{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/corresponding-projections-for-orphan","title":"Corresponding Projections for Orphan Screening","arxiv_id":"1812.00058","date":"2018-11-30","proceeding":null,"authors":["Sven Giesselbach","Katrin Ullrich","Michael Kamp","Daniel Paurat","Thomas Gärtner"],"abstract":"We propose a novel transfer learning approach for orphan screening called\ncorresponding projections. In orphan screening the learning task is to predict\nthe binding affinities of compounds to an orphan protein, i.e., one for which\nno training data is available. The identification of compounds with high\naffinity is a central concern in medicine since it can be used for drug\ndiscovery and design. Given a set of prediction models for proteins with\nlabelled training data and a similarity between the proteins, corresponding\nprojections constructs a model for the orphan protein from them such that the\nsimilarity between models resembles the one between proteins. Under the\nassumption that the similarity resemblance holds, we derive an efficient\nalgorithm for kernel methods. We empirically show that the approach outperforms\nthe state-of-the-art in orphan screening.","url_abs":"http://arxiv.org/abs/1812.00058v1","url_pdf":"http://arxiv.org/pdf/1812.00058v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"corresponding-projections-for-orphan","repo_url":"https://bitbucket.org/grumpy_kat/corresponding-projections","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"corresponding-projections-for-orphan","repo_url":"https://github.com/diogofbraga/OrphanPrincipalComponentAnalysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"drug-discovery","task_name":"Drug Discovery"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}