{"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/toxicity-prediction-using-deep-learning","title":"Toxicity Prediction using Deep Learning","arxiv_id":"1503.01445","date":"2015-03-04","proceeding":null,"authors":["Thomas Unterthiner","Andreas Mayr","Günter Klambauer","Sepp Hochreiter"],"abstract":"Everyday we are exposed to various chemicals via food additives, cleaning and\ncosmetic products and medicines -- and some of them might be toxic. However\ntesting the toxicity of all existing compounds by biological experiments is\nneither financially nor logistically feasible. Therefore the government\nagencies NIH, EPA and FDA launched the Tox21 Data Challenge within the\n\"Toxicology in the 21st Century\" (Tox21) initiative. The goal of this challenge\nwas to assess the performance of computational methods in predicting the\ntoxicity of chemical compounds. State of the art toxicity prediction methods\nbuild upon specifically-designed chemical descriptors developed over decades.\nThough Deep Learning is new to the field and was never applied to toxicity\nprediction before, it clearly outperformed all other participating methods. In\nthis application paper we show that deep nets automatically learn features\nresembling well-established toxicophores. In total, our Deep Learning approach\nwon both of the panel-challenges (nuclear receptors and stress response) as\nwell as the overall Grand Challenge, and thereby sets a new standard in tox\nprediction.","url_abs":"http://arxiv.org/abs/1503.01445v1","url_pdf":"http://arxiv.org/pdf/1503.01445v1.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":"toxicity-prediction-using-deep-learning","repo_url":"https://github.com/RezaCDoobary/DrugDiscovery-Tox21","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1503.01445","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}