{"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\/nb\/2024\/01\/researchers-aim-to-determine-how-black-box-drug-discovery-models-work\/","title":{"rendered":"Forskere skal finne ut hvordan \"black box\"-modeller for legemiddelforskning fungerer"},"content":{"rendered":"

Forskere ved Universitetet i Bonn, ledet av professor Dr. J\u00fcrgen Bajorath, har avdekket hvordan \"svarte bokser\" med kunstig intelligens fungerer i farmas\u00f8ytisk forskning.\u00a0<\/b><\/p>\n

Deres <\/span>studie<\/span><\/a>som nylig ble publisert i Nature Machine Intelligence, avsl\u00f8rer at AI-modeller for legemiddeloppdagelse hovedsakelig baserer seg p\u00e5 \u00e5 gjenkalle eksisterende data i stedet for \u00e5 l\u00e6re seg nye kjemiske interaksjoner.\u00a0<\/span>Dette utfordrer tidligere antakelser om hvordan kunstig intelligens gj\u00f8r sp\u00e5dommer p\u00e5 dette feltet.<\/span><\/p>\n

Forskere bruker maskinl\u00e6ring til \u00e5 finne molekyler som effektivt interagerer med m\u00e5lproteiner, noe som ofte inneb\u00e6rer \u00e5 forutsi hvilke molekyler som vil binde seg sterkt til m\u00e5lproteiner, etterfulgt av eksperimentell validering av disse prediksjonene.<\/span><\/p>\n

Denne formen for AI-assistert legemiddeloppdagelse fikk store gjennombrudd i 2023, inkludert en <\/span>MIT-utviklet modell<\/span><\/a> som analyserte millioner av stoffer for potensielle terapeutiske effekter, har AI-oppdagede legemidler som viser <\/span>lovende for \u00e5 bremse aldring<\/span><\/a>og AI-genererte proteiner som viser <\/span>utmerket bindingsstyrke<\/span><\/a>.<\/span><\/p>\n

Sp\u00f8rsm\u00e5let Bajorath og teamet fors\u00f8kte \u00e5 finne svar p\u00e5, er hvordan noen av disse modellene oppn\u00e5r sine resultater.<\/span><\/p>\n

Forskerteamet fokuserte p\u00e5 grafnevrale nettverk (GNN), en type maskinl\u00e6ringsapplikasjon som er mye brukt i legemiddelforskning. GNN-er trenes opp ved hjelp av grafer som representerer potensielle legemiddelinteraksjoner.\u00a0<\/span><\/p>\n

Men som professor Bajorath p\u00e5peker: \"Hvordan GNN-ene kommer frem til sine sp\u00e5dommer, er som en svart boks vi ikke kan f\u00e5 et glimt inn i.\"<\/span><\/p>\n

For \u00e5 avmystifisere denne prosessen grundig analyserte teamet seks ulike GNN-arkitekturer. Andrea Mastropietro, forfatter av studien og doktorgradskandidat ved Sapienza-universitetet i Roma, sier: \"GNN-ene er sv\u00e6rt avhengige av dataene de er trent med.\"<\/span><\/p>\n

Forskerne oppdaget at GNN-ene hovedsakelig baserer seg p\u00e5 kjemiske likheter fra treningsdataene sine for \u00e5 komme med sp\u00e5dommer, i stedet for \u00e5 l\u00e6re seg spesifikke interaksjoner mellom forbindelser og proteiner. <\/span><\/p>\n

Det betyr i praksis at AI-modellene ofte \"husker\" i stedet for \u00e5 \"l\u00e6re\" nye interaksjoner.<\/span><\/p>\n

\"Clever Hans-effekten\" innen AI<\/span><\/h2>\n

Forskerne sammenligner dette fenomenet med \"Clever Hans-effekten\", der en hest ser ut til \u00e5 utf\u00f8re aritmetikk ved \u00e5 tolke subtile signaler fra f\u00f8reren sin i stedet for \u00e5 forst\u00e5 matematikk.\u00a0<\/span><\/p>\n

P\u00e5 samme m\u00e5te handler AI-ens prediksjoner mer om \u00e5 gjenkalle kjente data enn \u00e5 forst\u00e5 komplekse kjemiske interaksjoner.<\/span><\/p>\n

Funnene tyder p\u00e5 at GNNs evne til \u00e5 l\u00e6re kjemiske interaksjoner er overvurdert, og at enklere metoder kan v\u00e6re like effektive.\u00a0<\/span><\/p>\n

Noen GNN-er viste imidlertid potensial for \u00e5 l\u00e6re flere interaksjoner, noe som tyder p\u00e5 at forbedrede oppl\u00e6ringsteknikker kan forbedre ytelsen deres.<\/span><\/p>\n

Prof. Bajoraths <\/span>teamet utvikler ogs\u00e5 metoder for \u00e5 klargj\u00f8re AI-modellenes funksjonalitet i jakten p\u00e5 \"Explainable AI\", et fremvoksende felt som skal gj\u00f8re AIs beslutningsprosesser transparente og forst\u00e5elige.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

Forskere ved Universitetet i Bonn, ledet av professor Dr. J\u00fcrgen Bajorath, har avdekket hvordan \"svarte bokser\" med kunstig intelligens fungerer i farmas\u00f8ytisk forskning. Studien deres, som nylig ble publisert i Nature Machine Intelligence, avsl\u00f8rer at AI-modeller i legemiddelforskning hovedsakelig er avhengig av \u00e5 huske eksisterende data i stedet for \u00e5 l\u00e6re seg nye kjemiske interaksjoner. Dette utfordrer tidligere antakelser om hvordan kunstig intelligens lager prediksjoner p\u00e5 dette feltet. Forskere bruker maskinl\u00e6ring til \u00e5 finne molekyler som effektivt interagerer med m\u00e5lproteiner, noe som ofte inneb\u00e6rer \u00e5 forutsi hvilke molekyler som vil binde seg sterkt til m\u00e5lproteiner, etterfulgt av eksperimentell validering av disse prediksjonene. Denne formen for AI-assistert legemiddel<\/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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