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Biomolecules
Special Issues
Translational Approaches for the Detection and Treatment of Malaria
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Translational Approaches for the Detection and Treatment of Malaria

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Molecular Medicine".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 2872

Special Issue Editor

Dr. Christiaan van Ooij

E-Mail Website
Guest Editor
London School of Hygiene & Tropical Medicinedisabled, London, UK
Interests: Malaria parasite-host interaction

Special Issue Information

Dear Colleagues,

Although the mortality owing to malaria has been decreasing over the past fifteen years, the rate at which it is decreasing is slowing yearly and appears to have reached a plateau. This is in part the result of the spread of resistance to anti-malarial drugs, with certain parasites containing multiple resistance markers and hence becoming difficult to treat. Therefore, there is a pressing need for new antimalarials and new target molecules for the development of these antimalarials. Another important hurdle in the fight against malaria is emergence of parasites lacking the markers used in rapid diagnostic test, allowing these parasites to go undetected. Such a selective advantage will likely spread quickly and requires an in-depth investigation of the role of the marker proteins and a search for potential new marker proteins.

The aim of this Special Issue is to provide a forum for the latest research on the development of new antimalarials, discovery of new drug targets and new ways of detecting malaria parasites. Original research articles and review articles are welcome. The collected works in this Special Issue will thus help researchers explore the new approaches that will be key in further decreasing the burden of malaria.

Dr. Christiaan van Ooij
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomolecules is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Malaria
  • Antimalarial drug development
  • Rapid Diagnostic Testing
  • Plasmodium

Published Papers (1 paper)

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Research

15 pages, 485 KiB  
Article
Antimalarial Drug Predictions Using Molecular Descriptors and Machine Learning against Plasmodium Falciparum
by Medard Edmund Mswahili, Gati Lother Martin, Jiyoung Woo, Guang J. Choi and Young-Seob Jeong
Biomolecules 2021, 11(12), 1750; https://doi.org/10.3390/biom11121750 - 24 Nov 2021
Cited by 8 | Viewed by 2539
Abstract
Malaria remains by far one of the most threatening and dangerous illnesses caused by the plasmodium falciparum parasite. Chloroquine (CQ) and first-line artemisinin-based combination treatment (ACT) have long been the drug of choice for the treatment and controlling of malaria; however, the emergence [...] Read more.
Malaria remains by far one of the most threatening and dangerous illnesses caused by the plasmodium falciparum parasite. Chloroquine (CQ) and first-line artemisinin-based combination treatment (ACT) have long been the drug of choice for the treatment and controlling of malaria; however, the emergence of CQ-resistant and artemisinin resistance parasites is now present in most areas where malaria is endemic. In this work, we developed five machine learning models to predict antimalarial bioactivities of a drug against plasmodium falciparum from the features (i.e., molecular descriptors values) obtained from PaDEL software from SMILES of compounds and compare the machine learning models by experiments with our collected data of 4794 instances. As a consequence, we found that three models amongst the five, namely artificial neural network (ANN), extreme gradient boost (XGB), and random forest (RF), outperform the others in terms of accuracy while observing that, using roughly a quarter of the promising descriptors picked by the feature selection algorithm, the five models achieved equivalent and comparable performance. Nevertheless, the contribution of all molecular descriptors in the models was investigated through the comparison of their rank values by the feature selection algorithm and found that the most potent and relevant descriptors which come from the ‘Autocorrelation’ module contributed more while the ‘Atom type electrotopological state’ contributed the least to the model. Full article
(This article belongs to the Special Issue Translational Approaches for the Detection and Treatment of Malaria)
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