eBook
Artificial Intelligence for Drug Product Lifecycle Applications
| Title: | Artificial Intelligence for Drug Product Lifecycle Applications |
|---|---|
| Description: | Artificial Intelligence for Drug Product Lifecycle Applications explains the use of artificial intelligence (AI) in drug discovery and development paths, including the clinical and postapproval phases. This book gives methods for each of the drug development steps, from the fundamentals to postapproval drug product. AI is a synergistic assembly of enhanced optimization strategies with particular applications in pharmaceutical development and advanced tools for promoting cost-effectiveness throughout the drug lifecycle. Specifically, AI brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patients comply with their treatments.Accelerated pharmaceutical development and drug product approval rates will enable larger profits from patent-protected market exclusivity. This book offers the tools and knowledge to create the right AI strategy to extend the landscape of AI applications across the drug lifecycle. It is especially useful for pharmaceutical scientists, health care professionals, and regulatory scientists, as well as advanced students and postgraduates actively involved in pharmaceutical product and process development involving the use of artificial intelligence in drug delivery applications. - Classifies AI methodologies and application examples into different categories representing the various steps of the drug development cycle - Combines timely literature review with clear artworks to improve understanding - Examines deep learning and machine learning in drug discovery |
| Authors: | Alberto Pais, Carla Vitorino, Sandra Nunes, Tânia Cova |
| Resource Type: | eBook. |
| Subjects: | Artificial intelligence--Medical applications, Drug development--Data processing |
| Categories: | MEDICAL / Pharmacology, BUSINESS & ECONOMICS / Industries / Pharmaceutical & Biotechnology |
| Database: | eBook Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edsebk DbLabel: eBook Index An: 3569089 RelevancyScore: 975 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 974.776672363281 |
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| Items | – Name: Title Label: Title Group: Ti Data: Artificial Intelligence for Drug Product Lifecycle Applications – Name: Abstract Label: Description Group: Ab Data: Artificial Intelligence for Drug Product Lifecycle Applications explains the use of artificial intelligence (AI) in drug discovery and development paths, including the clinical and postapproval phases. This book gives methods for each of the drug development steps, from the fundamentals to postapproval drug product. AI is a synergistic assembly of enhanced optimization strategies with particular applications in pharmaceutical development and advanced tools for promoting cost-effectiveness throughout the drug lifecycle. Specifically, AI brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patients comply with their treatments.Accelerated pharmaceutical development and drug product approval rates will enable larger profits from patent-protected market exclusivity. This book offers the tools and knowledge to create the right AI strategy to extend the landscape of AI applications across the drug lifecycle. It is especially useful for pharmaceutical scientists, health care professionals, and regulatory scientists, as well as advanced students and postgraduates actively involved in pharmaceutical product and process development involving the use of artificial intelligence in drug delivery applications. - Classifies AI methodologies and application examples into different categories representing the various steps of the drug development cycle - Combines timely literature review with clear artworks to improve understanding - Examines deep learning and machine learning in drug discovery – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Alberto+Pais%22">Alberto Pais</searchLink><br /><searchLink fieldCode="AR" term="%22Carla+Vitorino%22">Carla Vitorino</searchLink><br /><searchLink fieldCode="AR" term="%22Sandra+Nunes%22">Sandra Nunes</searchLink><br /><searchLink fieldCode="AR" term="%22Tânia+Cova%22">Tânia Cova</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence--Medical+applications%22">Artificial intelligence--Medical applications</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+development--Data+processing%22">Drug development--Data processing</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22MEDICAL+%2F+Pharmacology%22">MEDICAL / Pharmacology</searchLink><br /><searchLink fieldCode="ZK" term="%22BUSINESS+%26+ECONOMICS+%2F+Industries+%2F+Pharmaceutical+%26+Biotechnology%22">BUSINESS & ECONOMICS / Industries / Pharmaceutical & Biotechnology</searchLink> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=3569089 |
| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 615.190028563 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Artificial intelligence--Medical applications Type: general – SubjectFull: Drug development--Data processing Type: general Titles: – TitleFull: Artificial Intelligence for Drug Product Lifecycle Applications Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Alberto Pais – PersonEntity: Name: NameFull: Carla Vitorino – PersonEntity: Name: NameFull: Sandra Nunes – PersonEntity: Name: NameFull: Tânia Cova – PersonEntity: Name: NameFull: Alberto Pais – PersonEntity: Name: NameFull: Carla Vitorino – PersonEntity: Name: NameFull: Sandra Nunes – PersonEntity: Name: NameFull: Tânia Cova IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2024 – D: 23 M: 12 Type: profile Y: 2024 Identifiers: – Type: isbn-print Value: 9780323918190 – Type: isbn-electronic Value: 9780323972512 Titles: – TitleFull: Artificial Intelligence for Drug Product Lifecycle Applications Type: main |
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