DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method - Zheng, Jie; Xiao, Xuan; Qiu, Wang-Ren (Frontiers in Genetics, 2022)
Drug–target interactions (DTIs) believed to be an essential part of genomic drug discovery, and computational prediction of DTIs can accelerate to find lead drug for target, which can make up for the lack of time-consuming and expensive wet-lab techniques. Currently, many computational methods predict DTIs based on sequential composition or physicochemical properties of drug and target, but further efforts are needed to improve them. In this article, we proposed a new sequence-based method for accurately identifying DTIs. For target protein, we explore the use of pre-trained Bidirectional Encoder Representations from Transformers (BERT) to extract sequence features, which can provide unique and valuable pattern information. For drug molecules, Discrete Wavelet Transform (DWT) is employed for generate information from drug molecular fingerprint FP2. The feature vectors of the DTI pair are concatenated and input into BRL block and CNN model to extract DTIs features further. Subsequently, a BRL block is used as prediction engine. After optimizing the model based on contrastive loss and cross-entropy loss, it gave prediction accuracies of the target families of GPCRs, ion channels, enzymes, and nuclear receptors up to 90.1%, 94.7%, 94.9% and 89%, which indicated that the proposed method can outperforms the existing predictors. To make it as convenient as possible for researchers, a web-server for the new predictor is freely accessible at: https://bioinfo.jcu.edu.cn/dtibert or http://121.36.221.79/dtibert/ . The proposed method may also be a potential option for other DITs.