CryptoChem for Encoding and Storing Information Using Chemical Structures

This study presents CryptoChem, a new method and associated software to securely store and transfer information using chemicals. Relying on the concept of Big Chemical Data, molecular descriptors and machine learning techniques, CryptoChem offers a highly complex and robust system with multiple layers of security for transmitting confidential information. This revolutionary technology adds fully untapped layers of complexity and is thus of relevance for different types of applications and users. The algorithm directly uses chemical structures and their properties as the central element of the secured storage. QSDR (Quantitative Structure-Data Relationship) models are used as private keys to encode and decode the data. Herein, we validate the software with a series of five datasets consisting of numerical and textual information with increasing size and complexity. We discuss (i) the initial concept and current features of CryptoChem, (ii) the associated Molread and Molwrite programs which encode messages as series of molecules and decodes them with an ensemble of QSDR machine learning models, (iii) the Analogue Retriever and Label Swapper methods, which enforce additional layers of security, (iv) the results of encoding and decoding the five datasets using CryptoChem, and (v) the comparison of CryptoChem to contemporary encryption methods. CryptoChem is freely available for testing at https://github.com/XinhaoLi74/CryptoChem