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Fast methods for evaluating ADMET properties of small molecules often involve applying a set of simple empirical rules (educated guesses) and as such, compound collections' property profiling can be performed <jats:italic>in silico<\/jats:italic>. Clearly, these rules cannot assess the full complexity of the human body but can provide valuable information and assist decision-making.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>This paper presents FAF-Drugs2, a free adaptable tool for ADMET filtering of electronic compound collections. FAF-Drugs2 is a command line utility program (e.g., written in Python) based on the open source chemistry toolkit OpenBabel, which performs various physicochemical calculations, identifies key functional groups, some toxic and unstable molecules\/functional groups. In addition to filtered collections, FAF-Drugs2 can provide, via Gnuplot, several distribution diagrams of major physicochemical properties of the screened compound libraries.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>We have developed FAF-Drugs2 to facilitate compound collection preparation, prior to (or after) experimental screening or virtual screening computations. Users can select to apply various filtering thresholds and add rules as needed for a given project. As it stands, FAF-Drugs2 implements numerous filtering rules (23 physicochemical rules and 204 substructure searching rules) that can be easily tuned.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-9-396","type":"journal-article","created":{"date-parts":[[2008,9,24]],"date-time":"2008-09-24T18:13:57Z","timestamp":1222280037000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":218,"title":["FAF-Drugs2: Free ADME\/tox filtering tool to assist drug discovery and chemical biology projects"],"prefix":"10.1186","volume":"9","author":[{"given":"David","family":"Lagorce","sequence":"first","affiliation":[]},{"given":"Olivier","family":"Sperandio","sequence":"additional","affiliation":[]},{"given":"Herv\u00e9","family":"Galons","sequence":"additional","affiliation":[]},{"given":"Maria A","family":"Miteva","sequence":"additional","affiliation":[]},{"given":"Bruno O","family":"Villoutreix","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2008,9,24]]},"reference":[{"issue":"2","key":"2381_CR1","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/S1359-6446(01)02100-6","volume":"7","author":"HE Selick","year":"2002","unstructured":"Selick HE, Beresford AP, Tarbit MH: The emerging importance of predictive ADME simulation in drug discovery. 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