EventsMOL2NET'22, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 8th ed.
Event submissions
Published
with-doi10.3390/mol2net-08-13888
This submission belongs to the session 01. CHEMBIO.INFO-08: Cheminfo., Chemom., Comput. Chem. & Bioinfo., Congress München, GR-Cambridge, UK-Ch. Hill, USA, 2022. of the event MOL2NET'22, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 8th ed.
Published date
21 Dec, 2022
Academic Editor
author-avatarHumbert G. Díaz
Citation
Karel Diéguez-Santana, Gabriel Mazón-Ortiz, Galo Cerda-Mejía, Juan M. Ruso, Artificial intelligence and machine learning in Leishmania drug discovery, in Proceedings of MOL2NET'22, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 8th ed., 1 January–15 January 2023, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-08-13888
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Artificial intelligence and machine learning in Leishmania drug discovery

Gabriel Mazón-Ortiz 1,2
image
Galo Cerda-Mejía 1,2
Karel Diéguez-Santana 1
Juan M. Ruso 2
1. Universidad Regional Amazónica Ikiam, Parroquia Muyuna km 7 vía Alto Tena, 150150, Tena-Napo, Ecuador
2. Soft Matter and Molecular Biophysics Group, Department of Applied Physics, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
Abstract

Leishmaniasis is a vector-borne parasite that affects 700,000–1 million people and kills 26,000–65,000 annually. This work presents a brief, comprehensive review of AI/ML studies performed in leishmania drug discovery. In this study, research was carried out using the Scopus and Web of Science (WoS) databases from 2013 to 2022. Between the two databases, 28 documents were found, eight of which were duplicates; hence, a total of 20 articles were analysed. Of these, nine were research articles, 10 review articles, and one editorial document. Current Topics in Medicinal Chemistry was the only journal that received more than one paper (three papers in total). The available literature on the topic is limited. The most relevant articles selected from the two databases, WoS and Scopus, provided an overview of the scientific topic (stages of drug discovery for leishmaniasis using AI/ML). This brief review can also serve as a starting point for integrating knowledge in this field through research and suggest future research avenues for AI and ML applications in protozoan infectious diseases

Keywords
Machine learning
Leishmaniasis
protozoan infectious diseases
drug discovery
Manuscript
Manuscript.pdf
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