<rdf:RDF xmlns:rdf="http://www.openarchives.org/OAI/2.0/rdf/" xmlns:ow="http://www.ontoweb.org/ontology/1#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:ds="http://dspace.org/ds/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/rdf/ http://www.openarchives.org/OAI/2.0/rdf.xsd">
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      <dc:title>Artificial Intelligence-Based Methods for Drug Repurposing and Development in Cancer</dc:title>
      <dc:creator>Herráiz Gil, Sara</dc:creator>
      <dc:creator>Nygren-Jiménez, Elisa</dc:creator>
      <dc:creator>Acosta-Alonso, Diana N.</dc:creator>
      <dc:creator>León, Carlos</dc:creator>
      <dc:creator>Guerrero Aspizua, Sara</dc:creator>
      <dc:subject>Drug repurposing</dc:subject>
      <dc:subject>Artificial intelligence</dc:subject>
      <dc:subject>Machine learning</dc:subject>
      <dc:subject>Cancer</dc:subject>
      <dc:description>This study was supported in part by grants from the Spanish Ministry of Science and Innovation and the European Regional Development fund (PID2020-119792RB-I00), the Institute of Health Carlos III (RD21/0001/0022, Spanish Network of Advanced Therapies, TERAV-ISCIII), and the Fundación Mutua Madrileña (project: FMM-AP16030-2024). SHG is supported by a UC3M–PhD research training scholarship (PIPF).</dc:description>
      <dc:description>Drug discovery and development remains a complex and time-consuming process, often hindered by high costs and low success rates. In the big data era, artificial intelligence (AI) has emerged as a promising tool to accelerate and optimize these processes, particularly in the field of oncology. This review explores the application of AI-based methods for drug repurposing and natural product-inspired drug design in cancer, focusing on their potential to address the challenges and limitations of traditional drug discovery approaches. We delve into various AI-based approaches (machine learning, deep learning, and others) that are currently being employed for these purposes, and the role of experimental techniques in these approaches. By systematically reviewing the literature, we aim to provide a comprehensive overview of the current state of AI-assisted cancer drug discovery workflows, highlighting AI’s contributions to accelerating drug development, reducing costs, and improving therapeutic outcomes. This review also discusses the challenges and opportunities associated with the integration of AI into the drug discovery pipeline, such as data quality, interpretability, and ethical considerations.</dc:description>
      <dc:date>2025-03-13T09:28:33Z</dc:date>
      <dc:date>2025-03-13T09:28:33Z</dc:date>
      <dc:date>2025-03-05</dc:date>
      <dc:type>journal article</dc:type>
      <dc:identifier>Herráiz-Gil, S.; Nygren-Jiménez, E.; Acosta-Alonso, D.N.; León, C.; Guerrero-Aspizua, S. Artificial Intelligence-Based Methods for Drug Repurposing and Development in Cancer. Appl. Sci. 2025, 15, 2798. https://doi.org/10.3390/app15052798</dc:identifier>
      <dc:identifier>https://hdl.handle.net/10481/103029</dc:identifier>
      <dc:identifier>10.3390/app15052798</dc:identifier>
      <dc:language>eng</dc:language>
      <dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights>
      <dc:rights>open access</dc:rights>
      <dc:rights>Atribución 4.0 Internacional</dc:rights>
      <dc:publisher>MDPI</dc:publisher>
   </ow:Publication>
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