Drug-target interaction prediction with tree-ensemble learning and output space reconstruction

摘要

Predicting accurately drug-target interactions (DTI) is vital for the development of new drugs. Accurate and efficient identification of interactions between drugs and target proteins can accelerate the drug development process and reduce the required cost. In addition, the identification of drug-target interactions can unveil hidden drug or protein functions and shed light to enigmatic disease pathology mechanisms [ ]. It can also provide scientists with insights which help in foreseeing adverse effects of drugs [ , ]. Furthermore, apart from discovering new drugs, DTI prediction can also leverage drug repositioning [ , – ], which aims at revealing new uses for already approved drugs. However, despite the persisting efforts made by the scientific community, experimentally identifying DTIs remains extremely demanding in terms of both time and expenses [ , ]. The employment of computational methods and especially machine learning for in silico DTI prediction is thereby crucial for drug discovery and repositioning. Machine learning models can direct experiments, reveal latent patterns in large scale drug or protein data collections and extract unprecedented knowledge in drug-target networks.