  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 $\href{https://github.com/QizhiPei/SSM-DTA}{Github}$.
