{"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\/it\/2024\/01\/researchers-aim-to-determine-how-black-box-drug-discovery-models-work\/","title":{"rendered":"I ricercatori mirano a determinare come funzionano i modelli di scoperta dei farmaci \"a scatola nera\"."},"content":{"rendered":"

Gli scienziati dell'Universit\u00e0 di Bonn, guidati dal professor J\u00fcrgen Bajorath, hanno scoperto il funzionamento interno delle IA \"scatola nera\" coinvolte nella ricerca farmaceutica.\u00a0<\/b><\/p>\n

Il loro <\/span>studio<\/span><\/a>pubblicato di recente su Nature Machine Intelligence, rivela che i modelli di IA per la scoperta di farmaci dipendono prevalentemente dal richiamo di dati esistenti piuttosto che dall'apprendimento di nuove interazioni chimiche.\u00a0<\/span>Ci\u00f2 mette in discussione le precedenti ipotesi sul modo in cui l'IA fa previsioni in questo campo.<\/span><\/p>\n

I ricercatori utilizzano l'apprendimento automatico per individuare le molecole che interagiscono efficacemente con le proteine bersaglio, il che spesso comporta la previsione di quali molecole si legheranno fortemente alle proteine bersaglio, seguita dalla convalida sperimentale di queste previsioni.<\/span><\/p>\n

Questa forma di scoperta di farmaci assistita dall'intelligenza artificiale ha visto importanti scoperte nel 2023, tra cui una <\/span>Modello sviluppato dal MIT<\/span><\/a> che ha analizzato milioni di composti alla ricerca di potenziali effetti terapeutici, i farmaci scoperti dall'intelligenza artificiale che mostrano <\/span>promettenti per rallentare l'invecchiamento<\/span><\/a>e le proteine generate dall'AI che mostrano <\/span>eccellente forza di legame<\/span><\/a>.<\/span><\/p>\n

La domanda a cui Bajorath e il team hanno cercato di rispondere \u00e8: come fanno alcuni di questi modelli a raggiungere i loro risultati?<\/span><\/p>\n

Il team di ricerca si \u00e8 concentrato sulle reti neurali grafiche (GNN), un tipo di applicazione di apprendimento automatico ampiamente utilizzata nella scoperta di farmaci. Le GNN vengono addestrate utilizzando grafici che rappresentano le potenziali interazioni tra farmaci.\u00a0<\/span><\/p>\n

Tuttavia, come sottolinea il Prof. Bajorath, \"il modo in cui i GNN arrivano alle loro previsioni \u00e8 come una scatola nera che non riusciamo a scrutare\".<\/span><\/p>\n

Per demistificare in modo completo questo processo, il team ha analizzato sei diverse architetture GNN. Andrea Mastropietro, autore dello studio e dottorando all'Universit\u00e0 Sapienza di Roma, afferma: \"I GNN dipendono molto dai dati con cui vengono addestrati\".<\/span><\/p>\n

I ricercatori hanno scoperto che i GNN si basano prevalentemente sulle somiglianze chimiche dei dati di addestramento per fare previsioni, piuttosto che imparare interazioni specifiche tra composti e proteine. <\/span><\/p>\n

Ci\u00f2 significa essenzialmente che i modelli di intelligenza artificiale spesso \"ricordano\" piuttosto che \"imparano\" nuove interazioni.<\/span><\/p>\n

L'effetto \"Clever Hans\" nell'IA<\/span><\/h2>\n

I ricercatori paragonano questo fenomeno all'\"effetto Clever Hans\", in cui un cavallo sembra eseguire l'aritmetica interpretando sottili indicazioni del suo conduttore piuttosto che comprendere effettivamente la matematica.\u00a0<\/span><\/p>\n

Allo stesso modo, le previsioni dell'IA si basano pi\u00f9 sul richiamo di dati noti che sulla comprensione di interazioni chimiche complesse.<\/span><\/p>\n

I risultati suggeriscono che la capacit\u00e0 dei GNN di apprendere le interazioni chimiche \u00e8 sovrastimata e che metodi pi\u00f9 semplici potrebbero essere altrettanto efficaci.\u00a0<\/span><\/p>\n

Tuttavia, alcuni GNN hanno mostrato un potenziale nell'apprendimento di pi\u00f9 interazioni, indicando che il miglioramento delle tecniche di addestramento potrebbe migliorare le loro prestazioni.<\/span><\/p>\n

Il Prof. Bajorath <\/span>Il team sta anche sviluppando metodi per chiarire le funzionalit\u00e0 dei modelli di IA, nell'ottica dell'\"IA spiegabile\", un campo emergente per rendere trasparenti e comprensibili i processi decisionali dell'IA.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

Gli scienziati dell'Universit\u00e0 di Bonn, guidati dal professor J\u00fcrgen Bajorath, hanno scoperto il funzionamento interno delle IA \"scatola nera\" coinvolte nella ricerca farmaceutica. Il loro studio, recentemente pubblicato su Nature Machine Intelligence, rivela che i modelli di IA per la scoperta di farmaci dipendono prevalentemente dal richiamo di dati esistenti piuttosto che dall'apprendimento di nuove interazioni chimiche. Ci\u00f2 mette in discussione le precedenti ipotesi sul modo in cui l'IA fa previsioni in questo campo. I ricercatori utilizzano l'apprendimento automatico per individuare le molecole che interagiscono efficacemente con le proteine bersaglio, il che spesso comporta la previsione di quali molecole si legheranno fortemente alle proteine bersaglio, seguita dalla convalida sperimentale di queste previsioni. Questa forma di farmaco assistito dall'AI<\/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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