{"id":5095,"date":"2023-11-17T13:38:42","date_gmt":"2023-11-17T12:38:42","guid":{"rendered":"https:\/\/www.kode-solutions.net\/?page_id=5095"},"modified":"2024-07-31T11:54:14","modified_gmt":"2024-07-31T09:54:14","slug":"queen","status":"publish","type":"page","link":"https:\/\/www.kode-solutions.net\/it\/prodotti-e-framework-di-ai\/alchemy\/queen\/","title":{"rendered":"Queen"},"content":{"rendered":"\r\n\t
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QSAR modelling<\/strong><\/h1>\n\n\n\n

Under<\/strong> Effective<\/strong><\/h1>\n\n\n\n

and Efficient<\/strong><\/h1>\n\n\n\n

Neural-Networks<\/strong><\/h1>\n\n\n\n

Negli ultimi anni, la modellazione QSAR basata su reti neurali ha riscontrato crescente popolarit\u00e0, grazie alla sua capacit\u00e0 di catturare relazioni complesse e non lineari nei dati chimici. Questi modelli hanno infatti\u00a0 dimostrato di essere gli algoritmi pi\u00f9 potenti e versatili per gestire questo tipo di problemi, sebbene richiedano spesso una parametrizzazione complessa.<\/p>\n<\/div><\/div>\n\n\n\n

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QUEEN \u00e8 progettata per abbattere proprio questa complessit\u00e0.<\/p>\n\n\n\n

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Scopri di pi\u00f9 su FAST<\/a> e sull’intera Suite ALChemy<\/a><\/p>\n<\/div>\n<\/body><\/html>\n\n\n

\"\"<\/img><\/figure>\n<\/body><\/html><\/div>\n<\/div>\n\n\t\t\t\t\t<\/div>\r\n\t<\/div>\r\n\r\n\t\n\n\t\t
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 Vuoi saperne di pi\u00f9? Prenota una presentazione con il nostro team <\/p>\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t

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La modellazione QSAR diventa semplice<\/h2>\n\n\n\n

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QUEEN riunisce tutte le fasi del processo di modellazione QSAR basate su reti neurali in un’interfaccia semplice e intuitiva. Incorpora, infatti, un algoritmo di ottimizzazione degli iperparametri completamente automatizzato basato sul metodo Bayesiano e, una volta addestrato sul modello finale, la possibilit\u00e0 di unire centinaia di modelli individuali in un’unica risposta.<\/mark><\/p>\n\n\n\n

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Questo approccio garantisce risultati robusti e performanti in qualsiasi condizione, pur mantenendo una configurazione semplice e quasi priva di parametri.<\/p>\n<\/div>\n<\/body><\/html><\/div>\n\n\t\t\t\t\t<\/div>\r\n\t<\/div>\r\n\r\n\t\n\n\r\n\t

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Il metodo in 4 passaggi<\/h2>\n<\/div>\n\n\n\n
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\r\n\t\t\t\t\t\t\tImportazione del set di dati\t\t\t\t\t\t\t\t\t\t\t\t\t<\/h3>\r\n\t\t\t\t\t<\/div> \r\n\t\t\t\t\t
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Le informazioni minime per utilizzare in efficacemente questo software consistono in un dataset con le seguenti componenti: variabile target e matrice dei descrittori molecolari. Ogni volta che QUEEN viene eseguito in linea con FAST, tutte le informazioni vengono automaticamente prese dall’output di FAST.<\/p>\n


<\/p>\n

Se nel dataset non sono presenti etichette training\/test, QUEEN pu\u00f2 gestire la suddivisione applicando uno dei due approcci attualmente implementati: Random splitting and Venetian blind (impostazione predefinita).<\/p>\t\t\t\t\t\t

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\r\n\t\t\t\t\t\t\tOttimizzazione degli iperparametri\t\t\t\t\t\t\t\t\t\t\t\t\t<\/h3>\r\n\t\t\t\t\t<\/div> \r\n\t\t\t\t\t
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Ottimizzazione degli iperparametri del modello di rete neurale tramite un algoritmo Bayesiano. Se i parametri fissi sono gi\u00e0 definiti, il passaggio pu\u00f2 essere saltato. <\/p>\t\t\t\t\t\t

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\r\n\t\t\t\t\t\t\tTraining\t\t\t\t\t\t\t\t\t\t\t\t\t<\/h3>\r\n\t\t\t\t\t<\/div> \r\n\t\t\t\t\t
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Vengono addestrate diverse repliche che differiscono per i pesi casuali iniziali. Per ciascuna di esse, viene calcolato un valore di loss sui campioni di calibrazione in termini di errore quadratico medio e categorical-cross-entropy rispettivamente per la regressione e la classificazione. La replica che presenta le migliori prestazioni predittive viene scelta come modello finale. <\/p>\t\t\t\t\t\t

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\r\n\t\t\t\t\t\t\tApplicability Domain\t\t\t\t\t\t\t\t\t\t\t\t\t<\/h3>\r\n\t\t\t\t\t<\/div> \r\n\t\t\t\t\t
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Ogni predizione viene fornita con un punteggio e un’etichetta di Applicability Domain (AD) che indica l’affidabilit\u00e0 del risultato del modello per il campione fornito. <\/p>\t\t\t\t\t\t

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Ricarica Modello e Runner<\/strong><\/h2>\n\n\n\n


<\/br><\/p>\n\n\n\n

Con i file ottenuti (file di log o file di esportazione), \u00e8 possibile ricaricare il modello addestrato di Machine Learning. Il file di esportazione prodotto al termine del processo di training pu\u00f2 essere riaperto da QUEEN e utilizzato per prevedere la propriet\u00e0 target su qualsiasi nuovo campione.<\/p>\n\n\n\n

<\/div>\n\n\n\n

Scopri di pi\u00f9 sull’intera Suite ALChemy<\/a><\/p>\n<\/div>\n<\/body><\/html>\n\n\n

\"\"<\/img><\/figure>\n<\/body><\/html><\/div>\n<\/div>\n\n\t\t\t\t\t<\/div>\r\n\t<\/div>\r\n\r\n\t\n\n\t\t
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Hai domande sui nostri prodotti?<\/p>\n

Scrivici via mail!<\/p>\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t

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