Drug Target Interaction Prediction using Multitask Learning and Coattention Various machine learning models have been proposed as costeffective means to predict DrugTarget Interactions (DTI). Most existing researches treat DTI prediction either as a classification task (i.e. output negative or positive labels to indicate existence of interaction) or as a regression task (i.e. output numerical values as the strength of interaction). However, classifiers are more prone to higher bias and regression models tend to overfit the training data to generate large variance. In this paper, we explore to balance the bias and variance by a multitask learning framework. We propose an architecture to both predict accurate values of strength of interaction and decide correct boundary between positive and negative interactions. Furthermore, the two tasks are performed on a shared feature representation, which is learnt using a coattention mechanism. Comprehensive experiments demonstrate that the proposed method significantly outperforms stateoftheart methods.

AttentionDTA: prediction of drugxe2x80x93target binding affinity using attention model In bioinformatics, machine learningbased prediction of drugtarget interaction (DTI) plays an important role in virtual screening of drug discovery. DTI prediction, which have been treated as a binary classification problem, depends on the concentration of two molecules, the interaction between two molecules, and other factors. The degree of affinity between a drug molecule (such as a drug compound) and a target molecule (such as a receptor or protein kinase) reflects how tightly the drug binds to a particular target and is quantified by the measurement which can reflect more detailed and specific information than binary relationship. In this study, we proposed an endtoend model, named AttentionDTA, based on deep learning, which associates attention mechanism to predict the binding affinity of DTI. The novelty in this work is to use attentional mechanisms to consider which subsequences in a protein are more important for a drug and which subsequences in a drug are more important for a protein when predicting its affinity. So that the representational ability of the model is stronger. The model uses onedimensional Convolution Neural Networks (1DCNNs) to extract the abstract information of drug and protein, and makes the drug and protein representations mutually adapt through the attention mechanisms. We evaluate our model on two established drugtarget affinity benchmark datasets, Davis and KIBA. The model outperforms DeepDTA, a stateoftheart deep learning method for drugxe2x80x93target binding affinity prediction, with better Mean Squared Error (MSE), Concordance Index (CI),  r_m2 , and Area Under Precision Recall Curve (AUPR). Our results show that the attentionbased model can effectively extract effective representations by calculating the weight of the representation between the drug and the protein. Finally, we visualize the attention weight. It proves our model can obtain the information of binding sites.