{"id":120556,"date":"2024-11-12T19:09:07","date_gmt":"2024-11-12T17:09:07","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/deepmind-otkryl-kod-alphafold-3-ai-sistemy-modelirovaniya-struktury-belkov"},"modified":"2024-11-12T19:09:07","modified_gmt":"2024-11-12T17:09:07","slug":"deepmind-otkryl-kod-alphafold-3-ai-sistemy-modelirovaniya-struktury-belkov","status":"publish","type":"post","link":"https:\/\/prohoster.info\/it\/blog\/novosti-interneta\/deepmind-otkryl-kod-alphafold-3-ai-sistemy-modelirovaniya-struktury-belkov","title":{"rendered":"DeepMind ha rilasciato il codice di AlphaFold 3, un sistema AI per la modellazione della struttura delle proteine.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

L'azienda Google DeepMind ha reso disponibili i codici sorgente del sistema di apprendimento automatico AlphaFold 3, progettato per prevedere la struttura tridimensionale delle proteine e per modellare l'interazione delle proteine con altri tipi di molecole. Per la creazione degli algoritmi di machine learning implementati nella seconda versione di AlphaFold, quest'anno \u00e8 stato assegnato il Premio Nobel per la chimica. Gli strumenti associati ad AlphaFold 3 sono scritti in Python e C++ e sono distribuiti con licenza CC BY-NC-SA 4.0. I modelli addestrati sono forniti sulla base di un accordo con l'utente. \u00c8 stato lanciato separatamente un server<\/a>, consentendo di sperimentare con AlphaFold 3 in modalit\u00e0 online. <\/p>\n

Il modello, addestrato su una collezione che descrive le strutture di tutte le proteine e le sequenze di amminoacidi conosciuti, affronta il problema del ripiegamento delle proteine e consente di prevedere la struttura tridimensionale di nuove proteine con una precisione che non \u00e8 inferiore a quella dell'analisi di laboratorio e, nella valutazione dell'interazione delle proteine con altri tipi di molecole, supera notevolmente i metodi di previsione esistenti. La terza versione del modello AlphaFold si distingue per l'uso di una nuova architettura \u00abPairformer\u00bb, che sviluppa l'idea dell'architettura \u00abtrasformatori\u00bb. <\/p>\n

A differenza di AlphaFold 2, la nuova versione non \u00e8 limitata alle proteine composte da una sola catena polipeptidica e pu\u00f2 essere applicata per prevedere complessi proteici con DNA e RNA, oltre a modellare varianti modificate di queste molecole. In input ad AlphaFold viene fornita una lista di molecole, mentre in output viene generata una struttura 3D condivisa che determina l'interazione pi\u00f9 probabile delle molecole indicate. <\/p>\n

Dal punto di vista pratico, AlphaFold 3 pu\u00f2 essere utilizzato per la sviluppo di farmaci e terapie, oltre che per la creazione di nuove proteine. Ad esempio, con l'aiuto di AlphaFold \u00e8 stata progettata una proteina in grado di legarsi a determinate cellule tumorali, un aspetto che pu\u00f2 essere sfruttato nella terapia antitumorale di nuova generazione. AlphaFold \u00e8 anche utilizzato attivamente nello studio dell'interazione tra anticorpi e proteine per comprendere la risposta immunitaria umana e per la creazione di nuovi anticorpi.
\n
Fonte: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u041a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Google DeepMind \u043e\u043f\u0443\u0431\u043b\u0438\u043a\u043e\u0432\u0430\u043b\u0430 \u0438\u0441\u0445\u043e\u0434\u043d\u044b\u0435 \u0442\u0435\u043a\u0441\u0442\u044b \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f AlphaFold 3, \u043f\u0440\u0435\u0434\u043d\u0430\u0437\u043d\u0430\u0447\u0435\u043d\u043d\u043e\u0439 \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f \u0442\u0440\u0451\u0445\u043c\u0435\u0440\u043d\u043e\u0439 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u044b \u0431\u0435\u043b\u043a\u043e\u0432 \u0438 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0432\u0437\u0430\u0438\u043c\u043e\u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f \u0431\u0435\u043b\u043a\u043e\u0432 \u0441 \u0434\u0440\u0443\u0433\u0438\u043c\u0438 \u0442\u0438\u043f\u0430\u043c\u0438 \u043c\u043e\u043b\u0435\u043a\u0443\u043b. \u0417\u0430 \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u043e\u0432 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f, \u0440\u0435\u0430\u043b\u0438\u0437\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u0432\u043e \u0432\u0442\u043e\u0440\u043e\u0439 \u0432\u0435\u0440\u0441\u0438\u0438 AlphaFold, \u0432 \u044d\u0442\u043e\u043c \u0433\u043e\u0434\u0443 \u043f\u0440\u0438\u0441\u0443\u0436\u0434\u0435\u043d\u0430 \u041d\u043e\u0431\u0435\u043b\u0435\u0432\u0441\u043a\u0430\u044f \u043f\u0440\u0435\u043c\u0438\u044f \u043f\u043e \u0445\u0438\u043c\u0438\u0438. \u0421\u0432\u044f\u0437\u0430\u043d\u043d\u044b\u0439 \u0441 AlphaFold 3 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u0439 \u043d\u0430\u043f\u0438\u0441\u0430\u043d \u043d\u0430 Python \u0438 C++, \u0438 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u044f\u0435\u0442\u0441\u044f […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-120556","post","type-post","status-publish","format-standard","hentry","category-novosti-interneta"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd47DeepMind ha aperto il codice di AlphaFold 3, il sistema di intelligenza artificiale per la modellazione della struttura delle proteine | ProHoster","description":"L'azienda Google DeepMind ha reso disponibili i codici sorgente del sistema di apprendimento automatico AlphaFold 3, progettato per prevedere la struttura tridimensionale delle proteine e per modellare l'interazione delle proteine con altri tipi di molecole. Per la creazione degli algoritmi di machine learning implementati nella seconda versione di AlphaFold, quest'anno \u00e8 stato assegnato il Premio Nobel per la chimica. 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\u0441 \u0434\u0440\u0443\u0433\u0438\u043c\u0438 \u0442\u0438\u043f\u0430\u043c\u0438 \u043c\u043e\u043b\u0435\u043a\u0443\u043b. \u0417\u0430 \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u043e\u0432 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f, \u0440\u0435\u0430\u043b\u0438\u0437\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u0432\u043e \u0432\u0442\u043e\u0440\u043e\u0439 \u0432\u0435\u0440\u0441\u0438\u0438 AlphaFold, \u0432 \u044d\u0442\u043e\u043c \u0433\u043e\u0434\u0443 \u043f\u0440\u0438\u0441\u0443\u0436\u0434\u0435\u043d\u0430 \u041d\u043e\u0431\u0435\u043b\u0435\u0432\u0441\u043a\u0430\u044f \u043f\u0440\u0435\u043c\u0438\u044f \u043f\u043e \u0445\u0438\u043c\u0438\u0438. \u0421\u0432\u044f\u0437\u0430\u043d\u043d\u044b\u0439 \u0441 AlphaFold 3 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u0439 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