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...
Autores principales: | , |
---|---|
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 |