FAIR data management: what does it mean for drug discovery?
The drug discovery community faces high costs in bringing safe and effective medicines to market, in part due to the rising volume and complexity of data which must be generated during the research and development process. Fully utilising these expensively created experimental and computational data...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Drug Discovery |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fddsv.2023.1226727/full |
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| author | Yojana Gadiya Yojana Gadiya Yojana Gadiya Vassilios Ioannidis Vassilios Ioannidis David Henderson Philip Gribbon Philip Gribbon Philippe Rocca-Serra Philippe Rocca-Serra Venkata Satagopam Susanna-Assunta Sansone Wei Gu Wei Gu |
| author_facet | Yojana Gadiya Yojana Gadiya Yojana Gadiya Vassilios Ioannidis Vassilios Ioannidis David Henderson Philip Gribbon Philip Gribbon Philippe Rocca-Serra Philippe Rocca-Serra Venkata Satagopam Susanna-Assunta Sansone Wei Gu Wei Gu |
| author_sort | Yojana Gadiya |
| collection | DOAJ |
| description | The drug discovery community faces high costs in bringing safe and effective medicines to market, in part due to the rising volume and complexity of data which must be generated during the research and development process. Fully utilising these expensively created experimental and computational data resources has become a key aim of scientists due to the clear imperative to leverage the power of artificial intelligence (AI) and machine learning-based analyses to solve the complex problems inherent in drug discovery. In turn, AI methods heavily rely on the quantity, quality, consistency, and scope of underlying training data. While pre-existing preclinical and clinical data cannot fully replace the need for de novo data generation in a project, having access to relevant historical data represents a valuable asset, as its reuse can reduce the need to perform similar experiments, therefore avoiding a “reinventing the wheel” scenario. Unfortunately, most suitable data resources are often archived within institutes, companies, or individual research groups and hence unavailable to the wider community. Hence, enabling the data to be Findable, Accessible, Interoperable, and Reusable (FAIR) is crucial for the wider community of drug discovery and development scientists to learn from the work performed and utilise the findings to enhance comprehension of their own research outcomes. In this mini-review, we elucidate the utility of FAIR data management across the drug discovery pipeline and assess the impact such FAIR data has made on the drug development process. |
| first_indexed | 2024-03-13T00:05:51Z |
| format | Article |
| id | doaj.art-7851d8367c8b4406ac6cfd28f4d4d00e |
| institution | Directory Open Access Journal |
| issn | 2674-0338 |
| language | English |
| last_indexed | 2024-03-13T00:05:51Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Drug Discovery |
| spelling | doaj.art-7851d8367c8b4406ac6cfd28f4d4d00e2023-07-13T00:34:58ZengFrontiers Media S.A.Frontiers in Drug Discovery2674-03382023-07-01310.3389/fddsv.2023.12267271226727FAIR data management: what does it mean for drug discovery?Yojana Gadiya0Yojana Gadiya1Yojana Gadiya2Vassilios Ioannidis3Vassilios Ioannidis4David Henderson5Philip Gribbon6Philip Gribbon7Philippe Rocca-Serra8Philippe Rocca-Serra9Venkata Satagopam10Susanna-Assunta Sansone11Wei Gu12Wei Gu13Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Hamburg, GermanyFraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD), Frankfurt, GermanyBonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, GermanyVital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, SwitzerlandUNIRIS, University of Lausanne, Lausanne, SwitzerlandBayer AG, Business Development & Licensing & OI, Pharmaceuticals, Berlin, GermanyFraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Hamburg, GermanyFraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD), Frankfurt, GermanyOxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, United KingdomAstraZeneca, Data Office, Data Science and AI Unit R&D, Cambridge, United KingdomLuxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, Esch-sur-Alzette, LuxembourgOxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, United KingdomLuxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, Esch-sur-Alzette, Luxembourg0Luxembourg National Data Service, Esch-sur-Alzette, LuxembourgThe drug discovery community faces high costs in bringing safe and effective medicines to market, in part due to the rising volume and complexity of data which must be generated during the research and development process. Fully utilising these expensively created experimental and computational data resources has become a key aim of scientists due to the clear imperative to leverage the power of artificial intelligence (AI) and machine learning-based analyses to solve the complex problems inherent in drug discovery. In turn, AI methods heavily rely on the quantity, quality, consistency, and scope of underlying training data. While pre-existing preclinical and clinical data cannot fully replace the need for de novo data generation in a project, having access to relevant historical data represents a valuable asset, as its reuse can reduce the need to perform similar experiments, therefore avoiding a “reinventing the wheel” scenario. Unfortunately, most suitable data resources are often archived within institutes, companies, or individual research groups and hence unavailable to the wider community. Hence, enabling the data to be Findable, Accessible, Interoperable, and Reusable (FAIR) is crucial for the wider community of drug discovery and development scientists to learn from the work performed and utilise the findings to enhance comprehension of their own research outcomes. In this mini-review, we elucidate the utility of FAIR data management across the drug discovery pipeline and assess the impact such FAIR data has made on the drug development process.https://www.frontiersin.org/articles/10.3389/fddsv.2023.1226727/fulldrug discoveryFAIR principlesdata managementdata sharingmachine learning |
| spellingShingle | Yojana Gadiya Yojana Gadiya Yojana Gadiya Vassilios Ioannidis Vassilios Ioannidis David Henderson Philip Gribbon Philip Gribbon Philippe Rocca-Serra Philippe Rocca-Serra Venkata Satagopam Susanna-Assunta Sansone Wei Gu Wei Gu FAIR data management: what does it mean for drug discovery? Frontiers in Drug Discovery drug discovery FAIR principles data management data sharing machine learning |
| title | FAIR data management: what does it mean for drug discovery? |
| title_full | FAIR data management: what does it mean for drug discovery? |
| title_fullStr | FAIR data management: what does it mean for drug discovery? |
| title_full_unstemmed | FAIR data management: what does it mean for drug discovery? |
| title_short | FAIR data management: what does it mean for drug discovery? |
| title_sort | fair data management what does it mean for drug discovery |
| topic | drug discovery FAIR principles data management data sharing machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fddsv.2023.1226727/full |
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