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Neuraldecipher – reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures
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 descr...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8162443/ https://www.ncbi.nlm.nih.gov/pubmed/34094299 http://dx.doi.org/10.1039/d0sc03115a |
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author | Le, Tuan Winter, Robin Noé, Frank Clevert, Djork-Arné |
author_facet | Le, Tuan Winter, Robin Noé, Frank Clevert, Djork-Arné |
author_sort | Le, Tuan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8162443 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | The Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
spelling | pubmed-81624432021-06-04 Neuraldecipher – reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures Le, Tuan Winter, Robin Noé, Frank Clevert, Djork-Arné Chem Sci Chemistry 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. The Royal Society of Chemistry 2020-09-11 /pmc/articles/PMC8162443/ /pubmed/34094299 http://dx.doi.org/10.1039/d0sc03115a Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by/3.0/ |
spellingShingle | Chemistry Le, Tuan Winter, Robin Noé, Frank Clevert, Djork-Arné Neuraldecipher – reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures |
title | Neuraldecipher – reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures |
title_full | Neuraldecipher – reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures |
title_fullStr | Neuraldecipher – reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures |
title_full_unstemmed | Neuraldecipher – reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures |
title_short | Neuraldecipher – reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures |
title_sort | neuraldecipher – reverse-engineering extended-connectivity fingerprints (ecfps) to their molecular structures |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8162443/ https://www.ncbi.nlm.nih.gov/pubmed/34094299 http://dx.doi.org/10.1039/d0sc03115a |
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