Advanced machine-learning techniques in drug discovery. Issue 3 (March 2021)

• Record Type: Journal Article
• Authors: Elbadawi, Moe; Gaisford, Simon; Basit, Abdul W.
• Is Part Of: Drug discovery today. Volume 26:Issue 3(2021)
• Page Start: 769
• Page End: 777
• Publication Date: 2021-03
• Subjects: Drugs -- Design -- Periodicals; Drugs -- Research -- Periodicals; 615.1
• DOI: 10.1016/j.drudis.2020.12.003
• Languages: English
• ISSNs: 1359-6446

Abstract

Highlights: Machine learning techniques (MLTs) are progressing the drug discovery process. Conventional MLTs require large data, lack transparency and are not autonomous. Advanced techniques addressing these limitations are highlighted. Other emerging techniques are also presented. Abstract : The popularity of machine learning (ML) across drug discovery continues to grow, yielding impressive results. As their use increases, so do their limitations become apparent. Such limitations include their need for big data, sparsity in data, and their lack of interpretability. It has also become apparent that the techniques are not truly autonomous, requiring retraining even post deployment. In this review, we detail the use of advanced techniques to circumvent these challenges, with examples drawn from drug discovery and allied disciplines. In addition, we present emerging techniques and their potential role in drug discovery. The techniques presented herein are anticipated to expand the applicability of ML in drug discovery.