Ye Qing, Sun Yaxin
School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China.
School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Normal University, Jinhua, China.
PeerJ Comput Sci. 2025 Jan 3;11:e2622. doi: 10.7717/peerj-cs.2622. eCollection 2025.
Computational drug-target affinity prediction is important for drug screening and discovery. Currently, self-supervised learning methods face two major challenges in drug-target affinity prediction. The first difficulty lies in the phenomenon of sample mismatch: self-supervised learning processes drug and target samples independently, while actual prediction requires the integration of drug-target pairs. Another challenge is the mismatch between the broadness of self-supervised learning objectives and the precision of biological mechanisms of drug-target affinity (., the induced-fit principle). The former focuses on global feature extraction, while the latter emphasizes the importance of local precise matching. To address these issues, an adaptive self-supervised learning-based drug-target affinity prediction (ASSLDTA) was designed. ASSLDTA integrates a novel adaptive self-supervised learning (ASSL) module with a high-level feature learning network to extract the feature. The ASSL leverages a large amount of unlabeled training data to effectively capture low-level features of drugs and targets. Its goal is to maximize the retention of original feature information, thereby bridging the objective gap between self-supervised learning and drug-target affinity prediction and alleviating the sample mismatch problem. The high-level feature learning network, on the other hand, focuses on extracting effective high-level features for affinity prediction through a small amount of labeled data. Through this two-stage feature extraction design, each stage undertakes specific tasks, fully leveraging the advantages of each model while efficiently integrating information from different data sources, providing a more accurate and comprehensive solution for drug-target affinity prediction. In our experiments, ASSLDTA is much better than other deep methods, and the result of ASSLDTA is significantly increased by learning adaptive self-supervised learning-based features, which validates the effectiveness of our ASSLDTA.
计算药物 - 靶点亲和力预测对于药物筛选和发现至关重要。目前，自监督学习方法在药物 - 靶点亲和力预测中面临两个主要挑战。第一个困难在于样本不匹配现象：自监督学习独立处理药物和靶点样本，而实际预测需要整合药物 - 靶点对。另一个挑战是自监督学习目标的宽泛性与药物 - 靶点亲和力生物学机制的精确性（即诱导契合原理）之间的不匹配。前者侧重于全局特征提取，而后者强调局部精确匹配的重要性。为了解决这些问题，设计了一种基于自适应自监督学习的药物 - 靶点亲和力预测方法（ASSLDTA）。ASSLDTA将一个新颖的自适应自监督学习（ASSL）模块与一个高级特征学习网络集成以提取特征。ASSL利用大量未标记的训练数据来有效捕获药物和靶点的低级特征。其目标是最大程度地保留原始特征信息，从而弥合自监督学习与药物 - 靶点亲和力预测之间的目标差距并缓解样本不匹配问题。另一方面，高级特征学习网络专注于通过少量标记数据提取用于亲和力预测的有效高级特征。通过这种两阶段特征提取设计，每个阶段承担特定任务，充分利用每个模型的优势，同时有效地整合来自不同数据源的信息，为药物 - 靶点亲和力预测提供更准确和全面的解决方案。在我们的实验中，ASSLDTA比其他深度方法要好得多，并且通过学习基于自适应自监督学习的特征，ASSLDTA的结果显著提高，这验证了我们的ASSLDTA的有效性。
