Cargando…

A probabilistic molecular fingerprint for big data settings

BACKGROUND: Among the various molecular fingerprints available to describe small organic molecules, extended connectivity fingerprint, up to four bonds (ECFP4) performs best in benchmarking drug analog recovery studies as it encodes substructures with a high level of detail. Unfortunately, ECFP4 req...

Descripción completa

Detalles Bibliográficos
Autores principales: Probst, Daniel, Reymond, Jean-Louis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6755601/
https://www.ncbi.nlm.nih.gov/pubmed/30564943
http://dx.doi.org/10.1186/s13321-018-0321-8
_version_ 1783453267891585024
author Probst, Daniel
Reymond, Jean-Louis
author_facet Probst, Daniel
Reymond, Jean-Louis
author_sort Probst, Daniel
collection PubMed
description BACKGROUND: Among the various molecular fingerprints available to describe small organic molecules, extended connectivity fingerprint, up to four bonds (ECFP4) performs best in benchmarking drug analog recovery studies as it encodes substructures with a high level of detail. Unfortunately, ECFP4 requires high dimensional representations (≥ 1024D) to perform well, resulting in ECFP4 nearest neighbor searches in very large databases such as GDB, PubChem or ZINC to perform very slowly due to the curse of dimensionality. RESULTS: Herein we report a new fingerprint, called MinHash fingerprint, up to six bonds (MHFP6), which encodes detailed substructures using the extended connectivity principle of ECFP in a fundamentally different manner, increasing the performance of exact nearest neighbor searches in benchmarking studies and enabling the application of locality sensitive hashing (LSH) approximate nearest neighbor search algorithms. To describe a molecule, MHFP6 extracts the SMILES of all circular substructures around each atom up to a diameter of six bonds and applies the MinHash method to the resulting set. MHFP6 outperforms ECFP4 in benchmarking analog recovery studies. By leveraging locality sensitive hashing, LSH approximate nearest neighbor search methods perform as well on unfolded MHFP6 as comparable methods do on folded ECFP4 fingerprints in terms of speed and relative recovery rate, while operating in very sparse and high-dimensional binary chemical space. CONCLUSION: MHFP6 is a new molecular fingerprint, encoding circular substructures, which outperforms ECFP4 for analog searches while allowing the direct application of locality sensitive hashing algorithms. It should be well suited for the analysis of large databases. The source code for MHFP6 is available on GitHub (https://github.com/reymond-group/mhfp). [Image: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13321-018-0321-8) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-6755601
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Springer International Publishing
record_format MEDLINE/PubMed
spelling pubmed-67556012019-09-26 A probabilistic molecular fingerprint for big data settings Probst, Daniel Reymond, Jean-Louis J Cheminform Research Article BACKGROUND: Among the various molecular fingerprints available to describe small organic molecules, extended connectivity fingerprint, up to four bonds (ECFP4) performs best in benchmarking drug analog recovery studies as it encodes substructures with a high level of detail. Unfortunately, ECFP4 requires high dimensional representations (≥ 1024D) to perform well, resulting in ECFP4 nearest neighbor searches in very large databases such as GDB, PubChem or ZINC to perform very slowly due to the curse of dimensionality. RESULTS: Herein we report a new fingerprint, called MinHash fingerprint, up to six bonds (MHFP6), which encodes detailed substructures using the extended connectivity principle of ECFP in a fundamentally different manner, increasing the performance of exact nearest neighbor searches in benchmarking studies and enabling the application of locality sensitive hashing (LSH) approximate nearest neighbor search algorithms. To describe a molecule, MHFP6 extracts the SMILES of all circular substructures around each atom up to a diameter of six bonds and applies the MinHash method to the resulting set. MHFP6 outperforms ECFP4 in benchmarking analog recovery studies. By leveraging locality sensitive hashing, LSH approximate nearest neighbor search methods perform as well on unfolded MHFP6 as comparable methods do on folded ECFP4 fingerprints in terms of speed and relative recovery rate, while operating in very sparse and high-dimensional binary chemical space. CONCLUSION: MHFP6 is a new molecular fingerprint, encoding circular substructures, which outperforms ECFP4 for analog searches while allowing the direct application of locality sensitive hashing algorithms. It should be well suited for the analysis of large databases. The source code for MHFP6 is available on GitHub (https://github.com/reymond-group/mhfp). [Image: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13321-018-0321-8) contains supplementary material, which is available to authorized users. Springer International Publishing 2018-12-18 /pmc/articles/PMC6755601/ /pubmed/30564943 http://dx.doi.org/10.1186/s13321-018-0321-8 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Probst, Daniel
Reymond, Jean-Louis
A probabilistic molecular fingerprint for big data settings
title A probabilistic molecular fingerprint for big data settings
title_full A probabilistic molecular fingerprint for big data settings
title_fullStr A probabilistic molecular fingerprint for big data settings
title_full_unstemmed A probabilistic molecular fingerprint for big data settings
title_short A probabilistic molecular fingerprint for big data settings
title_sort probabilistic molecular fingerprint for big data settings
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6755601/
https://www.ncbi.nlm.nih.gov/pubmed/30564943
http://dx.doi.org/10.1186/s13321-018-0321-8
work_keys_str_mv AT probstdaniel aprobabilisticmolecularfingerprintforbigdatasettings
AT reymondjeanlouis aprobabilisticmolecularfingerprintforbigdatasettings
AT probstdaniel probabilisticmolecularfingerprintforbigdatasettings
AT reymondjeanlouis probabilisticmolecularfingerprintforbigdatasettings