LAUSR

Simple Summary Determining the drug–target relationships is the key to modern drug development, and it plays a crucial role in drug side effects research and individual treatment. However, traditional drug target identification by bio-experimental methods is often difficult to develop due to limitations of precision, flux and cost. With the rapid development of bioinformatics and computational biology, the computer-assisted drug–target interaction (DTIs) prediction approach has attracted great attention by researchers as an accurate and quick mean of drug target recognition. In this study, combined with the protein sequence information and drug molecular structure information, a prediction method of DTIs based on machine learning is developed to achieve the purpose of locking targets and saving costs for new drug research. Abstract As the basis for screening drug candidates, the identification of drug–target interactions (DTIs) plays a crucial role in the innovative drugs research. However, due to the inherent constraints of small-scale and time-consuming wet experiments, DTI recognition is usually difficult to carry out. In the present study, we developed a computational approach called RoFDT to predict DTIs by combining feature-weighted Rotation Forest (FwRF) with a protein sequence. In particular, we first encode protein sequences as numerical matrices by Position-Specific Score Matrix (PSSM), then extract their features utilize Pseudo Position-Specific Score Matrix (PsePSSM) and combine them with drug structure information-molecular fingerprints and finally feed them into the FwRF classifier and validate the performance of RoFDT on Enzyme, GPCR, Ion Channel and Nuclear Receptor datasets. In the above dataset, RoFDT achieved 91.68%, 84.72%, 88.11% and 78.33% accuracy, respectively. RoFDT shows excellent performance in comparison with support vector machine models and previous superior approaches. Furthermore, 7 of the top 10 DTIs with RoFDT estimate scores were proven by the relevant database. These results demonstrate that RoFDT can be employed to a powerful predictive approach for DTIs to provide theoretical support for innovative drug discovery.