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Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors

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dc.contributor.authorCova, Tânia
dc.contributor.authorVitorino, Carla
dc.contributor.authorFerreira, Márcio
dc.contributor.authorNunes, Sandra
dc.contributor.authorRondon-Villarreal, Paola
dc.contributor.authorPais, Alberto
dc.date.accessioned2022-08-03T21:09:45Z
dc.date.available2022-08-03T21:09:45Z
dc.date.issued2021-11-04
dc.descriptionDigitalspa
dc.description.abstractArtificial intelligence (AI) consists of a synergistic assembly of enhanced optimization strategies with wide application in drug discovery and development, providing advanced tools for promoting cost-effectiveness throughout drug life cycle. Specifically, AI brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patients complying with their treatments. Accelerated pharmaceutical development and drug product approval rates can further benefit from the quantum computing (QC) technology, which will ultimately enable larger profits from patent-protected market exclusivity. Key pharma stakeholders are endorsing cutting-edge technologies based on AI and QC , covering drug discovery, preclinical and clinical development, and postapproval activities. Indeed, AI-QC applications are expected to become standard in the pharma operating model over the next 5–10 years. Generalizing scalability to larger pharmaceutical problems instead of specialization is now the main principle for transforming pharmaceutical tasks on multiple fronts, for which systematic and cost-effective solutions have benefited in areas such as molecular screening, synthetic pathway design, and drug discovery and development. The information generated by coupling the life cycle of drugs and AI and/or QC through data-driven analysis, neural network prediction, and chemical system monitoring will enable (1) better understanding of the complexity of process data, (2) streamlining the design of experiments, (3) discovering new molecular targets and materials, and also (4) planning or rethinking upcoming pharmaceutical challenges The power of AI-QC makes accessible a range of different pharmaceutical problems and their rationalization that have not been previously addressed due to a lack of appropriate analytical tools, demonstrating the breadth of potential applications of these emerging multidimensional approaches. In this context, creating the right AI-QC strategy often involves a steep learning path, especially given the embryonic stage of the industry development and the relative lack of case studies documenting success. As such, a comprehensive knowledge of the underlying pillars is imperative to extend the landscape of applications across the drug life cycle. The topics enclosed in this chapter will focus on AI-QC methods applied to drug discovery and development, with emphasis on the most recent advances in this field.spa
dc.format.mimetypeapplication/pdfspa
dc.identifier.doihttps://doi.org/10.1007/978-1-0716-1787-8_14
dc.identifier.eisbn978-1-0716-1787-8spa
dc.identifier.isbn978-1-0716-1786-1spa
dc.identifier.urihttps://repositorio.udes.edu.co/handle/001/7336
dc.language.isoengspa
dc.publisher.placeSuizaspa
dc.relation.citationendpage347spa
dc.relation.citationstartpage321spa
dc.relation.citesCova, T., Vitorino, C., Ferreira, M., Nunes, S., Rondon-Villarreal, P., Pais, A. (2022). Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors. In: Heifetz, A. (eds) Artificial Intelligence in Drug Design. Methods in Molecular Biology, vol 2390. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1787-8_14
dc.relation.ispartofbookArtificial Intelligence in Drug Designspa
dc.rights© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Naturespa
dc.rights.accessrightsinfo:eu-repo/semantics/closedAccessspa
dc.rights.creativecommonsAtribución 4.0 Internacional (CC BY 4.0)spa
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/spa
dc.sourcehttps://link.springer.com/protocol/10.1007/978-1-0716-1787-8_14spa
dc.subject.proposalArtificial intelligenceeng
dc.subject.proposalMachine learningeng
dc.subject.proposalQuantum computingeng
dc.subject.proposalDrug discoveryeng
dc.subject.proposalDrug developmenteng
dc.subject.proposalDrug life cycleeng
dc.titleArtificial Intelligence and Quantum Computing as the Next Pharma Disruptorsspa
dc.typeCapítulo - Parte de Librospa
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/bookPartspa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTspa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
dcterms.audienceTodas las Audienciasspa
dspace.entity.typePublication
oaire.accessrightshttp://purl.org/coar/access_right/c_14cbspa
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
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