Drug Design for Malaria with Artificial Intelligence (AI)

Author:

Ghosh Bhaswar,Choudhuri Soham

Abstract

Malaria is a deadly disease caused by the plasmodium parasites. Approximately 210 million people get affected by malaria every year resulting in half a million deaths. Among several species of the parasite, Plasmodium falciparum is the primary cause of severe infection and death. Several drugs are available for malaria treatment in the market but plasmodium parasites have successfully developed resistance against many drugs over the years. This poses a serious threat to efficacy of the treatments and continuing discovery of new drug is necessary to tackle the situation, especially due to failure in designing an effective vaccine. People are now trying to design new drugs for malaria using AI technologies which can substantially reduce the time and cost required in classical drug discovery programs. In this chapter, we provide a comprehensive overview of a road map for several AI based computational techniques which can be implemented in a malaria drugs discovery program. Classical computers has limiting computing power. So, researchers are also trying to harness quantum machine learning to speed up the drug discovery processes.

Publisher

IntechOpen

Reference72 articles.

1. World Malaria Report 2020. https://www.who.int/news-room/fact-sheets/detail/malaria (2020)

2. White, Nicholas J. “Antimalarial drug resistance.” The Journal of clinical investigation vol. 113,8 (2004): 1084–92. doi:10.1172/JCI21682

3. Snow RW, Craig M, Deichmann U Marsh K: Estimating mortality, morbidity and disability due to malaria among Africa’s non-pregnant population. 1999, Bull WHO 77, 624–640. https://pubmed.ncbi.nlm.nih.gov/10516785/ [Accessed: 21 January 2021]

4. Breman JG, Egan A Keusch GT, The intolerable burden of malaria: a new look at the numbers. 2001, J Trop Med Hyg 64, (Suppl. 1–2), iv–vii https://core.ac.uk/reader/13114159?utm_source=linkout [Accessed: 21 January 2021]

5. Yamauchi LM, Coppi A, Snounou G Sinnis P (2007)Plasmodium sporozoites trickle out of the injection site. Cell Microbiol [Epub ahead of print]

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3