Chemical Data in Life Sciences R&D and the FAIR Principles


FAIR data principles adoption are critical for all research data.

Pleased to have collaborated with Gerd Blanke &  on discussing the importance of FAIR to Chemical data #fairdata #lifescience.

For more details see our article on Zenodo.

So far, chemical data such as chemical structures and chemical reactions and their associated data and metadata has not been a major topic of attention of the various FAIR initiatives. An attempt to introduce the FAIR principles to chemical data is the prototype of a FAIR Digital Object for the molecular structure, created by the Chemistry Implementation Network (ChIN)

Current State of Chemical Data in Industry 

For company-internal data in life sciences and chemistry research and development, elaborated data management practices have been adhered to since many years. This in particular applies to chemical data, where chemical structures and chemical reactions are well-defined objects. Chemical registration systems and chemistry ELN’s capture chemical compound and chemical reaction data as well as associated data in a structured manner, often supported by enforcement of detailed business rules and other data quality measures. Unique company ID’s and chemical structure searching enable researchers to find, access and reuse existing data. Therefore, though many of the systems were created without the formal concept of FAIR data in mind, often company-internal chemical data already meets the findability and accessibility criteria, at least as long as you use of the data within the well-defined local environment the data has been created in. On the other hand, in particular chemical data interoperability has always been a challenge. 

The deficiencies in chemical data FAIRness become more apparent once the data is to be used outside its sheltered local ‘silo’. Examples include combining chemical data from several systems, collaborations with CRO’s, selecting external compounds to complement screening collections, public-private partnerships such as IMI and Open PHACTS, and use of the data by autonomous devices and for machine learning / artificial intelligence

For more details see our article on Zenodo.

Images CC by SA

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