Predicting Drug-Target Interactions Binding Affinity by Using Dual Updating Multi-task Learning

The prediction of drug-target interactions binding affinity has received great attention in the field of drug discovery. The prediction models based on deep neural networks have shown the favorable performance. However, existing models mainly depend on large-scale labelled data and are unfit for the innovative drug discovery study because of local optimum on pre-training. This paper proposes a new deep learning model to predict the drug-target interaction binding affinity. By using multi-task learning, unsupervised pre-training tasks of drugs and proteins are combined with the drug-target prediction task for preventing local optimum on pre-training. And then the MAML based updating strategy is adopted to deal with the task gap problem in the traditional fine-tuning process. Experimental results show that the proposed model is superior to the existing methods on predicting the affinity between new drugs and new targets.