DNN-DTIs: Improved drug-target interactions prediction using XGBoost feature selection and deep neural network. (September 2021) - Chen, Cheng; Shi, Han; Jiang, Zhiwen; Salhi, Adil; Chen, Ruixin; Cui, Xuefeng; Yu, Bin
Abstract: Analysis and prediction of drug-target interactions (DTIs) play an important role in understanding drug mechanisms, as well as drug repositioning and design. Machine learning (ML)-based methods for DTIs prediction can mitigate the shortcomings of time-consuming and labor-intensive experimental approaches, while providing new ideas and insights for drug design. We propose a novel pipeline for predicting drug-target interactions, called DNN-DTIs. First, the target information is characterized by a number of features, namely, pseudo-amino acid composition, pseudo position-specific scoring matrix, conjoint triad composition, transition and distribution, Moreau-Broto autocorrelation, and structural features. The drug compounds are subsequently encoded using substructure fingerprints. Next, eXtreme gradient boosting (XGBoost) is used to determine the subset of non-redundant features of importance. The optimal balanced set of sample vectors is obtained by applying the synthetic minority oversampling technique (SMOTE). Finally, a DTIs predictor, DNN-DTIs, is developed based on a deep neural network (DNN) via a layer-by-layer learning scheme. Experimental results indicate that DNN-DTIs achieves better performance than other state-of-the-art predictors with ACC values of 98.78%, 98.60%, 97.98%, 98.24% and 98.00% on Enzyme, Ion Channels (IC), GPCR, Nuclear Receptors (NR) and Kuang's datasets. Therefore, the accurate prediction performance of DNN-DTIs makes it a favored choice for contributing to the study of DTIs, especially drug repositioning. Highlights: A novel pipeline called DNN-DTIs for drug-target interactions prediction. We firstly perform multi-information fusion via PseAAC, PsePSSM, CTD, CT, NMBroto, structure feature and molecular fingerprint. The XGBoost is firstly employed to determine optimal features for DTIs prediction. We use deep neural network as classifier to predict DTIs via layer-by-layer learning, which can obtain high-level features. DNN-DTIs model has good generalization ability on DTIs network prediction.