{"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/drug-cell-line-interaction-prediction","title":"Drug cell line interaction prediction","arxiv_id":"1812.11178","date":"2018-12-28","proceeding":null,"authors":["Pengfei Liu"],"abstract":"Understanding the phenotypic drug response on cancer cell lines plays a vital\nrule in anti-cancer drug discovery and re-purposing. The Genomics of Drug\nSensitivity in Cancer (GDSC) database provides open data for researchers in\nphenotypic screening to test their models and methods. Previously, most\nresearch in these areas starts from the fingerprints or features of drugs,\ninstead of their structures. In this paper, we introduce a model for phenotypic\nscreening, which is called twin Convolutional Neural Network for drugs in\nSMILES format (tCNNS). tCNNS is comprised of CNN input channels for drugs in\nSMILES format and cancer cell lines respectively. Our model achieves $0.84$ for\nthe coefficient of determinant($R^2$) and $0.92$ for Pearson\ncorrelation($R_p$), which are significantly better than previous\nworks\\cite{ammad2014integrative,haider2015copula,menden2013machine}. Besides\nthese statistical metrics, tCNNS also provides some insights into phenotypic\nscreening.","url_abs":"http://arxiv.org/abs/1812.11178v1","url_pdf":"http://arxiv.org/pdf/1812.11178v1.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":"drug-cell-line-interaction-prediction","repo_url":"https://github.com/Lowpassfilter/tCNNS-Project","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"drug-discovery","task_name":"Drug Discovery"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}