Accurate prediction of Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities. While wet experiments remain the most reliable method, they are time-consuming and resource-intensive, resulting in limited data availability that poses challenges for deep learning approaches. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. To overcome this challenge, we present the SSM-DTA framework, which incorporates three simple yet highly effective strategies: (1) A multi-task training approach that combines DTA prediction with masked language modeling (MLM) using paired drug-target data. (2) A semi-supervised training method that leverages large-scale unpaired molecules and proteins to enhance drug and target representations. This approach differs from previous methods that only employed molecules or proteins in pre-training. (3) The integration of a lightweight cross-attention module to improve the interaction between drugs and targets, further enhancing prediction accuracy. Through extensive experiments on benchmark datasets such as BindingDB, DAVIS, and KIBA, we demonstrate the superior performance of our framework. Additionally, we conduct case studies on specific drug-target binding activities, virtual screening experiments, drug feature visualizations, and real-world applications, all of which showcase the significant potential of our work. In conclusion, our proposed SSM-DTA framework addresses the data limitation challenge in DTA prediction and yields promising results, paving the way for more efficient and accurate drug discovery processes. Our code is available at $\href{https://github.com/QizhiPei/SSM-DTA}{Github}$.

翻译：准确预测药物-靶标亲和力（DTA）在早期药物发现中至关重要，有助于识别能够有效作用于特定靶标并调控其活性的药物。尽管湿实验仍是最可靠的方法，但其耗时且资源密集，导致数据可用性有限，给深度学习方法带来挑战。现有方法主要基于现有DTA数据开发技术，未能充分解决数据稀缺问题。为应对这一挑战，我们提出SSM-DTA框架，该框架整合了三种简单高效的策略：（1）一种多任务训练方法，通过配对药物-靶标数据将DTA预测与掩码语言建模（MLM）相结合；（2）一种半监督训练方法，利用大规模未配对的分子和蛋白质增强药物与靶标的表征，这与以往仅在预训练中使用分子或蛋白质的方法不同；（3）集成轻量级交叉注意力模块，改善药物与靶标之间的交互，进一步提升预测精度。通过在BindingDB、DAVIS和KIBA等基准数据集上的广泛实验，我们证明了该框架的优越性能。此外，我们还针对特定药物-靶标结合活性进行了案例研究、虚拟筛选实验、药物特征可视化及实际应用验证，所有结果均彰显了本工作的巨大潜力。综上，我们提出的SSM-DTA框架解决了DTA预测中的数据稀缺挑战并取得了优异结果，为更高效、更准确的药物发现过程铺平了道路。我们的代码已发布于$\href{https://github.com/QizhiPei/SSM-DTA}{Github}$。