Artificial Intelligence-Based Methods for Drug Repurposing and Development in Cancer
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 r...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/5/2798 |
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| author | Sara Herráiz-Gil Elisa Nygren-Jiménez Diana N. Acosta-Alonso Carlos León Sara Guerrero-Aspizua |
| author_facet | Sara Herráiz-Gil Elisa Nygren-Jiménez Diana N. Acosta-Alonso Carlos León Sara Guerrero-Aspizua |
| author_sort | Sara Herráiz-Gil |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-bbaaea508d6743b888c2434c7a84cbf3 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-bbaaea508d6743b888c2434c7a84cbf32025-08-20T02:52:41ZengMDPI AGApplied Sciences2076-34172025-03-01155279810.3390/app15052798Artificial Intelligence-Based Methods for Drug Repurposing and Development in CancerSara Herráiz-Gil0Elisa Nygren-Jiménez1Diana N. Acosta-Alonso2Carlos León3Sara Guerrero-Aspizua4Department of Bioengineering, Carlos III University, UC3M-IISFJD-CIEMAT-CIBERER, Av. de la Universidad 30, Leganés, 28911 Madrid, SpainBioFab i3D Lab-Biofabrication and 3D (Bio)printing Laboratory, Department of Human Anatomy and Embryology, Faculty of Medicine, University of Granada, 18016 Granada, SpainDepartment of Bioengineering, Carlos III University, UC3M-IISFJD-CIEMAT-CIBERER, Av. de la Universidad 30, Leganés, 28911 Madrid, SpainDepartment of Bioengineering, Carlos III University, UC3M-IISFJD-CIEMAT-CIBERER, Av. de la Universidad 30, Leganés, 28911 Madrid, SpainDepartment of Bioengineering, Carlos III University, UC3M-IISFJD-CIEMAT-CIBERER, Av. de la Universidad 30, Leganés, 28911 Madrid, SpainDrug 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.https://www.mdpi.com/2076-3417/15/5/2798drug repurposingartificial intelligencemachine learningcancer |
| spellingShingle | Sara Herráiz-Gil Elisa Nygren-Jiménez Diana N. Acosta-Alonso Carlos León Sara Guerrero-Aspizua Artificial Intelligence-Based Methods for Drug Repurposing and Development in Cancer Applied Sciences drug repurposing artificial intelligence machine learning cancer |
| title | Artificial Intelligence-Based Methods for Drug Repurposing and Development in Cancer |
| title_full | Artificial Intelligence-Based Methods for Drug Repurposing and Development in Cancer |
| title_fullStr | Artificial Intelligence-Based Methods for Drug Repurposing and Development in Cancer |
| title_full_unstemmed | Artificial Intelligence-Based Methods for Drug Repurposing and Development in Cancer |
| title_short | Artificial Intelligence-Based Methods for Drug Repurposing and Development in Cancer |
| title_sort | artificial intelligence based methods for drug repurposing and development in cancer |
| topic | drug repurposing artificial intelligence machine learning cancer |
| url | https://www.mdpi.com/2076-3417/15/5/2798 |
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