Heterogeneous networks in drug-target interaction prediction
It addresses the problem of high resource demands in drug discovery for researchers and practitioners, but is incremental as it is a survey paper summarizing existing work.
This survey reviews graph machine learning methods for predicting drug-target interactions, aiming to reduce the time and cost of drug discovery by narrowing experimental search spaces, covering frameworks, contributions, datasets, and source codes from 2020 to 2024.
Drug discovery requires a tremendous amount of time and cost. Computational drug-target interaction prediction, a significant part of this process, can 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 framework, main contribution, datasets, and their source codes. The selected papers were mainly published from 2020 to 2024. Prior to discussing papers, we briefly introduce the datasets commonly used with these methods and measurements to assess their performance. Finally, future challenges and some crucial areas that need to be explored are discussed.