A Review of Drug-related Associations Prediction Based on Artificial Intelligence Methods

Background: Predicting drug-related associations is an important task in drug development and discovery. With the rapid advancement of high-throughput technologies and various biological and medical data, artificial intelligence (AI), especially progress in machine learning (ML) and deep learning (DL), has paved a new way for the development of drug-related associations prediction. Many studies have been conducted in the literature to predict drug-related associations. This study looks at various computational methods used for drug-related associations prediction with the hope of getting a better insight into the computational methods used. Methods: The various computational methods involved in drug-related associations prediction have been reviewed in this work. We have first summarized the drug, target, and disease-related mainstream public datasets. Then, we have discussed existing drug similarity, target similarity, and integrated similarity measurement approaches and grouped them according to their suitability. We have then comprehensively investigated drug-related associations and introduced relevant computational methods. Finally, we have briefly discussed the challenges involved in predicting drug-related associations. Results: We discovered that quite a few studies have used implemented ML and DL approaches for drug-related associations prediction. The key challenges were well noted in constructing datasets with reasonable negative samples, extracting rich features, and developing powerful prediction models or ensemble strategies. Conclusion: This review presents useful knowledge and future challenges on the subject matter with the hope of promoting further studies on predicting drug-related associations.