Submit HARMONSMILE for pyOpenSci review

[NanoBiostructuresRG]

Submitting Author: Flavio F. Contreras-Torres (@NanoBiostructuresRG)
All current maintainers: Flavio F. Contreras-Torres (@NanoBiostructuresRG)
Package Name: harmonsmile
One-Line Description of Package: HARMONSMILE harmonizes molecular SMILES strings into a reproducible canonical, isomeric, and Kekulized RDKit-based representation for cheminformatics and molecular machine-learning dataset preparation.
Repository Link: https://github.com/NanoBiostructuresRG/harmonsmile
Version submitted: v0.2.4
EiC: TBD
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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Description

HARMONSMILE is a Python package for harmonizing molecular SMILES strings from heterogeneous chemical data sources into a consistent RDKit-based representation. It supports reproducible cheminformatics workflows by reducing representation inconsistencies that can affect molecule comparison, dataset integration, preprocessing for duplicate detection, and machine-learning dataset preparation.

The package provides both a Python API and a command-line interface. Its current functionality includes local SMILES preparation workflows as well as ingestion-oriented workflows for public chemical data sources such as PubChem and ChEMBL. HARMONSMILE is designed as a lightweight workflow layer around RDKit rather than as a replacement for RDKit itself.

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Scope

• Please indicate which category or categories.
  Check out our package scope page to learn more about our
  scope. (If you are unsure of which category you fit, we suggest you make a pre-submission inquiry):

  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization¹
  • Workflow automation
  • Citation management and bibliometrics
  • Scientific software wrappers
  • Database interoperability

Domain Specific

• Geospatial
• Education

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• For all submissions, explain how and why the package falls under the categories you indicated above. In your explanation, please address the following points (briefly, 1-2 sentences for each):

HARMONSMILE fits data retrieval and extraction because it provides ingestion-oriented workflows for public molecular databases such as PubChem and ChEMBL. It fits data processing/munging because its central purpose is to transform heterogeneous SMILES strings into a consistent RDKit-based molecular representation. It supports workflow automation and reproducibility through a tested Python API, CLI, documentation, examples, and packaged releases. It also supports database interoperability by helping users compare or merge molecular records from different chemical sources under a shared representation convention.

• Who is the target audience and what are scientific applications of this package?

• The target audience includes computational chemists, cheminformatics researchers, molecular machine-learning practitioners, and maintainers of molecular datasets who need reproducible preprocessing of SMILES strings before downstream analysis. Scientific applications include molecular dataset curation, reproducible SMILES standardization, cross-database molecular comparison, preprocessing for duplicate detection, and preprocessing for cheminformatics or machine-learning pipelines.

• Are there other Python packages that accomplish the same thing? If so, how does yours differ?

• RDKit already provides core cheminformatics functionality, including SMILES parsing, molecular standardization utilities, and canonical SMILES generation. HARMONSMILE does not replace RDKit; instead, it provides reusable tabular workflows, PubChem and ChEMBL ingestion-oriented utilities, a Python API, CLI, examples, tests, documentation, PyPI distribution, versioned releases, and Zenodo archival for a specific recurring dataset-preparation problem.

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• No pre-submission enquiry was made.

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• includes documentation with examples for all functions.
• contains a tutorial with examples of its essential functions and uses.
• has a test suite.
• has continuous integration setup, such as GitHub Actions CircleCI, and/or others.

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Footnotes

1. Please fill out a pre-submission inquiry before submitting a data visualization package. ↩