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Journal of Advances in Medical Sciences and Artificial Intelligence

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AI in Drug Discovery for Antimicrobial Resistance: Combating the Silent Pandemic
Authors
Mia Md Tofayel Gonee Manik 1 ,  Sadia Islam Nilima 2 ,  Md Redwan Hussain 3 ,  Jarin Tias Meraj 3 ,  Toufiqur Rahman Tonmoy 4 ,  Rokeya Khatun Shorna 4 ,  Oli Ahammed Sarker 3
Author's Details

Name: Mia Md Tofayel Gonee Manik

Email: m.manik.407@westcliff.edu

Department: College of Business

Affiliation Number: 1

Address: Irvine, CA 92614, USA

https://doi.org/10.63471/jamsai24001
Affiliations

1 College of Business, Westcliff University, Irvine, CA 92614, USA

2 Doctorate in Business Administration (DBA), International American University, Los Angeles, CA 90010, USA

3 Department of Computer Science and Engineering, Daffodil International University, Birulia, Savar, Dhaka-1216

4 Department of Computer Science and Engineering, Jahangirnagar University, Kalabagan Rd, Savar 1342, Dhaka, Bangladesh

Abstract
Antimicrobial resistance (AMR) is a serious threat to global health and could render efforts aimed at keeping antibiotics working and killing millions of people each year ineffective. The presence of these weaknesses and our deficiencies in predicting potential drug targets opens new machine-learning fronts to the field of drug discovery, and they will likely deliver novel antibiotic drug leads (as well as repurposing of existing drugs). AI combating AMR: This study applies to algorithmic identification of effective compounds, prediction of resistance patterns, and computational drug repurposing. The results show that AI can do a lot to bring the discovery process and spending to an optimum state while improving specificity in combating AMR. We can integrate AI into drug discovery to slow the silent AMR pandemic

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This article is Open Access CC BY-NC
Article Information
Article Type
Research Paper
Submitted
03 July, 2024
Revised
31 July, 2024
Accepted
15 August, 2024
Online First
22 August, 2024
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