{"id":1583,"date":"2023-06-10T10:12:51","date_gmt":"2023-06-10T10:12:51","guid":{"rendered":"https:\/\/dailyai.com\/?p=1583"},"modified":"2023-06-11T20:16:55","modified_gmt":"2023-06-11T20:16:55","slug":"researchers-build-breakthrough-ai-model-for-drug-discovery","status":"publish","type":"post","link":"https:\/\/dailyai.com\/fr\/2023\/06\/researchers-build-breakthrough-ai-model-for-drug-discovery\/","title":{"rendered":"Des chercheurs \u00e9laborent un mod\u00e8le d'IA r\u00e9volutionnaire pour la d\u00e9couverte de m\u00e9dicaments"},"content":{"rendered":"<p><strong>Dans un <a href=\"https:\/\/news.mit.edu\/2023\/new-model-offers-speedy-drug-discovery-0608\">\u00e9tude de rupture<\/a>Des chercheurs du MIT et de l'universit\u00e9 de Tufts ont mis au point une m\u00e9thode d'apprentissage automatique pour acc\u00e9l\u00e9rer le processus de d\u00e9couverte de m\u00e9dicaments.<\/strong><\/p>\n<p>Il existe de vastes biblioth\u00e8ques de m\u00e9dicaments contenant des milliards de compos\u00e9s diff\u00e9rents qui pourraient traiter efficacement toutes sortes de maladies, du cancer aux maladies cardiaques. La question est de savoir comment les trouver.<\/p>\n<p>La d\u00e9couverte de m\u00e9dicaments a toujours \u00e9t\u00e9 un processus \u00e0 forte intensit\u00e9 de main-d'\u0153uvre, les scientifiques devant tester chaque compos\u00e9 potentiel sur toutes les cibles possibles, ce qui prend beaucoup de temps et co\u00fbte cher.<\/p>\n<p>Pour r\u00e9soudre ce probl\u00e8me, les chercheurs ont commenc\u00e9 \u00e0 utiliser des m\u00e9thodes informatiques pour s\u00e9lectionner des biblioth\u00e8ques de compos\u00e9s m\u00e9dicamenteux. Cependant, cette approche n\u00e9cessite encore beaucoup de temps car elle implique le calcul de la structure tridimensionnelle de chaque prot\u00e9ine cible sur la base de sa s\u00e9quence d'acides amin\u00e9s.<\/p>\n<p>Cependant, l'\u00e9quipe du MIT et de Tufts a con\u00e7u une nouvelle approche bas\u00e9e sur un grand mod\u00e8le de langage (LLM), qui est le type de mod\u00e8le qui alimente l'IA comme ChatGPT. Le mod\u00e8le analyse de grandes quantit\u00e9s de donn\u00e9es pour d\u00e9terminer quels acides amin\u00e9s sont susceptibles de s'associer, un peu comme les mod\u00e8les de langage tels que ChatGPT analysent d'\u00e9normes volumes de texte pour d\u00e9terminer quels mots s'associent.<\/p>\n<p>Ce nouveau mod\u00e8le, <a href=\"https:\/\/www.pnas.org\/doi\/10.1073\/pnas.2220778120\">nomm\u00e9 ConPLex<\/a>Ce mod\u00e8le permet d'associer des prot\u00e9ines cibles \u00e0 des mol\u00e9cules m\u00e9dicamenteuses potentielles sans avoir \u00e0 calculer la structure des mol\u00e9cules. Ce mod\u00e8le a permis aux chercheurs de passer en revue plus de 100 millions de compos\u00e9s en une journ\u00e9e.<\/p>\n<p>Bonnie Berger, professeur de math\u00e9matiques \u00e0 la chaire Simons et chef du groupe Computation and Biology au sein du Computer Science and Artificial Intelligence Laboratory (CSAIL) du MIT, a d\u00e9clar\u00e9 : \"Ce travail r\u00e9pond \u00e0 la n\u00e9cessit\u00e9 d'un criblage in silico efficace et pr\u00e9cis des m\u00e9dicaments candidats potentiels, et l'\u00e9volutivit\u00e9 du mod\u00e8le permet des criblages \u00e0 grande \u00e9chelle pour l'\u00e9valuation des effets hors cible, la r\u00e9orientation des m\u00e9dicaments et la d\u00e9termination de l'impact des mutations sur la liaison des m\u00e9dicaments\".<\/p>\n<h2>L'IA rationalise le d\u00e9pistage et le d\u00e9veloppement des m\u00e9dicaments<\/h2>\n<p>L'un des principaux obstacles dans ce domaine a \u00e9t\u00e9 la tendance des mod\u00e8les existants \u00e0 ne pas \u00e9carter les compos\u00e9s \"leurres\". Ces compos\u00e9s sont similaires \u00e0 des m\u00e9dicaments efficaces mais n'interagissent pas bien avec la cible. Pour \u00e9carter les leurres, l'\u00e9quipe a incorpor\u00e9 une phase d'entra\u00eenement qui a aid\u00e9 le mod\u00e8le \u00e0 distinguer les vrais m\u00e9dicaments des imposteurs.