{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T17:06:22Z","timestamp":1790355982288,"version":"4.1.0"},"reference-count":67,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T00:00:00Z","timestamp":1637712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Information &amp; Communications Technology Planning &amp; Evaluation(IITP) grant funded by the Korea government(MSIT)","award":["No. 2020-0-01108"],"award-info":[{"award-number":["No. 2020-0-01108"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomolecules"],"abstract":"<jats:p>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 \u2018Autocorrelation\u2019 module contributed more while the \u2018Atom type electrotopological state\u2019 contributed the least to the model.<\/jats:p>","DOI":"10.3390\/biom11121750","type":"journal-article","created":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T04:01:28Z","timestamp":1637812888000},"page":"1750","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Antimalarial Drug Predictions Using Molecular Descriptors and Machine Learning against Plasmodium Falciparum"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6893-6281","authenticated-orcid":false,"given":"Medard Edmund","family":"Mswahili","sequence":"first","affiliation":[{"name":"Department of ICT Convergence, Soonchunhyang University, Asan-si 31538, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gati Lother","family":"Martin","sequence":"additional","affiliation":[{"name":"Department of ICT Convergence, Soonchunhyang University, Asan-si 31538, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiyoung","family":"Woo","sequence":"additional","affiliation":[{"name":"Department of ICT Convergence, Soonchunhyang University, Asan-si 31538, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guang J.","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Engineering, Soonchunhyang University, Asan-si 31538, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9441-2940","authenticated-orcid":false,"given":"Young-Seob","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Chungbuk National University, Cheongju 28644, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1016\/0277-9536(93)90250-8","article-title":"The Impact of Malaria on Labour Use and Efficiency in the Sudan","volume":"37","author":"Nur","year":"1993","journal-title":"Soc. 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