SMT-DTA: Improving Drug-Target Affinity Prediction with Semi-supervised Multi-task Training

@article{Pei2022SMTDTAID,
  title={SMT-DTA: Improving Drug-Target Affinity Prediction with Semi-supervised Multi-task Training},
  author={Qizhi Pei and Lijun Wu and Jinhua Zhu and Yingce Xia and Shufang Xia and Tao Qin and Haiguang Liu and Tie-Yan Liu},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.09818}
}
Drug-Target Affinity (DTA) prediction is an essential task for drug discovery and pharmaceutical research. Accurate predictions of DTA can greatly benefit the design of new drug. As wet experiments are costly and time consuming, the supervised data for DTA prediction is extremely limited. This seriously hinders the application of deep learning based methods, which require a large scale of supervised data. To address this challenge and improve the DTA prediction accuracy, we propose a framework… 

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