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

Abstract

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 intelli-gence (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 re-viewed in this work. We have first summarized the drug, target, and disease-related mainstream public da-tasets. Then, we have discussed existing drug similarity, target similarity, and integrated similarity measurement approaches and grouped them according to their suita-bility. We have then comprehensively investigated drug-related associations and introduced relevant computa-tional methods. Finally, we have briefly discussed the challenges involved in predicting drug-related associa-tions. Result: 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 neg-ative 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 as-sociations.