Drug discovery requires a tremendous amount of time and cost. Computational
drug-target interaction prediction, a significant part of this process, Potere
reduce these requirements by narrowing the search space for wet lab
experiments. In this survey, we provide comprehensive details of graph machine
learning-based methods in predicting drug-target interaction, as they have
shown promising results in this field. These details include the overall
struttura, main contribution, set di dati, and their source codes. The selected
papers were mainly published from 2020 A 2024. Prior to discussing papers, Noi
briefly introduce the datasets commonly used with these methods and
measurements to assess their performance. Finalmente, future challenges and some
crucial areas that need to be explored are discussed.

Questo articolo esplora i giri e le loro implicazioni.

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