{"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\/da\/2024\/01\/researchers-aim-to-determine-how-black-box-drug-discovery-models-work\/","title":{"rendered":"Forskere vil finde ud af, hvordan \"black box\"-modeller til l\u00e6gemiddelopdagelse fungerer"},"content":{"rendered":"

Forskere ved universitetet i Bonn, ledet af professor Dr. J\u00fcrgen Bajorath, har afd\u00e6kket det indre arbejde i 'black box' AI'er, der er involveret i farmaceutisk forskning.\u00a0<\/b><\/p>\n

Deres <\/span>unders\u00f8gelse<\/span><\/a>som for nylig blev offentliggjort i Nature Machine Intelligence, afsl\u00f8rer, at AI-modeller til l\u00e6gemiddelopdagelse overvejende er afh\u00e6ngige af at genkalde sig eksisterende data snarere end at l\u00e6re nye kemiske interaktioner.\u00a0<\/span>Dette udfordrer tidligere antagelser om, hvordan AI laver forudsigelser p\u00e5 dette omr\u00e5de.<\/span><\/p>\n

Forskere bruger maskinl\u00e6ring til at finde molekyler, der effektivt interagerer med m\u00e5lproteiner, hvilket ofte indeb\u00e6rer forudsigelse af, hvilke molekyler der vil binde sig st\u00e6rkt til m\u00e5lproteiner, efterfulgt af eksperimentel validering af disse forudsigelser.<\/span><\/p>\n

Denne form for AI-assisteret l\u00e6gemiddelopdagelse oplevede store gennembrud i 2023, herunder en <\/span>MIT-udviklet model<\/span><\/a> der analyserede millioner af stoffer for potentielle terapeutiske effekter, AI-opdagede stoffer, der viste <\/span>L\u00f8fte om at bremse aldring<\/span><\/a>og AI-genererede proteiner, der viser <\/span>Fremragende bindingsstyrke<\/span><\/a>.<\/span><\/p>\n

Det sp\u00f8rgsm\u00e5l, Bajorath og teamet fors\u00f8gte at besvare, er, hvordan nogle af disse modeller opn\u00e5r deres resultater?<\/span><\/p>\n

Forskerteamet fokuserede p\u00e5 Graph Neural Networks (GNN'er), en type maskinl\u00e6ringsapplikation, der er meget udbredt inden for l\u00e6gemiddelopdagelse. GNN'er tr\u00e6nes ved hj\u00e6lp af grafer, der repr\u00e6senterer potentielle l\u00e6gemiddelinteraktioner.\u00a0<\/span><\/p>\n

Men som professor Bajorath p\u00e5peger: \"Hvordan GNN'er n\u00e5r frem til deres forudsigelser, er som en sort boks, vi ikke kan kigge ind i.\"<\/span><\/p>\n

For at afmystificere denne proces grundigt analyserede teamet seks forskellige GNN-arkitekturer. Andrea Mastropietro, der er forfatter til unders\u00f8gelsen og ph.d.-kandidat ved Sapienza University i Rom, siger: \"GNN'erne er meget afh\u00e6ngige af de data, de er tr\u00e6net med.\"<\/span><\/p>\n

Forskerne opdagede, at GNN'erne overvejende er afh\u00e6ngige af kemiske ligheder fra deres tr\u00e6ningsdata for at komme med forudsigelser i stedet for at l\u00e6re specifikke interaktioner mellem forbindelser og proteiner. <\/span><\/p>\n

Det betyder i bund og grund, at AI-modellerne ofte \"husker\" snarere end \"l\u00e6rer\" nye interaktioner.<\/span><\/p>\n

\"Klog Hans-effekten\" i AI<\/span><\/h2>\n

Forskerne sammenligner f\u00e6nomenet med \"Clever Hans-effekten\", hvor en hest ser ud til at regne ved at fortolke subtile signaler fra sin f\u00f8rer i stedet for rent faktisk at forst\u00e5 matematik.\u00a0<\/span><\/p>\n

P\u00e5 samme m\u00e5de handler AI'ens forudsigelser mere om at genkalde sig kendte data end om at forst\u00e5 komplekse kemiske interaktioner.<\/span><\/p>\n

Resultaterne tyder p\u00e5, at GNN's evne til at l\u00e6re kemiske interaktioner er overvurderet, og at enklere metoder kan v\u00e6re lige s\u00e5 effektive.\u00a0<\/span><\/p>\n

Nogle GNN'er viste dog potentiale til at l\u00e6re flere interaktioner, hvilket indikerer, at forbedrede tr\u00e6ningsteknikker kan forbedre deres pr\u00e6stationer.<\/span><\/p>\n

Prof. Bajoraths <\/span>Teamet udvikler ogs\u00e5 metoder til at tydeligg\u00f8re AI-modellers funktionalitet i jagten p\u00e5 \"Explainable AI\", et nyt felt, der skal g\u00f8re AI's beslutningsprocesser gennemsigtige og forst\u00e5elige.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

Forskere ved universitetet i Bonn under ledelse af professor Dr. J\u00fcrgen Bajorath har afd\u00e6kket det indre arbejde i \"black box\" AI'er, der er involveret i farmaceutisk forskning. Deres unders\u00f8gelse, som for nylig blev offentliggjort i Nature Machine Intelligence, afsl\u00f8rer, at AI-modeller inden for l\u00e6gemiddelopdagelse overvejende er afh\u00e6ngige af at huske eksisterende data snarere end at l\u00e6re nye kemiske interaktioner. Det udfordrer tidligere antagelser om, hvordan AI laver forudsigelser p\u00e5 dette omr\u00e5de. Forskere bruger maskinl\u00e6ring til at finde molekyler, der effektivt interagerer med m\u00e5lproteiner, hvilket ofte indeb\u00e6rer forudsigelse af, hvilke molekyler der vil binde sig st\u00e6rkt til m\u00e5lproteiner, efterfulgt af eksperimentel validering af disse forudsigelser. Denne form for AI-assisteret l\u00e6gemiddel<\/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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