RFDTI: Using Rotation Forest with Feature Weighted for Drug-Target Interaction Prediction from Drug Molecular Structure and Protein Sequence

Abstract

The identification and prediction of Drug-Target Interactions (DTIs) is the basis for screening drug candidates, which plays a vital role in the development of innovative drugs. However, due to the time-consuming and high cost constraints of biological experimental methods, traditional drug target identification technologies are often difficult to develop on a large scale. Therefore, in silico methods are urgently needed to predict drug-target interactions in a genome-wide manner. In this article, we design a new in silico approach, named RFDTI to predict the DTIs combine Feature weighted Rotation Forest (FwRF) classifier with protein amino acids information. This model has two outstanding advantages: a) using the fusion data of protein sequence and drug molecular fingerprint, which can fully carry information; b) using the classifier with feature selection ability, which can effectively remove noise information and improve prediction performance. More specifically, we first use Position-Specific Score Matrix (PSSM) to numerically convert protein sequences and utilize Pseudo Position-Specific Score Matrix (PsePSSM) to extract their features.Then a unified digital descriptor is formed by combining molecular fingerprints representing drug information. Finally, the FwRF is applied to implement on Enzyme, Ion Channel, GPCR, and Nuclear Receptor data sets. The results of the five-fold cross-validation experiment show that the prediction accuracy of this approach reaches 91.68%, 88.11%, 84.72% and 78.33% on four benchmark data sets, respectively. To further validate the performance of the RFDTI, we compare it with other excellent methods and Support Vector Machine (SVM) model. In addition, 7 of the 10 highest predictive scores in predicting novel DTIs were validated by relevant databases. The experimental results of cross-validation indicated that RFDTI is feasible in predicting the relationship among drugs and target, and can provide help for the discovery of new candidate drugs.

Ensemble Learning Prediction of Drug-Target Interactions Using GIST Descriptor Extracted from PSSM-Based Evolutionary Information

“…Until now, numerous computational methods have been proposed for predicting the DTI. In our study, we made a performance comparison between the proposed method and the other four existing methods that include NetCBP [ 47 ], Mousavian et al's [ 48 ], Li et al's [ 21 ], and RFDTI [ 49 ]; these methods were also employed the five-fold cross-validation on enzyme , ion channel , GPCRs , and nuclear receptor dataset, respectively. The differences of them were the different feature extractions and classifiers adopted.…”

“…From Table 6, we can see that the average results of the SVM classifier are lower than the performance of the proposed method. The prediction result also shows that the performance of the RF classifier is better than the performance [21], and RFDTI [49]; these methods were also employed the five-fold cross-validation on enzyme, ion channel, GPCRs, and nuclear receptor dataset, respectively. The differences of them were the different feature extractions and classifiers adopted.…”