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onglai-classify-homologues

OngLai: A cheminformatics algorithm to classify homologous chemical series

https://github.com/adelenelai/onglai-classify-homologues

Science Score: 77.0%

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Keywords

chemical-series cheminformatics rdkit
Last synced: 6 months ago · JSON representation ·

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OngLai: A cheminformatics algorithm to classify homologous chemical series

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  • Stars: 7
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  • Forks: 5
  • Open Issues: 3
  • Releases: 2
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chemical-series cheminformatics rdkit
Created over 4 years ago · Last pushed over 1 year ago
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Readme License Citation

README.md

OngLai: An Algorithm to Classify Homologous Series

Powered by RDKit License Maintenance GitHub issues GitHub contributors DOI GitHub release PyPI version fury.io

Introduction

Homologous series are groups of chemical compounds sharing the same core structure(s) and different numbers of repeating units (RU) connected end-to-end.

This is an open-source algorithm to classify homologous series within compound datasets provided as SMILES, implemented using the RDKit.

For example, these series were classified in COCONUT and the NORMAN Suspect List Exchange, datasets containing natural products and environmental chemicals respectively.

CH2 Repeating Unit: coconut-hs

CF2 Repeating Unit: norman-hs

Requirements

The algorithm requires RDKit to be installed via conda-forge.

shell $ conda create -c conda-forge -n my-rdkit-env rdkit $ conda activate my-rdkit-env

Installation

shell $ git clone https://github.com/adelenelai/onglai-classify-homologues $ cd onglai-classify-homologues $ pip install -e . Note that pip installing the package is not enough; in addition, the repo must be cloned from GitHub because the algorithm runs as a script (see below).

Usage

Run:

shell $ python nextgen_classify_homols.py [-in <arg>] [-sep <arg>] [-s <arg>] [-n <arg>] [-ru <arg>] [-min <arg>] [-max <arg>] 2>log

| Flag | Description | | --- | ----------- | | -in --inputcsv | path to input CSV containing SMILES and Name columns| | -sep --separator | delimiter for input CSV. Default is comma i.e., ','| | -s --smiles | name of column containing SMILES. Default is SMILES| | -n --names | name of column containing Names. Default is Name| | -ru --repeatingunits | chemical RU as SMARTS, enclosed within speech marks. Default is CH2 i.e., '[#6&H2]'| | -min --minRUin | minimum length of RU chain, default is 3| | -max --maxRUin | maximum length of RU chain, default is 30 | | -f --frag_steps | no. times to fragment molecules to obtain cores, the default is 2 |

Try: shell $ cd onglai_classify_homologues $ python nextgen_classify_homols.py -in ../tests/test1_23.csv -s SMILES -n Name -ru '[#6&H2]' -min 3 -max 30 -f 2 2>log

Successful classification will generate an output directory containing the following files:

  1. A TXT file containing the summary of classification results and explanation of outputs (series_no codes)
  2. A CSV file containing 8 columns: series_no, cpd_name, CanoSmiles_FinalCores, SMILES, InChI, InChIKey, molecular_formula and monoisotopic_mass. The first column series_no contains the results of the homologous series classification. CanoSmiles_FinalCores indicates the common core shared by all members within a given series. The remaining columns contain information calculated based on the SMILES.
  3. A TXT file of unparseable SMILES that were removed (if all SMILES were parsed OK, then empty)

Reproducing Classification described in Lai et al.

Classification using default settings as described above. Code below runs for sample datasets provided in input/, full datasets have been archived on Zenodo (amend -in accordingly to classify full datasets).

```

activate your rdkit environment

NORMAN-SLE

$ python nextgenclassifyhomols.py -in ../../input/pubchemnormansletreeparentcid981162022-03-21from115115trial.csv -s isosmiles -n cmpdname 2>log

PubChemLite

$ python nextgenclassifyhomols.py -in ../../input/PubChemLiteexposomics20220225_trial.csv -n CompoundName 2>log

COCONUT

$ python nextgenclassifyhomols.py -in ../../input/COCONUTDB2021-11_trial.txt 2>log ```

References and Links

  • Lai, A., Schaub, J., Steinbeck, C. et al. An algorithm to classify homologous series within compound datasets. J Cheminform 14, 85 (2022). https://doi.org/10.1186/s13321-022-00663-y
  • Poster presented at the 17th German Cheminformatics Conference, Garmisch-Partenkirchen, Germany (May 8-10, 2022)

Acknowledgements

Steffen Neumann, Charles Tapley-Hoyt, Kohulan Rajan, Mahnoor Zulfiqar, Anjana Elapavalore, Zhanyun Wang, Christos Nicolaou, Maximilian Beckers, Greg Landrum, Paolo Tosco. (and Kohulan for the logo :))

License

This project is licensed under Apache 2.0 - see LICENSE for details.

Our Research Groups

Environmental Cheminformatics Group at the

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Owner

  • Name: Adelene Lai
  • Login: adelenelai
  • Kind: user
  • Location: Luxembourg

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Lai
    given-names: Adelene
    orcid: https://orcid.org/0000-0002-2985-6473
title: "An Algorithm to Classify Homologous Series"
version: 1.0.0
doi: 10.5281/zenodo.6806919
date-released: 2022-07-07

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Name Email Commits
Adelene a****i@u****u 36
Adelene LAI a****i@m****x 32
adelenelai a****i@g****m 13
Adelene LAI a****i@m****x 11
Charles Tapley Hoyt c****t@g****m 8
alai a****i@a****x 5
alai a****i@a****x 2
alai a****i@a****l 2
Kohulan k****n@u****e 2
Steffen Neumann s****n@i****e 1
Egon Willighagen e****n@g****m 1
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    • pypi 9 last-month
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pypi.org: onglai-classify-homologues

A cheminformatics algorithm to classify homologous series.

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 9 Last month
Rankings
Dependent packages count: 6.6%
Forks count: 15.7%
Stargazers count: 21.8%
Average: 27.6%
Dependent repos count: 30.6%
Downloads: 63.5%
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adelenelai
Last synced: 6 months ago

Dependencies

setup.py pypi
  • datamol *
  • matplotlib *
  • numpy *
  • pandas *
  • pytest *
  • rdkit *