{"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/accelerating-prototype-based-drug-discovery","title":"Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks","arxiv_id":"1804.02668","date":"2018-04-08","proceeding":null,"authors":["Shahar Harel","Kira Radinsky"],"abstract":"Designing a new drug is a lengthy and expensive process. As the space of\npotential molecules is very large (10^23-10^60), a common technique during drug\ndiscovery is to start from a molecule which already has some of the desired\nproperties. An interdisciplinary team of scientists generates hypothesis about\nthe required changes to the prototype. In this work, we develop an algorithmic\nunsupervised-approach that automatically generates potential drug molecules\ngiven a prototype drug. We show that the molecules generated by the system are\nvalid molecules and significantly different from the prototype drug. Out of the\ncompounds generated by the system, we identified 35 FDA-approved drugs. As an\nexample, our system generated Isoniazid - one of the main drugs for\nTuberculosis. The system is currently being deployed for use in collaboration\nwith pharmaceutical companies to further analyze the additional generated\nmolecules.","url_abs":"http://arxiv.org/abs/1804.02668v1","url_pdf":"http://arxiv.org/pdf/1804.02668v1.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":"accelerating-prototype-based-drug-discovery","repo_url":"https://github.com/shaharharel/CDN_Molecule","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"accelerating-prototype-based-drug-discovery","repo_url":"https://github.com/0h-n0/cdn_molecule_pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"drug-discovery","task_name":"Drug Discovery"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}