Best practices for artificial intelligence in life sciences research
•Use of artificial intelligence (AI) and machine learning (ML) methods in life sciences research calls for best practices.•At the core of any AI project should be quality data that are well-understood, consistent and use FAIR principles.•Models should be published with code, training and testing dat...
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| Veröffentlicht in: | Drug discovery today 2021-05, Vol.26 (5), p.1107-1110 |
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| container_end_page | 1110 |
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| container_issue | 5 |
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| container_title | Drug discovery today |
| container_volume | 26 |
| creator | Makarov, Vladimir A. Stouch, Terry Allgood, Brandon Willis, Chris D. Lynch, Nick |
| description | •Use of artificial intelligence (AI) and machine learning (ML) methods in life sciences research calls for best practices.•At the core of any AI project should be quality data that are well-understood, consistent and use FAIR principles.•Models should be published with code, training and testing data along with scientific results.•Research organizations should build skills in quality AI/ML modeling.
We describe 11 best practices for the successful use of artificial intelligence and machine learning in pharmaceutical and biotechnology research at the data, technology and organizational management levels. |
| doi_str_mv | 10.1016/j.drudis.2021.01.017 |
| format | Article |
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| identifier | ISSN: 1359-6446 |
| ispartof | Drug discovery today, 2021-05, Vol.26 (5), p.1107-1110 |
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| language | eng |
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| source | MEDLINE; Access via ScienceDirect (Elsevier) |
| subjects | Artificial Intelligence Biotechnology - methods Humans Machine Learning Research Design Technology, Pharmaceutical - methods |
| title | Best practices for artificial intelligence in life sciences research |
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