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Python package collecting tools for self-consistent charge optimization workflow and calculations.

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Tools for automated charge optimization

Goal

SMAMP (synthetic mimics of antimicrobial peptides) can be used for antimicrobial coating in medical applications. To understand a class of SMAMP, we simulate them via Molecular Dynamics (MD). An MD models atom as point-charges, so these charges are an essential component of MD simulations. These charges are not available for the SMAMP molecules of interest, so we have to determine them ourselves.

Multiple methods to determine point charges exists, e.g., Bader Charge Analysis, HORTON-style cost-function fitting and more. Also, subvariants of these methods are of interest: diverse constraints, different algorithms and convergence parameters.

Overview

Content

  • smamp: Code snippets for the charge optimization workflow
  • setup.py: Configuration file to create a python package
  • README.md: The document you are reading right now
  • Makefile: Automatically build and upload the package and git via make
  • .update_version.py: Increment version number by 0.01 in setup.py
  • .test.py: check if package import works

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Python package collecting tools for self-consistent charge optimization workflow and calculations.

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