GetRandomPCMol

Python program for obtaining random PubChem molecules and generating conformer ensembles for them (optionally).

Dependencies

getrandompcmol in its current state depends on existing installations of

• the xtb program package for optimization and MDs/MTDs (for details and installation instructions, see github.com/grimme-lab/xtb)
• the mctc-convert executable that can be obtained from the mctc-lib library (for details and installation instructions, see github.com/grimme-lab/mctc-lib)
• the PubGrep script for downloading PubChem molecules based on their CID (for details, see github.com/grimme-lab/PubGrep)
• if conformer search is desired: crest for creating conformer ensembles from a given structure file (for details, see github.com/crest-lab/crest](https://github.com/crest-lab/crest))

Installation

After cloning the code via git clone [email protected]:grimme-lab/GetRandomPCMol.git, a new virtual conda environment with required Python pre-requisites can be set up with:

conda env create -f environment.yml
conda activate getrandompcmol

getrandompcmol can be installed into this environment with

pip install -e .

The flag -e allows modification of the code without the need to reinstall.

Use

After installation, the package can generate a data set of random molecules. The randomization is based on a random number generator in NumPy.

getrandompcmol -n 500 --opt --crest protonate --maxnumat 35 --maxcid 1000000 --seed 27051997 --evalconf 5 10

The distinct keywords are described as follows:

• -n/--n <int>: controls the number of molecules generated.
• --maxnumat <int>: Maximally allowed number of atoms per molecule.
• --maxcid <int>: Range between 1 and <maxcid>, in which random CIDs are generated.
• • --opt: Optimize the molecules using GFN2-xTB in xtb.
• --crest {normal,protonate}: Generate conformer ensembles from the given structures via crest. If protonated is used instead of normal, every structure is protonated by crest before the actual conformer search.
• --seed <int>: Starting seed for random number generation.
• --evalconf <int> <int>: Range of conformer ensemble size allowed for post-processing.

If only the evaluation and post-processing of a previously generated conformer ensemble is desired, use --evalconfonly. Further information on possible input flags is available via --help.

CIDs and names of generated molecules are saved into a file compounds.txt. Additionally, (if --crest) is active, each directory contains a file conformer.json, in which relevant properties of the generated ensemble are saved.

The crest conformer ensemble generation is executed in parallel. The parallelization adapts to the number of available cores on your machine and the requested number of molecules.

Source code

The source code is in the src/getrandompcmol directory. Here, also some dunder files can be found:

• __version__.py: just the version number as a string, used by config files
• __init__.py: entry point for program/library

Setup files and Packaging

Packaging is done with setuptools, which is configured through the pyproject.toml and/or setup.cfg/setup.py files.

pyproject.toml vs. setup.cfg vs setup.py

The setup.py file is a Python script, and configuration is passed through keyword arguments of setuptools.setup(). This is not recommended due to possible security and parsing issues. The same setup can be accomplished in a declarative style within setup.cfg, and setup.py remains mostly empty only calling setuptools.setup(). The pyproject.toml file aims to unify configuration files including various tools like black or pytest. For packaging, it is very similar to setup.cfg. However, pyproject.toml has not been adopted as the default yet, and many projects still use setup.cfg to declare the packaging setup. Note that setup.py is not necessary if a pyproject.toml is present.

pyproject.toml

• minimal build specification to use with setuptools
• configuration of other tools (black, pytest, mypy, ...)

setup.cfg

• declarative configuration for setuptools
• metadata: must at least contain name and version
• options: package discovery, dependencies
  • additional setup required for src/ layout
• options.extras_require: optional dependencies (dev tools, docs, ...)
• options.package_data: inclusion of other, non-Python files (marker files, data, ...)
  • alternative: MANIFEST.in
• options.entry_points: entry point for command line interface
• can also hold configuration of other tools

The package can be installed with pip install . or something like pip install . [dev] to also install additional dependencies specified in setup.cfg's options.extras_require. Pass the -e flag for editable mode, which loads the package from the source directory, i.e., changing the source code does not require a new installation.