Neuraldecipher - reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures.

ABSTRACT: Protecting molecular structures from disclosure against external parties is of great relevance for industrial and private associations, such as pharmaceutical companies. Within the framework of external collaborations, it is common to exchange datasets by encoding the molecular structures into descriptors. Molecular fingerprints such as the extended-connectivity fingerprints (ECFPs) are frequently used for such an exchange, because they typically perform well on quantitative structure-activity relationship tasks. ECFPs are often considered to be non-invertible due to the way they are computed. In this paper, we present a fast reverse-engineering method to deduce the molecular structure given revealed ECFPs. Our method includes the Neuraldecipher, a neural network model that predicts a comp

SUBMITTER: Le T 

PROVIDER: S-EPMC8162443 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature