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molbloom

Molecular bloom filter tool

https://github.com/whitead/molbloom

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    1 of 5 committers (20.0%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (13.0%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Molecular bloom filter tool

Basic Info
  • Host: GitHub
  • Owner: whitead
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 112 MB
Statistics
  • Stars: 121
  • Watchers: 2
  • Forks: 14
  • Open Issues: 2
  • Releases: 19
Created over 3 years ago · Last pushed 7 months ago
Metadata Files
Readme License Citation

README.md

molbloom

Can I buy this molecule? Returns results in about 500 ns and consumes about 100MB of RAM (or 2 GB if using all ZINC20).

sh pip install molbloom

```py from molbloom import buy buy('CCCO')

True

buy('ONN1CCCC1')

False

```

If buy returns True - it may be purchasable with a measured error rate of 0.0003. If it returns False - it is not purchasable. The catalog information is from ZINC20. Add canonicalize=True if your SMILES are not canonicalized (requires installing rdkit).

There are other available catalogs - see options with molbloom.catalogs(). Most catalogs require an initial download. buy('CCCO', catalog='zinc-instock-mini) doesn't require a download and is included in the package. Useful for testing, but has a high false positive rate of 1%.

Simple Reagents

By default, it will first check against common organic reagents like water, ether, etc. You can disable this check by adding check_common=False

Querying Small World

Just because buy returns True doesn't mean you can buy it -- you should follow-up with a real query at ZINC or you can use the search feature in SmallWorld to find similar purchasable molecules.

```py from smallworld_api import SmallWorld sw = SmallWorld()

aspirin = 'O=C(C)Oc1ccccc1C(=O)O' results = sw.search(aspirin, dist=5, db=sw.REAL_dataset) ``` this will query ZINC Small World.

Custom Filter

Do you have your own list of SMILES? There are two ways to build a filter -- you can use a C tool that is very fast (1M / s) if your SMILES are in a file and already canonical. Or you can use the Python API to programmaticaly build a filter and canonicalize as you go. See below

Once your custom filter is built:

```py from molbloom import BloomFilter bf = BloomFilter('myfilter.bloom')

usage:

'CCCO' in bf ```

Build with C Tool

You can build your own filter using the code in the tool/ directory.

sh cd tool make ./molbloom-bloom <MB of final filter> <filter name> <approx number of compounds> <input file 1> <input file 2> ...

where each input file has SMILES on each line in the first column and is already canonicalized. The higher the MB, the lower the rate of false positives. If you want to choose the false positive rate rather than the size, you can use the equation:

$$ M = - \frac{N \ln \epsilon}{(\ln 2)^2} $$

where $M$ is the size in bits, $N$ is the number of compounds, and $\epsilon$ is the false positive rate.

Build with Python

You can also build a filter using python as follows:

```py from molbloom import CustomFilter, canon bf = CustomFilter(size=100, n=1000, name='myfilter') bf.add('CCCO')

canonicalize one record

s = canon("CCCOC") bf.add(s)

finalize filter into a file

bf.save('test.bloom') ```

Citation

bibtex @article{medina2023bloom, title={Bloom filters for molecules}, author={Medina, Jorge and White, Andrew D}, journal={Journal of Cheminformatics}, volume={15}, number={1}, pages={95}, year={2023}, publisher={Springer} }

Owner

  • Name: Andrew White
  • Login: whitead
  • Kind: user
  • Location: Rochester, NY
  • Company: University of Rochester

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "White"
  given-names: "Andrew D"
  orcid: "https://orcid.org/0000-0002-6647-3965"
title: "molbloom: quick assessment of compound purchasability with bloom filters"
version: 2.0.0
doi: 10.5281/zenodo.7426402
date-released: 2022-12-01
url: "https://github.com/whitead/molbloom"

GitHub Events

Total
  • Create event: 18
  • Issues event: 2
  • Release event: 6
  • Watch event: 8
  • Delete event: 10
  • Issue comment event: 4
  • Member event: 1
  • Push event: 26
  • Pull request review comment event: 7
  • Pull request review event: 26
  • Pull request event: 22
  • Fork event: 2
Last Year
  • Create event: 18
  • Issues event: 2
  • Release event: 6
  • Watch event: 8
  • Delete event: 10
  • Issue comment event: 4
  • Member event: 1
  • Push event: 26
  • Pull request review comment event: 7
  • Pull request review event: 26
  • Pull request event: 22
  • Fork event: 2

Committers

Last synced: 10 months ago

All Time
  • Total Commits: 91
  • Total Committers: 5
  • Avg Commits per committer: 18.2
  • Development Distribution Score (DDS): 0.165
Past Year
  • Commits: 9
  • Committers: 1
  • Avg Commits per committer: 9.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Andrew White w****w@g****m 76
Matthew O'Meara m****m@u****u 9
Jorge 9****5 3
Hugo MacDermott-Opeskin h****t@g****m 2
Janosh Riebesell j****l@g****m 1
Committer Domains (Top 20 + Academic)
umich.edu: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 12
  • Total pull requests: 18
  • Average time to close issues: 3 months
  • Average time to close pull requests: 10 days
  • Total issue authors: 9
  • Total pull request authors: 5
  • Average comments per issue: 1.83
  • Average comments per pull request: 0.72
  • Merged pull requests: 18
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 7
  • Average time to close issues: N/A
  • Average time to close pull requests: about 1 hour
  • Issue authors: 1
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 7
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • hmacdope (2)
  • jamesbraza (2)
  • whitead (2)
  • ichxw (1)
  • SamCox822 (1)
  • UnixJunkie (1)
  • ValeryPolyakov (1)
  • ijpulidos (1)
  • WardLT (1)
Pull Request Authors
  • whitead (23)
  • Jgmedina95 (4)
  • hmacdope (3)
  • maomlab (1)
  • janosh (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 4,873 last-month
  • Total dependent packages: 2
  • Total dependent repositories: 1
  • Total versions: 25
  • Total maintainers: 1
pypi.org: molbloom

Purchaseable SMILES filter

  • Versions: 25
  • Dependent Packages: 2
  • Dependent Repositories: 1
  • Downloads: 4,873 Last month
Rankings
Dependent packages count: 4.8%
Stargazers count: 7.4%
Downloads: 7.5%
Average: 10.8%
Forks count: 12.5%
Dependent repos count: 21.5%
Maintainers (1)
whitead
Last synced: 7 months ago

Dependencies

requirements.txt pypi
  • cython *
  • pre-commit *
  • pytest *
  • rdkit *
setup.py pypi
  • importlib_resources *
.github/workflows/build.yml actions
  • actions/checkout v2 composite
  • actions/checkout v3 composite
  • actions/download-artifact v3 composite
  • actions/upload-artifact v3 composite
  • pypa/cibuildwheel v2.11.2 composite
  • pypa/gh-action-pypi-publish master composite
.github/workflows/tests.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
pyproject.toml pypi