{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T12:19:07Z","timestamp":1772799547789,"version":"3.50.1"},"reference-count":64,"publisher":"Oxford University Press (OUP)","issue":"13","license":[{"start":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T00:00:00Z","timestamp":1653264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,27]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Accurate ADMET (an abbreviation for \u2018absorption, distribution, metabolism, excretion and toxicity\u2019) predictions can efficiently screen out undesirable drug candidates in the early stage of drug discovery. In recent years, multiple comprehensive ADMET systems that adopt advanced machine learning models have been developed, providing services to estimate multiple endpoints. However, those ADMET systems usually suffer from weak extrapolation ability. First, due to the lack of labelled data for each endpoint, typical machine learning models perform frail for the molecules with unobserved scaffolds. Second, most systems only provide fixed built-in endpoints and cannot be customized to satisfy various research requirements. To this end, we develop a robust and endpoint extensible ADMET system, HelixADMET (H-ADMET). H-ADMET incorporates the concept of self-supervised learning to produce a robust pre-trained model. The model is then fine-tuned with a multi-task and multi-stage framework to transfer knowledge between ADMET endpoints, auxiliary tasks and self-supervised tasks.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our results demonstrate that H-ADMET achieves an overall improvement of 4%, compared with existing ADMET systems on comparable endpoints. Additionally, the pre-trained model provided by H-ADMET can be fine-tuned to generate new and customized ADMET endpoints, meeting various demands of drug research and development requirements.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>H-ADMET is freely accessible at https:\/\/paddlehelix.baidu.com\/app\/drug\/admet\/train.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac342","type":"journal-article","created":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T12:37:22Z","timestamp":1653309442000},"page":"3444-3453","source":"Crossref","is-referenced-by-count":51,"title":["HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer"],"prefix":"10.1093","volume":"38","author":[{"given":"Shanzhuo","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Natural Language Processcing, Baidu International Technology (Shenzhen) Co., Ltd , Shenzhen 518000, China"}]},{"given":"Zhiyuan","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Natural Language Processcing, Baidu International Technology (Shenzhen) Co., Ltd , Shenzhen 518000, China"}]},{"given":"Yueyang","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Natural Language Processcing, Baidu International Technology (Shenzhen) Co., Ltd , Shenzhen 518000, China"}]},{"given":"Lihang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Natural Language Processcing, Baidu International Technology (Shenzhen) Co., Ltd , Shenzhen 518000, China"}]},{"given":"Donglong","family":"He","sequence":"additional","affiliation":[{"name":"Department of Natural Language Processcing, Baidu International Technology (Shenzhen) Co., Ltd , Shenzhen 518000, China"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology (HIT) , Shenzhen 518000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7563-5268","authenticated-orcid":false,"given":"Xiaomin","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Natural Language Processcing, Baidu International Technology (Shenzhen) Co., Ltd , Shenzhen 518000, China"}]},{"given":"Xiaonan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Natural Language Processcing, Baidu International Technology (Shenzhen) Co., Ltd , Shenzhen 518000, China"}]},{"given":"Fan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Natural Language Processcing, Baidu International Technology (Shenzhen) Co., Ltd , Shenzhen 518000, China"}]},{"given":"Hua","family":"Wu","sequence":"additional","affiliation":[{"name":"Baidu Inc , Beijing 100000, China"}]},{"given":"Haifeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Baidu Inc , Beijing 100000, China"}]}],"member":"286","published-online":{"date-parts":[[2022,5,23]]},"reference":[{"key":"2023041407594882500_","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1016\/j.drudis.2013.05.015","article-title":"The Tox21 robotic platform for the assessment of environmental chemicals\u2013from vision to reality","volume":"18","author":"Attene-Ramos","year":"2013","journal-title":"Drug Discov. 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