Sharing
Chemical Relationships Does Not Reveal Structures
Matthew Matlock
S. Joshua Swamidass
10.1021/ci400399a.s001
https://acs.figshare.com/articles/journal_contribution/Sharing_Chemical_Relationships_Does_Not_Reveal_Structures/2329264
In
this study, we propose a new, secure method of sharing useful
chemical information from small-molecule libraries, without revealing
the structures of the libraries’ molecules. Our method shares
the relationship between molecules rather than structural descriptors.
This is an important advance because, over the past few years, several
groups have developed and published new methods of analyzing small-molecule
screening data. These methods include advanced hit-picking protocols,
promiscuous active filters, economic optimization algorithms, and
screening visualizations, which can identify patterns in the data
that might otherwise be overlooked. Application of these methods to
private data requires finding strategies for sharing useful chemical
data without revealing chemical structures. This problem has been
examined in the context of ADME prediction models, with results from
information theory suggesting it is impossible to share useful chemical
information without revealing structures. In contrast, we present
a new strategy for encoding the relationships between molecules instead
of their structures, based on anonymized scaffold networks and trees,
that safely shares enough chemical information to be useful in analyzing
chemical data, while also sufficiently blinding structures from discovery.
We present the details of this encoding, an analysis of the usefulness
of the information it conveys, and the security of the structures
it encodes. This approach makes it possible to share data across institutions,
and may securely enable collaborative analysis that can yield insight
into both specific projects and screening technology as a whole.
2014-01-27 00:00:00
chemical information
chemical data
anonymized scaffold networks
screening
method
molecule
ADME prediction models