{"id":219957,"date":"2025-08-12T20:05:07","date_gmt":"2025-08-13T01:05:07","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2025\/08\/ai-automatically-designs-optimal-drug-candidates-for-cancer-targeting-mutations"},"modified":"2025-08-12T20:05:07","modified_gmt":"2025-08-13T01:05:07","slug":"ai-automatically-designs-optimal-drug-candidates-for-cancer-targeting-mutations","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2025\/08\/ai-automatically-designs-optimal-drug-candidates-for-cancer-targeting-mutations","title":{"rendered":"AI automatically designs optimal drug candidates for cancer-targeting mutations"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/ai-automatically-designs-optimal-drug-candidates-for-cancer-targeting-mutations2.jpg\"><\/a><\/p>\n<p>Traditional drug development methods involve identifying a target protein (e.g., a cancer cell receptor) that causes disease, and then searching through countless molecular candidates (potential drugs) that could bind to that protein and block its function. This process is costly, time-consuming, and has a low success rate.<\/p>\n<p>KAIST researchers have developed an AI model that, using only information about the target protein, can design optimal drug candidates without any prior molecular data\u2014opening up new possibilities for <a href=\"https:\/\/phys.org\/tags\/drug+discovery\/\" rel=\"tag\" class=\"\">drug discovery<\/a>. The research is <a href=\"https:\/\/advanced.onlinelibrary.wiley.com\/doi\/10.1002\/advs.202502702\" target=\"_blank\">published<\/a> in the journal Advanced Science.<\/p>\n<p>The research team led by Professor Woo Youn Kim in the Department of Chemistry has developed an AI model named BInD (Bond and Interaction-generating Diffusion model), which can design and optimize drug candidate molecules tailored to a protein\u2019s structure alone\u2014without needing prior information about binding molecules. The model also predicts the binding mechanism (non-covalent interactions) between the drug and the target protein.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Traditional drug development methods involve identifying a target protein (e.g., a cancer cell receptor) that causes disease, and then searching through countless molecular candidates (potential drugs) that could bind to that protein and block its function. This process is costly, time-consuming, and has a low success rate. KAIST researchers have developed an AI model that, [\u2026]<\/p>\n","protected":false},"author":511,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11,6],"tags":[],"class_list":["post-219957","post","type-post","status-publish","format-standard","hentry","category-biotech-medical","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/219957","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/511"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=219957"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/219957\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=219957"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=219957"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=219957"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}