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In Frontiers in genetics ; h5-index 62.0

Exploring drug-target interactions by biomedical experiments requires a lot of human, financial, and material resources. To save time and cost to meet the needs of the present generation, machine learning methods have been introduced into the prediction of drug-target interactions. The large amount of available drug and target data in existing databases, the evolving and innovative computer technologies, and the inherent characteristics of various types of machine learning have made machine learning techniques the mainstream method for drug-target interaction prediction research. In this review, details of the specific applications of machine learning in drug-target interaction prediction are summarized, the characteristics of each algorithm are analyzed, and the issues that need to be further addressed and explored for future research are discussed. The aim of this review is to provide a sound basis for the construction of high-performance models.

Xu Lei, Ru Xiaoqing, Song Rong


data, drug development, drug–target interactions, features, machine learning, task algorithms