测模型的泛化能力。
