Incorporating FPConv-DTI Deep Learning Network and Borderline-SMOTE Algorithm for Predicting Drug-Target Interactions

Drug-target interactions prediction is of great significance in medical and biological research, but traditional laboratory methods have disadvantages such as high cost and time-consuming. Therefore, in recent years, deep learning, similarity calculation methods and other methods are becoming more and more widely applied to related research. This paper proposes an improved deep learning model, named as FPConv-DTI, which uses the fingerprint information of drug and the evolution feature information of protein based on a convolutional neural network. The Borderline-SMOTE algorithm is also used to generate new positive examples for training to solve the imbalance problem, and combines the number of sample data to process the input differently. Experiments have been carried out with four standard datasets and Drugbank dataset. The results show that compared with other methods, our method has greatly improvement for predicting drug-target interactions. In addition, some COVID-19 drugs are also predicted with the best-performing model, which shows that FPConv-DTI model is the potential for practical drug prediction.