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In silico approaches based on machine learning methods are becoming increasing popular, but are nonetheless limited by the availability of data. With a view to making both data and models available to the scientific community, we have developed FPADMET which is a repository of molecular fingerprint-based predictive models for ADMET properties.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Summary<\/jats:title>\n                <jats:p>In this article, we have examined the efficacy of fingerprint-based machine learning models for a large number of ADMET-related properties. The predictive ability of a set of 20 different binary fingerprints (based on substructure keys, atom pairs, local path environments, as well as custom fingerprints such as all-shortest paths) for over 50 ADMET and ADMET-related endpoints have been evaluated as part of the study. We find that for a majority of the properties, fingerprint-based random forest models yield comparable or better performance compared with traditional 2D\/3D molecular descriptors.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Availability<\/jats:title>\n                <jats:p>The models are made available as part of open access software that can be downloaded from <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/gitlab.com\/vishsoft\/fpadmet\">https:\/\/gitlab.com\/vishsoft\/fpadmet<\/jats:ext-link>.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s13321-021-00557-5","type":"journal-article","created":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T19:06:39Z","timestamp":1632855999000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":125,"title":["FP-ADMET: a compendium of fingerprint-based ADMET prediction models"],"prefix":"10.1186","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7609-2245","authenticated-orcid":false,"given":"Vishwesh","family":"Venkatraman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,28]]},"reference":[{"issue":"5","key":"557_CR1","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1016\/j.drudis.2019.03.015","volume":"24","author":"LLG Ferreira","year":"2019","unstructured":"Ferreira LLG, Andricopulo AD (2019) ADMET modeling approaches in drug discovery. 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