The role of artificial intelligence in the battle against antimicrobial-resistant bacteria.

Affiliation

Lau HJ(1), Lim CH(2), Foo SC(1)(3), Tan HS(4)(5).
Author information:
(1)School of Science, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia.
(2)School of Information Technology, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia.
(3)Tropical Medicine and Biology Multidisciplinary Platform, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia.
(4)School of Science, Monash University Malaysia, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia. [Email]
(5)Tropical Medicine and Biology Multidisciplinary Platform, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia. [Email]

Abstract

Antimicrobial resistance (AMR) in bacteria is a global health crisis due to the rapid emergence of multidrug-resistant bacteria and the lengthy development of new antimicrobials. In light of this, artificial intelligence in the form of machine learning has been viewed as a potential counter to delay the spread of AMR. With the aid of AI, there are possibilities to predict and identify AMR in bacteria efficiently. Furthermore, a combination of machine learning algorithms and lab testing can help to accelerate the process of discovering new antimicrobials. To date, many machine learning algorithms for antimicrobial-resistance discovery had been created and vigorously validated. Most of these algorithms produced accurate results and outperformed the traditional methods which relied on sequence comparison within a database. This mini-review will provide an updated overview of antimicrobial design workflow using the latest machine-learning antimicrobial discovery algorithms in the last 5 years. With this review, we hope to improve upon the current AMR identification and antimicrobial development techniques by introducing the use of AI into the mix, including how the algorithms could be made more effective.