Identifying interactions among drug compounds and target proteins are the basis of drug research, and plays a crucial in drug discovery. However, determining drug-target interactions (DTIs) and potential protein-compound interactions by biological experiment based method alone is a very complicated, expensive and time-consuming process. Hence, there is an intense motivation to design in silico prediction methods to overcome these obstacles. In this work, we designed a novel in silico strategy to predict proteome-scale DTIs based on the assumption that DTI pairs can be expressed through the evolutionary information derived from frequency profiles and drugs' structural properties. To achieve this, drug molecules are encoded into the substructure fingerprints to represent certain fragments; target proteins are first converted into Position-Specific Scoring Matrix (PSSM), and then encoded as 2-dimensional Principal Component Analysis (2DPCA) descriptors. In the prediction phase, the feature weighted Rotation Forest (RF) classifier is used to estimate whether drug and target interact with each other on four benchmark datasets, including Enzymes, Ion Channels, GPCRs and Nuclear Receptors. The prediction accuracy of cross-validation on the four datasets is 95.40%, 88.82%, 85.67%, and 82.22%, respectively. In order to have a clearer assessment of the proposed approach, we compared it with the Discrete Cosine Transform (DCT) descriptors model, Support Vector Machine (SVM) classifier model and existing excellent approaches, including DBSI, NetCBP, KBMF2K, SIMCOMP and RFDT. The excellent results of experimental indicated that the proposed approach can effectively improve the DTI prediction accuracy and can be used as a practical tool for the research and design of new drugs. This article is protected by copyright. All rights reserved.