title: Surrogate data--a secure way to share corporate data.
abstract: The privacy of chemical structure is of paramount importance for the industrial sector, in particular for the pharmaceutical industry. At the same time, companies handle large amounts of physico-chemical and biological data that could be shared in order to improve our molecular understanding of pharmacokinetic and toxicological properties, which could lead to improved predictivity and shorten the development time for drugs, in particular in the early phases of drug discovery. The current study provides some theoretical limits on the information required to produce reverse engineering of molecules from generated descriptors and demonstrates that the information content of molecules can be as low as less than one bit per atom. Thus theoretically just one descriptor can be used to completely disclose the molecular structure. Instead of sharing descriptors, we propose to share surrogate data. The sharing of surrogate data is nothing else but sharing of reliably predicted molecules. The use of surrogate data can provide the same information as the original set. We consider the practical application of this idea to predict lipophilicity of chemical compounds and we demonstrate that surrogate and real (original) data provides similar prediction ability. Thus, our proposed strategy makes it possible not only to share descriptors, but also complete collections of surrogate molecules without the danger of disclosing the underlying molecular structures.
authors.affiliation: Institute of Bioorganic and Petroleum Chemistry, Ukrainian Academy of Sciences, Kyiv, Ukraine. itetko@vcclab.org
authors.forename: Igor V
authors.initials: IV
authors.lastname: Tetko
authors.forename: Ruben
authors.initials: R
authors.lastname: Abagyan
authors.forename: Tudor I
authors.initials: TI
authors.lastname: Oprea
mesh_terms: D002616:Chemical Engineering; D016494:Computer Security; D016208:Databases, Factual; D015195:Drug Design; D004345:Drug Industry; D008956:Models, Chemical; D015394:Molecular Structure; D016571:Neural Networks, Computer