Skip to main content

Organizing and processing tables of chemical structures.

Project description

⬢⬢⬢ schemist

GitHub Workflow Status (with branch) PyPI - Python Version PyPI Open in Spaces

Cleaning, collating, and augmenting chemical datasets.

Installation

The easy way

Install the pre-compiled version from PyPI:

pip install schemist

From source

Clone the repository, then cd into it. Then run:

pip install -e .

Command-line usage

schemist provides command-line utlities. The list of commands can be checked like so:

$ schemist --help
usage: schemist [-h] [--version] {clean,convert,featurize,collate,dedup,enumerate,react,split} ...

Tools for cleaning, collating, and augmenting chemical datasets.

options:
  -h, --help            show this help message and exit
  --version, -v         show program's version number and exit

Sub-commands:
  {clean,convert,featurize,collate,dedup,enumerate,react,split}
                        Use these commands to specify the tool you want to use.
    clean               Clean and normalize SMILES column of a table.
    convert             Convert between string representations of chemical structures.
    featurize           Convert between string representations of chemical structures.
    collate             Collect disparate tables or SDF files of libraries into a single table.
    dedup               Deduplicate chemical structures and retain references.
    enumerate           Enumerate bio-chemical structures within length and sequence constraints.
    react               React compounds in silico in indicated columns using a named reaction.
    split               Split table based on chosen algorithm, optionally taking account of chemical structure during splits.

Each command is designed to work on large data files in a streaming fashion, so that the entire file is not held in memory at once. One caveat is that the scaffold-based splits are very slow with tables of millions of rows.

All commands (except collate) take from the input table a named column with a SMILES, SELFIES, amino-acid sequence, HELM, or InChI representation of compounds.

The tools complete specific tasks which can be easily composed into analysis pipelines, because the TSV table output goes to stdout by default so they can be piped from one tool to another.

To get help for a specific command, do

schemist <command> --help

For the Python API, see below.

Python API

schemist can be imported into Python to help make custom analyses.

>>> import schemist as sch

Documentation

Full API documentation is at ReadTheDocs.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

schemist-0.0.1.tar.gz (25.4 kB view details)

Uploaded Source

Built Distribution

schemist-0.0.1-py3-none-any.whl (27.6 kB view details)

Uploaded Python 3

File details

Details for the file schemist-0.0.1.tar.gz.

File metadata

  • Download URL: schemist-0.0.1.tar.gz
  • Upload date:
  • Size: 25.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.20

File hashes

Hashes for schemist-0.0.1.tar.gz
Algorithm Hash digest
SHA256 d32e57c4fbdc03fb27369b9322dc364019278196c37b6344b5f935b3b4ab761e
MD5 e0b89df00f74ac4616a80fe1c8c926e6
BLAKE2b-256 b769e9dea9026400c60a8c8264c15971490acf86c08859257f5cdcaf6c84e708

See more details on using hashes here.

File details

Details for the file schemist-0.0.1-py3-none-any.whl.

File metadata

  • Download URL: schemist-0.0.1-py3-none-any.whl
  • Upload date:
  • Size: 27.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.20

File hashes

Hashes for schemist-0.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 c67421e037601967c7aee58fe3b7321cbce79af0cd32906b1afd21dfc9ff317c
MD5 47c064066e0a629686a08be1595419c8
BLAKE2b-256 90cb83178adf074adc82b512913ac0d335281887702bbe06fdf7c55679264227

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page