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In this study we analyze publicly available assays involving different classes of GPCR to identify false positives. Using the latest developments in Machine Learning, we then build models that can predict such compounds with high confidence. Given the ubiquity of GPCR assays, we believe such models will be very helpful in flagging potential false positives for further testing.<\/jats:p>","DOI":"10.1007\/978-3-030-30493-5_71","type":"book-chapter","created":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T20:03:41Z","timestamp":1568145821000},"page":"764-770","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Analysis and Modelling of False Positives in GPCR Assays"],"prefix":"10.1007","author":[{"given":"Dipan","family":"Ghosh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6855-0012","authenticated-orcid":false,"given":"Igor","family":"Tetko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bert","family":"Klebl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Nussbaumer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Uwe","family":"Koch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"key":"71_CR1","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1038\/nrd.2017.178","volume":"16","author":"AS Hauser","year":"2017","unstructured":"Hauser, A.S., Attwood, M.M., Rask-Andersen, M., Schioth, H.B., Gloriam, D.E.: Trends in GPCR drug discovery: new agents, targets and indications. 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