{"id":8773,"date":"2024-01-01T16:25:29","date_gmt":"2024-01-01T16:25:29","guid":{"rendered":"https:\/\/dailyai.com\/?p=8773"},"modified":"2024-01-11T18:39:18","modified_gmt":"2024-01-11T18:39:18","slug":"researchers-aim-to-determine-how-black-box-drug-discovery-models-work","status":"publish","type":"post","link":"https:\/\/dailyai.com\/de\/2024\/01\/researchers-aim-to-determine-how-black-box-drug-discovery-models-work\/","title":{"rendered":"Forscher wollen herausfinden, wie \"Black Box\"-Modelle zur Arzneimittelentdeckung funktionieren"},"content":{"rendered":"

Wissenschaftler der Universit\u00e4t Bonn haben unter der Leitung von Professor Dr. J\u00fcrgen Bajorath das Innenleben von \"Black Box\"-KIs in der Pharmaforschung aufgedeckt.\u00a0<\/b><\/p>\n

Ihre <\/span>Studie<\/span><\/a>Die k\u00fcrzlich in Nature Machine Intelligence ver\u00f6ffentlichte Studie zeigt, dass KI-Modelle in der Arzneimittelforschung \u00fcberwiegend auf dem Abrufen vorhandener Daten beruhen, anstatt neue chemische Wechselwirkungen zu lernen.\u00a0<\/span>Dies stellt bisherige Annahmen dar\u00fcber in Frage, wie KI in diesem Bereich Vorhersagen trifft.<\/span><\/p>\n

Forscher nutzen maschinelles Lernen, um Molek\u00fcle zu finden, die effektiv mit Zielproteinen interagieren. Dazu geh\u00f6rt h\u00e4ufig die Vorhersage, welche Molek\u00fcle stark an Zielproteine binden werden, gefolgt von einer experimentellen Validierung dieser Vorhersagen.<\/span><\/p>\n

Bei dieser Form der KI-gest\u00fctzten Arzneimittelforschung gab es 2023 wichtige Durchbr\u00fcche, darunter ein <\/span>MIT-entwickeltes Modell<\/span><\/a> das Millionen von Verbindungen auf ihre potenzielle therapeutische Wirkung hin analysiert hat, zeigen die von der KI entdeckten Arzneimittel <\/span>vielversprechend bei der Verlangsamung der Alterung<\/span><\/a>und AI-generierte Proteine, die <\/span>ausgezeichnete Bindungsst\u00e4rke<\/span><\/a>.<\/span><\/p>\n

Die Frage, die Bajorath und sein Team zu beantworten versuchten, lautet: Wie kommen einige dieser Modelle zu ihren Ergebnissen?<\/span><\/p>\n

Das Forschungsteam konzentrierte sich auf Graph Neural Networks (GNNs), eine Art von maschinellen Lernanwendungen, die in der Arzneimittelforschung weit verbreitet sind. GNNs werden anhand von Graphen trainiert, die potenzielle Arzneimittelinteraktionen darstellen.\u00a0<\/span><\/p>\n

Doch wie die GNN zu ihren Vorhersagen kommen, ist wie eine Blackbox, in die wir nicht hineinschauen k\u00f6nnen\", so Prof. Bajorath.<\/span><\/p>\n

Um diesen Prozess umfassend zu entmystifizieren, analysierte das Team sechs verschiedene GNN-Architekturen. Andrea Mastropietro, Studienautor und Doktorand an der Universit\u00e4t Sapienza in Rom, erkl\u00e4rt: \"Die GNNs sind sehr abh\u00e4ngig von den Daten, mit denen sie trainiert werden.\"<\/span><\/p>\n

Die Forscher entdeckten, dass sich die GNNs haupts\u00e4chlich auf chemische \u00c4hnlichkeiten aus ihren Trainingsdaten st\u00fctzen, um Vorhersagen zu treffen, anstatt spezifische Wechselwirkungen zwischen Verbindungen und Proteinen zu lernen. <\/span><\/p>\n

Das bedeutet im Wesentlichen, dass sich die KI-Modelle oft an neue Interaktionen \"erinnern\", anstatt sie zu \"lernen\".<\/span><\/p>\n

Der \"Clevere Hans-Effekt\" in der KI<\/span><\/h2>\n

Die Forscher vergleichen dieses Ph\u00e4nomen mit dem \"Clever-Hans-Effekt\", bei dem ein Pferd scheinbar Arithmetik betreibt, indem es subtile Hinweise seines F\u00fchrers interpretiert, anstatt die Mathematik tats\u00e4chlich zu verstehen.\u00a0<\/span><\/p>\n

Auch bei den Vorhersagen der KI geht es eher darum, bekannte Daten abzurufen, als komplexe chemische Wechselwirkungen zu verstehen.<\/span><\/p>\n

Die Ergebnisse deuten darauf hin, dass die F\u00e4higkeit von GNNs, chemische Interaktionen zu lernen, \u00fcbersch\u00e4tzt wird und dass einfachere Methoden ebenso effektiv sein k\u00f6nnten.\u00a0<\/span><\/p>\n

Einige GNNs zeigten jedoch das Potenzial, mehr Interaktionen zu lernen, was darauf hindeutet, dass verbesserte Trainingstechniken ihre Leistung steigern k\u00f6nnten.<\/span><\/p>\n

Prof. Bajoraths <\/span>Das Team entwickelt au\u00dferdem Methoden zur Kl\u00e4rung der Funktionsweise von KI-Modellen im Rahmen der \"erkl\u00e4rbaren KI\", einem neuen Bereich, der die Entscheidungsprozesse der KI transparent und verst\u00e4ndlich machen soll.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

Wissenschaftler der Universit\u00e4t Bonn unter der Leitung von Professor Dr. J\u00fcrgen Bajorath haben das Innenleben von \"Black Box\"-KIs in der Pharmaforschung aufgedeckt. Ihre Studie, die k\u00fcrzlich in Nature Machine Intelligence ver\u00f6ffentlicht wurde, zeigt, dass KI-Modelle in der Arzneimittelforschung \u00fcberwiegend auf dem Abruf vorhandener Daten beruhen, anstatt neue chemische Wechselwirkungen zu lernen. Dies stellt bisherige Annahmen dar\u00fcber in Frage, wie KI in diesem Bereich Vorhersagen trifft. Forscher setzen maschinelles Lernen ein, um Molek\u00fcle zu finden, die effektiv mit Zielproteinen interagieren. Dazu geh\u00f6rt h\u00e4ufig die Vorhersage, welche Molek\u00fcle stark an Zielproteine binden werden, gefolgt von einer experimentellen Validierung dieser Vorhersagen. Diese Form der KI-gest\u00fctzten Arzneimittel<\/p>","protected":false},"author":2,"featured_media":8774,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[105,135],"class_list":["post-8773","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-machine-learning","tag-science"],"yoast_head":"\nResearchers aim to determine how 'black box' drug discovery models work | DailyAI<\/title>\n<meta name=\"description\" content=\"Scientists at the University of Bonn, led by Prof. Dr. J\u00fcrgen Bajorath, have uncovered the inner workings of \u2018black box\u2019 AIs involved in pharmaceutical research.\u00a0\" \/>\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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