Sharing Chemical Information without Sharing Chemical Structure

Abstract

Studies to assess the risks of revealing chemical structures by sharing various chemical descriptor data are presented. Descriptors examined include "Lipinski-like" properties, 2D-BCUT descriptors, and a high-dimensional "fingerprint-like" descriptor (MACCs-vector). We demonstrate that unless sufficient precautions are taken, de novo design software such as EA-Inventor is able to derive a unique chemical structure or a set of closely related analogs from some commonly used descriptors. Based on the results of our studies, a set of guidelines or recommendations for safely sharing chemical information without revealing chemical structure is presented. A procedure for assessing the risk of revealing chemical structure when exchanging chemical descriptor information was also developed. The procedure is generic and can be applied to any chemical descriptor or combination of descriptors and to any set of structures to enable a decision about whether the exchange of information can be done without revealing the chemical structures.

Using Open Source Computational Tools for Predicting Human Metabolic Stability and Additional Absorption, Distribution, Metabolism, Excretion, and Toxicity Properties

“…It remains to be seen how well the descriptors used mask the structure identity, and further studies will be required for assessment of this factor, such as that performed with other descriptors (Masek et al, 2008). We have not described the actual sharing of models or the format, for doing this will require testing to ensure compatibility and reproducibility among laboratories.…”

“…These data would represent precompetitive data, and we would need to ensure that molecular structures could not be distinguished or reverseengineered from the training sets and descriptors upon which the models were built (Masek et al, 2008). ADME/Tox programs have been traditionally limited in using the same very small datasets from the literature or combining datasets from different groups.…”

“…If the sensitive intellectual property contained in the training sets could be obfuscated, pharmaceutical organizations would often want to share these models with collaborators and academics working on important neglected diseases, for example. There have been some efforts in understanding how chemical information can be shared without directly sharing structures; for example, fingerprints should be avoided and low levels of precision in numeric descriptors and feature count descriptors may be "fuzzy" enough to protect the structure identity (Masek et al, 2008).…”

A Survey of Quantitative Descriptions of Molecular Structure

“…It has not been conclusively proven that such a “one-way” descriptor can be defined. For example Masek et al [63] have shown that one can regenerate a target structure (or substructural features) or a set of closely related analogs given a combination of descriptors such as BCUTs. Tetko et al [64] suggest an approach to prevent reverse engineering but avoid the use of descriptors and instead sharing structures of related molecules, rather than the molecules of interest.…”

On the interpretation and interpretability of quantitative structure–activity relationship models

“…For example, the Burden-CAS-University of Texas (BCUT) descriptors [78] are eigenvalues of the atomic property-weighted Burden matrix [11]. Linking the eigenvalues back to the molecular structure can be a difficult task, though Masek et al [70] have recently shown that one could derive connection tables from the values of a set of BCUT descriptors. Though this can be considered one form of interpretability, it is indirect and more challenging than descriptors with a clear physical connection to molecular structure.…”