{"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/massively-multitask-networks-for-drug","title":"Massively Multitask Networks for Drug Discovery","arxiv_id":"1502.02072","date":"2015-02-06","proceeding":null,"authors":["Bharath Ramsundar","Steven Kearnes","Patrick Riley","Dale Webster","David Konerding","Vijay Pande"],"abstract":"Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.","url_abs":"http://arxiv.org/abs/1502.02072v1","url_pdf":"http://arxiv.org/pdf/1502.02072v1.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":"massively-multitask-networks-for-drug","repo_url":"https://github.com/willy-b/tiny-GIN-for-ogbg-molhiv","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"drug-discovery","task_name":"Drug Discovery"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1502.02072","atlas_url":"https://app.syntology.ai/?focus=1502.02072","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}