Issue 38, 2020

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 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 up to 69% of molecular structures on a validation set (112 K unique samples) with our method.

Graphical abstract: Neuraldecipher – reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures

Supplementary files

Article information

Article type
Edge Article
Submitted
03 jun 2020
Accepted
10 sep 2020
First published
11 sep 2020
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2020,11, 10378-10389

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

T. Le, R. Winter, F. Noé and D. Clevert, Chem. Sci., 2020, 11, 10378 DOI: 10.1039/D0SC03115A

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements