Neuraldecipher - Reverse-Engineering ECFP Fingerprints to Their Molecular Structures

18 May 2020, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


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 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 reverse-engineering method to deduce the molecular structure given revealed ECFPs. Our method includes the Neuraldecipher, a neural network model that predicts a compact vector representation of compounds, given ECFPs. We then utilize another pre-trained model to retrieve the molecular structure as SMILES representation. We demonstrate that our method is able to reconstruct molecular structures to some extent, and improves, when ECFPs with larger fingerprint sizes are revealed. For example, given ECFP count vectors of length 4096, we are able to correctly deduce around 60% of molecular structures on a validation set (112K unique samples) with our method.


Molecular descriptor
Reverse engineering
Representation learning
Data privacy
De novo design
Machine learning


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