Ten simple rules to power drug discovery with data science

The combination of data, computing power, and advanced analytics is positioning data science as a critical core discipline in pharmaceutical research, alongside the more traditional disciplines of biology, chemistry, and medicine. [...]machine learning engineers and specialized data scientists with...

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Bibliographic Details
Published in:PLoS computational biology 2020-08, Vol.16 (8), p.e1008126-e1008126
Main Authors: Ferrero, Enrico, Brachat, Sophie, Jenkins, Jeremy L, Marc, Philippe, Skewes-Cox, Peter, Altshuler, Robert C, Gubser Keller, Caroline, Kauffmann, Audrey, Sassaman, Erik K, Laramie, Jason M, Schoeberl, Birgit, Borowsky, Mark L, Stiefl, Nikolaus
Format: Article
Language:English
Subjects:
Artificial intelligence
Biomedical research
Computer and Information Sciences
Computer applications
Councils
Data analysis
Data management
Data science
Decision making
Design
Drug discovery
Experiments
Image processing
Information science
Leadership
Learning algorithms
Machine learning
Medical research
Medicine and Health Sciences
Metadata
Methods
People and Places
Pharmaceutical industry
Pharmaceuticals
R&D
Research & development
Science
Science Policy
Scientists
Skills
Technology application
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Summary:The combination of data, computing power, and advanced analytics is positioning data science as a critical core discipline in pharmaceutical research, alongside the more traditional disciplines of biology, chemistry, and medicine. [...]machine learning engineers and specialized data scientists with specific skillsets (e.g., deep learning, image processing, or body sensors analysis) have joined the ranks of growing data science teams in pharmaceutical companies. Inclusion of data science leaders in decision-making bodies connects data scientists to critical business questions, raises organizational awareness of computational approaches and data management, and further connects disease-focused departments with discovery and clinical platforms. Over time, important information is lost due to organizational changes and employee turnover, either because the data are not well documented or because they are stored in nonstandard or nonmachine-readable formats. [...]it is crucial to have in place FAIR play processes from the point of data generation, including clear data and metadata management strategies.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1008126