{"id":8334,"date":"2024-10-24T20:16:30","date_gmt":"2024-10-24T20:16:30","guid":{"rendered":"https:\/\/www.tun.com\/home\/?p=8334"},"modified":"2024-10-24T20:16:56","modified_gmt":"2024-10-24T20:16:56","slug":"university-of-bonn-develops-ai-for-advanced-drug-discovery","status":"publish","type":"post","link":"https:\/\/www.tun.com\/home\/university-of-bonn-develops-ai-for-advanced-drug-discovery\/","title":{"rendered":"University of Bonn Develops AI for Advanced Drug Discovery"},"content":{"rendered":"\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-uagb-blockquote uagb-block-e7eb3fc3 uagb-blockquote__skin-border uagb-blockquote__stack-img-none\"><blockquote class=\"uagb-blockquote\"><div class=\"uagb-blockquote__content\">In a pioneering study, University of Bonn researchers have developed an AI similar to ChatGPT, but for molecules. This &#8220;chemical language model&#8221; predicts compounds with dual-target activities, opening new vistas in pharmaceutical research.<\/div><footer><div class=\"uagb-blockquote__author-wrap uagb-blockquote__author-at-left\"><\/div><\/footer><\/blockquote><\/div>\n\n\n\n<div class=\"wp-block-group is-content-justification-space-between is-nowrap is-layout-flex wp-container-core-group-is-layout-0dfbf163 wp-block-group-is-layout-flex\"><div style=\"font-size:16px;\" class=\"has-text-align-left wp-block-post-author\"><div class=\"wp-block-post-author__content\"><p class=\"wp-block-post-author__name\">The University Network<\/p><\/div><\/div>\n\n\n<div class=\"wp-block-uagb-social-share uagb-social-share__outer-wrap uagb-social-share__layout-horizontal uagb-block-ee584a31\">\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-ec619ce7\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.facebook.com\/sharer.php?u=\" tabindex=\"0\" role=\"button\" aria-label=\"facebook\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M504 256C504 119 393 8 256 8S8 119 8 256c0 123.8 90.69 226.4 209.3 245V327.7h-63V256h63v-54.64c0-62.15 37-96.48 93.67-96.48 27.14 0 55.52 4.84 55.52 4.84v61h-31.28c-30.8 0-40.41 19.12-40.41 38.73V256h68.78l-11 71.69h-57.78V501C413.3 482.4 504 379.8 504 256z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-32d99934\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/twitter.com\/share?url=\" tabindex=\"0\" role=\"button\" aria-label=\"twitter\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M389.2 48h70.6L305.6 224.2 487 464H345L233.7 318.6 106.5 464H35.8L200.7 275.5 26.8 48H172.4L272.9 180.9 389.2 48zM364.4 421.8h39.1L151.1 88h-42L364.4 421.8z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-1d136f14\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.linkedin.com\/shareArticle?url=\" tabindex=\"0\" role=\"button\" aria-label=\"linkedin\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 448 512\"><path d=\"M416 32H31.9C14.3 32 0 46.5 0 64.3v383.4C0 465.5 14.3 480 31.9 480H416c17.6 0 32-14.5 32-32.3V64.3c0-17.8-14.4-32.3-32-32.3zM135.4 416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n<p><\/p>\n\n\n\n<p>In a groundbreaking advancement, researchers from the University of Bonn have trained an artificial intelligence model to predict potential active ingredients with specific properties, akin to a chemical ChatGPT for molecules. This innovative study, <a href=\"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2666386424005605\" title=\"\">published<\/a> in the journal Cell Reports Physical Science, holds promise for revolutionizing the field of pharmaceutical research by identifying compounds with dual-target activities.<\/p>\n\n\n\n<p>The AI model, referred to as a chemical language model, is designed to generate the structural formulas of chemical compounds that can bind to two different target proteins simultaneously. Such compounds are highly coveted in pharmaceutical research due to their polypharmacology, promising enhanced efficacy by influencing multiple intracellular processes and signaling pathways at once.<\/p>\n\n\n\n<p>\u201cIn pharmaceutical research, these types of active compounds are highly desirable due to their polypharmacology,\u201d J\u00fcrgen Bajorath, a professor and chair of Life Science Informatics at the University of Bonn, said in a <a href=\"https:\/\/www.uni-bonn.de\/en\/news\/207-2024\">news release<\/a>. <\/p>\n\n\n\n<p>\u201cBecause compounds with desirable multi-target activity influence several intracellular processes and signaling pathways at the same time, they are often particularly effective \u2013 such as in the fight against cancer,\u201d he added.