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https://github.com/adicksonlab/flexibletopology

ML-based molecular representation models using PyTorch

https://github.com/adicksonlab/flexibletopology

Science Score: 49.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: acs.org
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    Academic email domains
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    JOSS paper metadata
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    Scientific vocabulary similarity
    Low similarity (8.5%) to scientific vocabulary
Last synced: 9 months ago · JSON representation

Repository

ML-based molecular representation models using PyTorch

Basic Info
  • Host: GitHub
  • Owner: ADicksonLab
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 4.92 MB
Statistics
  • Stars: 11
  • Watchers: 3
  • Forks: 3
  • Open Issues: 2
  • Releases: 0
Created about 4 years ago · Last pushed about 1 year ago
Metadata Files
Readme License

README.org 

* Flexible Topology
This project aims to develop a tool to dynamically design potential
drug molecules. The ~Flexible Topology~ method uses [[https://pytorch.org][PyTorch]] to build a
ML model, which can be trainable or non-trainable. It will then
predict the structure and pose of a set of given ~ghost atoms~ to be a
potential ligand candidate for a protein. The output of the model is a
function whose gradient, with respect to positions, produces external
forces. These force will constally change the chemical type and
positions of ghost atoms and optimize them toward target drug-like
molecules.

We run molecular dynamics simulations using [[https://github.com/pandegroup/openmm][Openmm]] where the OpenMM
Plugin [[https://github.com/ADicksonLab/mlforce.git][MLForce]] is employed to apply the ML-based forces.
For more details read the [[https://pubs.acs.org/doi/10.1021/acs.jctc.3c00409][Flexible Topology: A Dynamic Model of a Continuous Chemical Space]]
paper in JCTC.

* Installation
To install this package do the folloeing commands
- git clone https://github.com/ADicksonLab/flexibletopology.git
- cd flexibletopology
- pip install -e .

Owner

  • Name: ADicksonLab
  • Login: ADicksonLab
  • Kind: organization

GitHub Events

Total
  • Issues event: 4
  • Watch event: 2
  • Issue comment event: 2
  • Push event: 6
  • Pull request event: 2
  • Create event: 1
Last Year
  • Issues event: 4
  • Watch event: 2
  • Issue comment event: 2
  • Push event: 6
  • Pull request event: 2
  • Create event: 1

Issues and Pull Requests

Last synced: 9 months ago

All Time
  • Total issues: 3
  • Total pull requests: 9
  • Average time to close issues: 10 months
  • Average time to close pull requests: 2 days
  • Total issue authors: 2
  • Total pull request authors: 3
  • Average comments per issue: 0.33
  • Average comments per pull request: 0.33
  • Merged pull requests: 8
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 3
  • Pull requests: 3
  • Average time to close issues: 10 months
  • Average time to close pull requests: about 12 hours
  • Issue authors: 2
  • Pull request authors: 2
  • Average comments per issue: 0.33
  • Average comments per pull request: 0.67
  • Merged pull requests: 3
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • alexrd (2)
  • amrhamedp (1)
Pull Request Authors
  • alexrd (6)
  • FatemehFathiNiazi (2)
  • ndonyapour (1)
Top Labels
Issue Labels
Pull Request Labels

Dependencies

requirements.in pypi
  • h5py *
  • numpy *
  • scikit-learn *
setup.py pypi
  • h5py *
  • numpy *
  • scikit-learn *
  • scipy *