Abstract
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.
Highlights
Tens of thousands of known diseases threatening human health, and new ones are being added every year
Most target molecules are proteins, of which four protein families [kinases, G protein-coupled receptors (GPCRs), ion channels, and nuclear receptors] account for 44% of the target molecules, and 70% of the currently developed drugs are targeted to these four protein families
Feature engineering is a key concern in building machine learning models
Summary
Tens of thousands of known diseases threatening human health, and new ones are being added every year. Investigating drug–target interactions is an important step in the drug discovery process and can improve the success rate of new drug discovery (Chen et al, 2019; Huang et al, 2020; Zeng et al, 2020b). These signal the need to expend significant resources to find and test candidate compounds one by one during the drug development phase to confirm that they meet expectations, and demonstrate the importance of drug–target interaction prediction in the overall drug development process. Machine learning methods have been introduced into the prediction of drug–target interactions
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