{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T17:50:18Z","timestamp":1723657818901},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Korean government","award":["NRF-2019M3E5D4065860","2020R1C1C1003218"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,3]]},"abstract":"Abstract<\/jats:title>\n \n Motivation<\/jats:title>\n Poor metabolic stability leads to drug development failure. Therefore, it is essential to evaluate the metabolic stability of small compounds for successful drug discovery and development. However, evaluating metabolic stability in vitro and in vivo is expensive, time-consuming and laborious. In addition, only a few free software programs are available for metabolic stability data and prediction. Therefore, in this study, we aimed to develop a prediction model that predicts the metabolic stability of small compounds.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n We developed a computational model, PredMS, which predicts the metabolic stability of small compounds as stable or unstable in human liver microsomes. PredMS is based on a random forest model using an in-house database of metabolic stability data of 1917 compounds. To validate the prediction performance of PredMS, we generated external test data of 61 compounds. PredMS achieved an accuracy of 0.74, Matthew\u2019s correlation coefficient of 0.48, sensitivity of 0.70, specificity of 0.86, positive predictive value of 0.94 and negative predictive value of 0.46 on the external test dataset. PredMS will be a useful tool to predict the metabolic stability of small compounds in the early stages of drug discovery and development.<\/jats:p>\n <\/jats:sec>\n \n Availability and implementation<\/jats:title>\n The source code for PredMS is available at https:\/\/bitbucket.org\/krictai\/predms, and the PredMS web server is available at https:\/\/predms.netlify.app.<\/jats:p>\n <\/jats:sec>\n \n Supplementary information<\/jats:title>\n Supplementary data are available at Bioinformatics online.<\/jats:p>\n <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab547","type":"journal-article","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T12:31:00Z","timestamp":1631190660000},"page":"364-368","source":"Crossref","is-referenced-by-count":18,"title":["PredMS: a random forest model for predicting metabolic stability of drug candidates in human liver microsomes"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-0603-1599","authenticated-orcid":false,"given":"Jae Yong","family":"Ryu","sequence":"first","affiliation":[{"name":"Department of Biotechnology, Duksung Women\u2019s University , Seoul 01369, Republic of Korea"}]},{"given":"Jeong Hyun","family":"Lee","sequence":"additional","affiliation":[{"name":"Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology , 34114 Daejeon, Republic of Korea"}]},{"given":"Byung Ho","family":"Lee","sequence":"additional","affiliation":[{"name":"Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology , 34114 Daejeon, Republic of Korea"}]},{"given":"Jin Sook","family":"Song","sequence":"additional","affiliation":[{"name":"Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology , 34114 Daejeon, Republic of Korea"}]},{"given":"Sunjoo","family":"Ahn","sequence":"additional","affiliation":[{"name":"Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology , 34114 Daejeon, Republic of Korea"},{"name":"Department of Medicinal and Pharmaceutical Chemistry, University of Science and Technology , Daejeon 34129, Republic of Korea"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4242-0818","authenticated-orcid":false,"given":"Kwang-Seok","family":"Oh","sequence":"additional","affiliation":[{"name":"Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology , 34114 Daejeon, Republic of Korea"},{"name":"Department of Medicinal and Pharmaceutical Chemistry, University of Science and Technology , Daejeon 34129, Republic of Korea"}]}],"member":"286","published-online":{"date-parts":[[2021,9,13]]},"reference":[{"key":"2023020108414386500_btab547-B1","first-page":"265"},{"key":"2023020108414386500_btab547-B2","doi-asserted-by":"crossref","first-page":"878","DOI":"10.15252\/msb.20156651","article-title":"Deep learning for computational biology","volume":"12","author":"Angermueller","year":"2016","journal-title":"Mol. 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