Sharing Chemical Relationships Does Not Reveal Structures

Abstract

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.

Can we accelerate medicinal chemistry by augmenting the chemist with Big Data and artificial intelligence?

“…In the development of matched molecular pair methodology, two critical papers suggest that the level 3, Ar-HAr-F can be too unspecific and give a "smear" of outcomes, whereas the inclusion of more chemical context (equivalent to level 5 R-p-phenyl-H  R-p-phenyl-F) can give a result with a lower variance so increasing confidence that this could be a useful change [36] [37]. The possibility of using context-encoded matched molecular pair analysis to share medicinal chemistry knowledge was proposed by Dossetter et al [38] in 2013, with a confirmation of the data security of such an approach provided by Swamidass [39] in 2014.…”

Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB)

“…Published efforts have also been reviewed on sharing relevant chemical information about screening data that leave structures blinded, which could open the door for increased collaboration. Swamidass and co-workers recently proposed different approaches to secure sharing of molecules [105], using scaffold networks for compounds demonstrated they do not convey information to reveal chemical structure [105]. A second approach from these researchers uses a method of measuring the overlap between two private datasets using an algorithm that constructs a private dataset's shareable summary (cryptoset [106]), then overlap of private datasets is achieved by comparing these.…”

Bigger data, collaborative tools and the future of predictive drug discovery

“…For example, a 2005 American Chemical Society meeting, co-chaired by Dr. Christopher Lipinski and Dr. Tudor Oprea included a session on securely sharing chemical information to support collaborative development of absorption, distribution, metabolism and excretion (ADME) predictors [79-89]. Swamidass and co-workers recently proposed several approaches to the problem of sharing molecules securely [90] that may overcome the previous failings. First, they propose a totally new, secure method of sharing useful chemical information from small-molecule screens, without revealing the structures of the molecules [90].…”

“…Swamidass and co-workers recently proposed several approaches to the problem of sharing molecules securely [90] that may overcome the previous failings. First, they propose a totally new, secure method of sharing useful chemical information from small-molecule screens, without revealing the structures of the molecules [90]. The method generates scaffold networks for compounds, enabling sharing of: molecule identifiers with assay data; how molecules in a screen are connected to one another in a screening network; how molecules are grouped together into scaffold groups; how these groups are connected into trees; how these groups are connected into networks; and how molecules are connected together into R-group networks.…”

“…The method generates scaffold networks for compounds, enabling sharing of: molecule identifiers with assay data; how molecules in a screen are connected to one another in a screening network; how molecules are grouped together into scaffold groups; how these groups are connected into trees; how these groups are connected into networks; and how molecules are connected together into R-group networks. Statistical analysis using the PubChem data also clearly demonstrated that scaffold networks do not convey enough information to reliably reveal chemical structure [90]. …”