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p>Traditionally, such dual-target effects might be achieved through the co-administration of multiple drugs. However, this comes with inherent risks like unwanted drug interactions and variable metabolic rates in the body, complicating their administration. <\/p>\n\n\n\n<p>The AI model developed by the University of Bonn offers a solution by predicting compounds that can precisely achieve these effects on their own.<\/p>\n\n\n\n<p>Creating single-target molecules in drug discovery is a formidable challenge. Designing compounds with predefined dual-target activities is even more complex. <\/p>\n\n\n\n<p>The newly developed chemical language model could transform this aspect of drug discovery. Operating similarly to how ChatGPT learns from vast amounts of written text, this chemical language model has been trained with thousands of chemical representations known as SMILES strings, which encode the structures and compositions of organic molecules.<\/p>\n\n\n\n<p>\u201cWe have now trained our chemical language model with pairs of strings,\u201d Sanjana Srinivasan, a member of Bajorath\u2019s research group, said in the news release. \u201cOne of the strings described a molecule that we know only acts against one target protein. The other represented a compound that, in addition to this protein, also influences a second target protein.\u201d<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p>The AI model was trained with over 70,000 pairs of these strings, enabling it to understand the subtle distinctions between single-target and dual-target compounds. Following this training, the model could suggest molecules based on a known compound that would act not only against its target protein but also against a second, distinct protein.<\/p>\n\n\n\n<p>The versatility of the AI model was further enhanced through a fine-tuning phase, where the model was trained with several dozen specialized pairs. This phase prepared the AI to predict compounds affecting entirely different classes of enzymes or receptors, akin to asking ChatGPT to switch from writing a sonnet to a limerick.<\/p>\n\n\n\n<p>After fine-tuning, the AI was able to suggest molecules already known to act against desired protein combinations. <\/p>\n\n\n\n<p>\u201cThis shows that the process works,\u201d Bajorath said. <\/p>\n\n\n\n<p>However, the real power of this approach lies in the AI\u2019s ability to suggest novel chemical structures. <\/p>\n\n\n\n<p><\/p>\n\n\n\n<p>&#8220;To a certain extent, it triggers \u2018out of the box\u2019 ideas and comes up with original solutions that can lead to new design hypotheses and approaches,\u201d he added.<\/p>\n\n\n\n<p>The study signifies a significant leap forward in the integration of AI into life sciences, potentially setting new standards in drug discovery and development.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In a groundbreaking advancement, researchers from the University of Bonn have trained an artificial intelligence model to predict potential active ingredients with specific properties, akin to a chemical ChatGPT for molecules. This innovative study, published in the journal Cell Reports Physical Science, holds promise for revolutionizing the field of pharmaceutical research by identifying compounds with [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"single-no-separators","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[8,12],"tags":[],"class_list":["post-8334","post","type-post","status-publish","format-standard","hentry","category-ai","category-health"],"acf":[],"aioseo_notices":[],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false},"uagb_author_info":{"display_name":"The University Network","author_link":"https:\/\/www.tun.com\/home\/author\/funky_junkie\/"},"uagb_comment_info":0,"uagb_excerpt":"In a groundbreaking advancement, researchers from the University of Bonn have trained an artificial intelligence model to predict potential active ingredients with specific properties, akin to a chemical ChatGPT for molecules. This innovative study, published in the journal Cell Reports Physical Science, holds promise for revolutionizing the field of pharmaceutical research by identifying compounds with&hellip;","_links":{"self":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/8334","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/comments?post=8334"}],"version-history":[{"count":10,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/8334\/revisions"}],"predecessor-version":[{"id":8459,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/8334\/revisions\/8459"}],"wp:attachment":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/media?parent=8334"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/categories?post=8334"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/tags?post=8334"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}