<\/p>\n<p>Les chercheurs ont test\u00e9 le mod\u00e8le ConPLex en passant au crible une biblioth\u00e8que d'environ 4 700 mol\u00e9cules m\u00e9dicamenteuses candidates contre un ensemble de 51 enzymes connues sous le nom de prot\u00e9ines kinases.<\/p>\n<p>Apr\u00e8s avoir test\u00e9 exp\u00e9rimentalement 19 des paires m\u00e9dicament-prot\u00e9ine les plus prometteuses, ils en ont trouv\u00e9 12 qui pr\u00e9sentaient une forte affinit\u00e9 de liaison avec la cible.<\/p>\n<p>Bien que cette \u00e9tude se soit principalement concentr\u00e9e sur les m\u00e9dicaments \u00e0 petites mol\u00e9cules, l'\u00e9quipe \u00e9tudie la possibilit\u00e9 d'appliquer cette approche \u00e0 d'autres types de m\u00e9dicaments, tels que les anticorps th\u00e9rapeutiques.<\/p>\n<p>Le mod\u00e8le pourrait \u00e9galement permettre d'effectuer des tests de toxicit\u00e9 sur des compos\u00e9s m\u00e9dicamenteux potentiels, afin de s'assurer qu'ils ne produisent pas d'effets secondaires ind\u00e9sirables avant d'\u00eatre test\u00e9s sur des mod\u00e8les animaux.<\/p>\n<p>Rohit Singh, chercheur au CSAIL, d\u00e9clare : \"Si la d\u00e9couverte de m\u00e9dicaments est si co\u00fbteuse, c'est en partie parce que les taux d'\u00e9chec sont \u00e9lev\u00e9s. Si nous pouvons r\u00e9duire ces taux d'\u00e9chec en disant d'embl\u00e9e que ce m\u00e9dicament a peu de chances de fonctionner, cela pourrait contribuer \u00e0 r\u00e9duire consid\u00e9rablement le co\u00fbt de la d\u00e9couverte de m\u00e9dicaments.\"<\/p>\n<p>Eytan Ruppin, chef du laboratoire de science des donn\u00e9es sur le cancer \u00e0 l'Institut national du cancer, salue cette approche comme une \"avanc\u00e9e significative dans la pr\u00e9diction des interactions entre m\u00e9dicaments et cibles\".<\/p>\n<p>Fin mai, une autre \u00e9quipe de recherche du MIT a construit un mod\u00e8le de s\u00e9lection d'antibiotiques qui fonctionne de mani\u00e8re similaire \u00e0 celui-ci.<\/p>\n<p>L'\u00e9quipe a utilis\u00e9 l'apprentissage automatique pour explorer des milliers de m\u00e9dicaments existants et a localis\u00e9 un compos\u00e9 particulier efficace contre les superbact\u00e9ries r\u00e9sistantes aux antibiotiques.<\/p>\n<p><a href=\"https:\/\/dailyai.com\/fr\/2023\/05\/ai-helps-researchers-discover-a-new-antibiotic-for-treating-drug-resistant-bacteria\/\">Pour en savoir plus, cliquez ici<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"<p>Des chercheurs du MIT et de l'universit\u00e9 de Tufts ont mis au point une m\u00e9thode d'apprentissage automatique pour acc\u00e9l\u00e9rer le processus de d\u00e9couverte de m\u00e9dicaments. Il existe de vastes biblioth\u00e8ques de m\u00e9dicaments contenant des milliards de compos\u00e9s diff\u00e9rents qui pourraient traiter efficacement toutes sortes de maladies, du cancer aux maladies cardiaques. La question est de savoir comment les trouver. La d\u00e9couverte de m\u00e9dicaments a toujours \u00e9t\u00e9 un processus \u00e0 forte intensit\u00e9 de main-d'\u0153uvre, les scientifiques devant tester chaque compos\u00e9 potentiel sur toutes les cibles possibles - une entreprise longue et co\u00fbteuse. Pour r\u00e9soudre ce probl\u00e8me, les chercheurs ont commenc\u00e9 \u00e0 utiliser des m\u00e9thodes informatiques pour trier les biblioth\u00e8ques de compos\u00e9s m\u00e9dicamenteux. Cependant, cette approche n\u00e9cessite encore beaucoup de temps car elle<\/p>","protected":false},"author":2,"featured_media":1584,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[105,117,135],"class_list":["post-1583","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-machine-learning","tag-mit","tag-science"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Researchers build breakthrough AI model for drug discovery | DailyAI<\/title>\n<meta name=\"description\" content=\"MIT and Tufts University researchers built an AI model that vastly accelerates the drug development process.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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