{"id":37076570,"url":"https://github.com/lucinamay/biosynfoni","last_synced_at":"2026-01-14T09:00:11.728Z","repository":{"id":192017307,"uuid":"667287325","full_name":"lucinamay/biosynfoni","owner":"lucinamay","description":"a *biosynformatic* fingerprint to explore natural product distance and diversity","archived":false,"fork":false,"pushed_at":"2025-08-30T23:38:04.000Z","size":77080,"stargazers_count":20,"open_issues_count":0,"forks_count":3,"subscribers_count":3,"default_branch":"main","last_synced_at":"2026-01-03T15:23:56.943Z","etag":null,"topics":["bioinformatics","biosynformatic-fingerprint","biosynformatics","cheminformatics","metabolites","metabolomics","molecular-fingerprints","natural-products"],"latest_commit_sha":null,"homepage":"https://moltools.bioinformatics.nl/biosynfoni","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lucinamay.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2023-07-17T07:03:38.000Z","updated_at":"2025-11-25T07:55:08.000Z","dependencies_parsed_at":null,"dependency_job_id":"833cdf4d-438f-4a60-bf90-b9917c6bedb4","html_url":"https://github.com/lucinamay/biosynfoni","commit_stats":null,"previous_names":["lucinamay/thesis","lucinamay/biosynfoni"],"tags_count":2,"template":false,"template_full_name":null,"purl":"pkg:github/lucinamay/biosynfoni","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucinamay%2Fbiosynfoni","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucinamay%2Fbiosynfoni/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucinamay%2Fbiosynfoni/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucinamay%2Fbiosynfoni/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lucinamay","download_url":"https://codeload.github.com/lucinamay/biosynfoni/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lucinamay%2Fbiosynfoni/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28414732,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-14T08:38:59.149Z","status":"ssl_error","status_checked_at":"2026-01-14T08:38:43.588Z","response_time":107,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bioinformatics","biosynformatic-fingerprint","biosynformatics","cheminformatics","metabolites","metabolomics","molecular-fingerprints","natural-products"],"created_at":"2026-01-14T09:00:11.053Z","updated_at":"2026-01-14T09:00:11.723Z","avatar_url":"https://github.com/lucinamay.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cimg width=\"800\" alt=\"스크린샷 2023-10-19 오후 7 59 27\" src=\"https://github.com/lucinamay/biosynfoni/assets/119406697/c2b32601-8a00-4520-b027-101206becf81\"\u003e\\\n\u003cspan style=\"color:green\"\u003e 🌿 *a biosynformatic molecular fingerprint tailored to natural product chem- and bioinformatic research* 🌿\u003c/span\u003e\n\n\n\u003cp align=\"center\"\u003e\n    \u003ca href=\"https://github.com/lucinamay/biosynfoni/actions/workflows/test-biosynfoni.yml\"\u003e\n        \u003cimg alt=\"Tests\" src=\"https://github.com/lucinamay/biosynfoni/actions/workflows/test-biosynfoni.yml/badge.svg\" /\u003e\u003c/a\u003e\n    \u003ca href=\"https://pypi.org/project/biosynfoni\"\u003e\n        \u003cimg alt=\"PyPI\" src=\"https://img.shields.io/pypi/v/biosynfoni\" /\u003e\u003c/a\u003e\n    \u003ca href=\"https://pypi.org/project/biosynfoni\"\u003e\n        \u003cimg alt=\"PyPI - Python Version\" src=\"https://img.shields.io/pypi/pyversions/biosynfoni\" /\u003e\u003c/a\u003e\n    \u003ca href=\"https://github.com/lucinamay/biosynfoni/blob/main/LICENSE\"\u003e\n        \u003cimg alt=\"PyPI - License\" src=\"https://img.shields.io/pypi/l/cinemol\" /\u003e\u003c/a\u003e\n    \u003ca href='https://github.com/psf/black'\u003e\n        \u003cimg src='https://img.shields.io/badge/code%20style-black-000000.svg' alt='Code style: black' /\u003e\u003c/a\u003e\n    \u003ca href=\"https://doi.org/10.5281/zenodo.14822624\"\u003e\n        \u003cimg src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.14822624.svg\" alt=\"DOI\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://fairsoftwarechecklist.net/v0.2?f=20\u0026a=30112\u0026i=20122\u0026r=123\"\u003e\n        \u003cimg src=\"https://fairsoftwarechecklist.net/badge.svg\" alt=\"FAIR checklist badge\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n\\________________________________________________________________________________________\n\n\n  **bi·o·syn·for·ma·tic**\\\n  /ˌbaɪ  oʊ  sɪn  fərˈ mæt ɪk/\\\n  *adjective Computers, Biochemistry*\n\n  relating to biosynthetic information and biochemical logic.\\\n  as a concatenation of  *biosynthetic* and *bioinformatics*, it was coined\\\n  during the creation of `BioSynFoni`.\n\n\\_________________________________________________________________________________________\n\n\n### Getting started 🌿\n\nRead more about Biosynfoni in our preprint [here](https://doi.org/10.26434/chemrxiv-2025-cwq74).\n\n#### Predict biosynthetic class\n\nWe have trained a biosynthetic class predictor on `biosynfoni` fingerprints. \n\nYou can try out the predictor on your own molecules [here](https://moltools.bioinformatics.nl/biosynfoni)!\n\n#### Installation\n\nBiosynfoni requires Python 3.9 or later. RDKit is installed as a dependency when installing Biosynfoni.\n\nTo install the package, you can use pip:\n\n```bash\npip install biosynfoni\n```\n\nNow you can import the `biosynfoni` package in your Python code or use the command line tool.\n\n#### Usage in Python\n\nConvert a SMILES string to a fingerprint:\n\n```python\nfrom biosynfoni import Biosynfoni\nfrom rdkit import Chem\n\nsmi = \u003cSMILES\u003e\nmol = Chem.MolFromSmiles(smi)\nfp = Biosynfoni(mol).fingerprint  # returns biosynfoni's count fingerprint of the molecule\n```\n\n#### Usage in the command line\n\nCreate a fingerprint from a SMILES string:\n\n```bash \nbiosynfoni \u003cSMILES\u003e\n```\n\nCreate a fingerprint from an InChI string:\n\n```bash\nbiosynfoni \u003cInChI\u003e\n```\n\nWrite the fingerprints of all molecules in an SDF file to a CSV file:\n\n```bash\nbiosynfoni \u003cmolecule_supplier.sdf\u003e\n```\n\n### Publication\n\n#### Citation\n\nIf you use `biosynfoni` in your research, please cite our [publication](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-025-01081-6).\n\n#### Data availability\n\nWe created several biosynthetic class predictors for our manuscript, which can be downloaded from Zenodo [here](https://zenodo.org/records/14791239).\n\nWe have used data from the [COCONUT](https://coconut.naturalproducts.net) natural product database ([DOI](https://doi.org/10.1186/s13321-020-00478-9)) and [ZINC](https://zinc.docking.org) compound database ([DOI](https://pubs.acs.org/doi/10.1021/acs.jcim.0c00675)). The parsed data used for the analysis in our manuscript can be downloaded from Zenodo [here](https://zenodo.org/records/14791205). \n\nResults for the stratified classification analysis (see: `experiments/classification_stratified.py`) can be downloaded from Zenodo [here](https://doi.org/10.5281/zenodo.15150841).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flucinamay%2Fbiosynfoni","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flucinamay%2Fbiosynfoni","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flucinamay%2Fbiosynfoni/lists"}