Computational methods for the prediction of drug-target interactions from drug fingerprints and protein sequences by stacked auto-encoder deep neural network

Identifying the interaction among drugs and target proteins is an important area of drug research, which provides a broad prospect for low-risk and faster drug development. However, due to the limitations of traditional experiments when revealing drug-protein interactions (DTIs), the screening of targets not only takes a lot of time and money, but also has high false-positive and false-negative rates. Therefore, it is imperative to develop effective automatic computational methods to accurately predict DTIs in the post-genome era. In this paper, we propose a new computational method for predicting DTIs from drug molecular structure and protein sequence by using the stacked auto-encoder of deep learning which can adequately extracts the raw data information. The proposed method has the advantage that it can automatically mine the hidden information from protein sequences and generate highly representative features through iterations of multiple layers. The feature descriptors are then constructed by combining the molecular substructure fingerprint information, and fed into the rotation forest for accurate prediction. The experimental results of 5-fold cross-validation indicate that the proposed method achieves superior performance on golden standard datasets (enzymes, ion channels, GPCRs and nuclear receptors) with accuracy of 0.9414, 0.9116, 0.8669 and 0.8056, respectively. We further comprehensively explore the performance of the proposed method by comparing it with other feature extraction algorithm, state-of-the-art classifier and other excellent methods on the same dataset. The excellent comparison results demonstrate that the proposed method is highly competitive when predicting drug-target interactions.