Research Progress of Drug Target Interaction Prediction Based on Machine Learning

In recent years, with the continuous breakthrough of computer hardware capability, software efficiency and data abundance, the applications of artificial intelligence technology represented by machine learning have been continuously expanded and integrated, which has greatly promoted the development in fields of biology, medicine, pharmacy, and especially drug R&D. Among those technology advancements, the identification of drug-target interactions (DTI) is an important problem in the field of drug R&D and a popular research direction for the cross-integration of artificial intelligence technology. As the source of innovative drug development, drug-target interaction prediction can provide high-probability potential drug targets for biological experiments, thereby increasing the rate of lead compound discovery, increasing the success rate of late-stage drug development and shortening the total development cycle. Researchers have already done a lot of work in constructing the prediction methods of drug-target interactions by building databases, developing software and establishing machine learning algorithms. In most works, data are transformed into feature vectors or similarities, and then suitable machine learning methods are employed to build predictive models. This paper introduces the basic process and reviews the research progress of drug-target interaction prediction based on machine learning. In addition, the advantages and disadvantages of existing prediction methods are briefly summarized in order to facilitate the development of more efficient prediction algorithms and drug-target interaction prediction methods.