{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,15]],"date-time":"2026-08-15T01:30:47Z","timestamp":1786757447455,"version":"build-2736575974"},"reference-count":81,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T00:00:00Z","timestamp":1658880000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and ICT through the National Research Foundation of the Republic of Korea","award":["NRF-2012M3A9C4048758"],"award-info":[{"award-number":["NRF-2012M3A9C4048758"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Acute myeloid leukemia (AML) is one of the deadly cancers. Chemotherapy is the first-line treatment and the only curative intervention is stem cell transplantation which are intolerable for aged and comorbid patients. Therefore, finding complementary treatment is still an active research area. For this, empirical knowledge driven search for therapeutic agents have been carried out by long and arduous wet lab processes. Nonetheless, currently there is an accumulated bioinformatics data about natural products that enabled the use of efficient and cost effective in silico methods to find drug candidates. In this work, therefore, we set out to computationally investigate the phytochemicals from<jats:italic>Brucea antidysentrica<\/jats:italic>to identify therapeutic phytochemicals for AML. We performed in silico molecular docking of compounds against AML receptors IDH2, MCL1, FLT3 and BCL2. Phytochemicals were docked to AML receptors at the same site where small molecule drugs were bound and their binding affinities were examined. In addition, random compounds from PubChem were docked with AML targets and their docking score was compared with that of phytochemicals using statistical analysis. Then, non-covalent interactions between phytochemicals and receptors were identified and visualized using discovery studio and Protein-Ligand Interaction Profiler web tool (PLIP). From the statistical analysis, most of the phytochemicals exhibited significantly lower (p-value \u2264 0.05) binding energies compared with random compounds. Using cutoff binding energy of less than or equal to one standard deviation from the mean of the phytochemicals\u2019 binding energies for each receptor, 12 phytochemicals showed considerable binding affinity. Especially, hydnocarpin (-8.9 kcal\/mol) and yadanzioside P (-9.4 kcal\/mol) exhibited lower binding energy than approved drugs AMG176 (-8.6 kcal\/mol) and gilteritinib (-9.1 kcal\/mol) to receptors MCL1 and FLT3 respectively, indicating their potential to be lead molecules. In addition, most of the phytochemicals possessed acceptable drug-likeness and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Based on the binding affinities as exhibited by the molecular docking studies supported by the statistical analysis, 12 phytochemicals from<jats:italic>Brucea antidysentrica<\/jats:italic>(1,11-dimethoxycanthin-6-one, 1-methoxycanthin-6-one, 2-methoxycanthin-6-one, beta-carboline-1-propionic acid, bruceanol A, bruceanol D, bruceanol F, bruceantarin, bruceantin, canthin-6-one, hydnocarpin, and yadanzioside P) can be considered as candidate compounds to prevent and manage AML. However, the phytochemicals should be further studied using<jats:italic>in vivo &amp; in vitro<\/jats:italic>experiments on AML models. Therefore, this study concludes that combination of empirical knowledge, in silico molecular docking and ADMET profiling is useful to find natural product-based drug candidates. This technique can be applied to other natural products with known empirical efficacy.<\/jats:p>","DOI":"10.1371\/journal.pone.0270050","type":"journal-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T17:45:56Z","timestamp":1658943956000},"page":"e0270050","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":19,"title":["Combining empirical knowledge, in silico molecular docking and ADMET profiling to identify therapeutic phytochemicals from Brucea antidysentrica for acute myeloid leukemia"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6457-7082","authenticated-orcid":true,"given":"Lemessa Etana","family":"Bultum","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6405-2908","authenticated-orcid":true,"given":"Gemechu Bekele","family":"Tolossa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9070-4316","authenticated-orcid":true,"given":"Doheon","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,7,27]]},"reference":[{"key":"pone.0270050.ref001","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1016\/S0140-6736(16)31678-6","article-title":"Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990\u20132015: a systematic analysis for the Global Burden of Disease Study 2015","volume":"388","author":"Collaborators","year":"2016","journal-title":"Lancet (London, England)"},{"key":"pone.0270050.ref002","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1016\/S0140-6736(16)31012-1","article-title":"Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980\u20132015: a systematic analysis for the Global Burden of Disease Study 2015","volume":"388","author":"H Wang","year":"2016","journal-title":"Lancet"},{"key":"pone.0270050.ref003","unstructured":"Society AC. 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