Data

Tools

Indexes

Applications

Experiments

Open Source Science

pharmd

MD pharmacophores and virtual screening

https://github.com/ci-lab-cz/pharmd

Science Score: 13.0%

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

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.6%) to scientific vocabulary

Keywords

chemoinformatics pharmacophore-models
Last synced: 6 months ago · JSON representation

Repository

MD pharmacophores and virtual screening

Basic Info
  • Host: GitHub
  • Owner: ci-lab-cz
  • License: bsd-3-clause
  • Language: Python
  • Default Branch: master
  • Size: 45.9 KB
Statistics
  • Stars: 33
  • Watchers: 4
  • Forks: 17
  • Open Issues: 4
  • Releases: 0
Topics
chemoinformatics pharmacophore-models
Created over 6 years ago · Last pushed about 2 years ago
Metadata Files
Readme License

README.md

PharMD - extraction of pharmacophores from MD trajectories and virtual screening

PharMD is a tool to retrieve pharmacophore models from MD trajectories of protein-ligand complexes, identification of redundant pharmacophores and virtual screening with multiple pharmacophore models using different scoring schemes.

Dependency

mdtraj >= 1.9.3
pmapper >= 0.3.1
psearch >= 0.0.2
rdkit - should be installed by a user

Installation

text pip install pharmd

Usage

Retrieve pharmacophores from an MD trajectory

To retrieve individual snapshots of MD trajectory mdtraj package is used. Therefore the md2pharm utility takes the same arguments as mdconvert utility from mdtraj. Thus you may extract only specified frames not all of them. You have to specify ligand code as it is given in PDB topology file. Individual frames will be stored in a single PDB file without solvent molecules. Pharmacophore models for each frame in xyz-format will be stored in the same directory as output pdb-file.

bash md2pharm -i md.xtc -t md.pdb -s 10 -g LIG -o pharmacophores/frames.pdb

Retrieve non-redundant pharmacophores

Similar pharmacophores are recognized by identical 3D pharmacophore hashes. It is expected that pharmacophores with identical hashes would have RMSD less than the specified binning step. By default binning step equals to 1A. Pharmacophores with distinct hashes are stored in a specified directory. Optionally one may provide a path where to store hashes for al pharmacophores.

bash get_distinct -i pharmacophores/ -o distinct_pharmacophores/

Perform virtual screening using multiple non-redundant pharmacophores

screen_db utility from psearch package is used for this purpose. Therefore you have to generate database of compound conformers and their pharmacophore representations using utilities from psearch package. At this step you may specify a desired binning step value which will be used further in screening (default is 1).

bash prepare_db -i input.smi -o compounds.db -c 2 -v

If you would like to calculate scoring based on Conformer Coverage Approach you have to specify --conf argument for screen_db. Then all conformers of a compound matching pharmacophore models will be retrieved as hits (may be slower). Otherwise only the first matching conformer will be returned.

It is recommended to restrict screening to complex pharmacophores having at least four features, because less complex models would retrieve many irrelevant compounds.

bash screen_db -i compounds.db -q distinct_pharmacophores/ -o screen/ --conf -c 2 -f 4

Multiple txt-files will be created in the output directory containing hit lists retrieved by individual pharmacophore models.

Calculate compound scores based on multiple hit lists

The advantage of ensemble scoring is that you do not need validate individual models and select best performing ones. Ensemble scoring is calculated by:
1. Conformer Coverage Approach (CCA) - the score is equal to the percentage of conformers matching at least one of supplied pharmacophore models. 2. Common HIts Approach (CHA) - the score is equal to the percentage of models matched at least one conformer of a compound.

In the case of CCA scoring you have to supply the database of screened compounds as an additional parameter. bash get_scores -i screen/ -o cca_scores.txt -s cca -d compounds.db

Documentation

All utilities have -h option to get help pages with descriptions of all available arguments.

Citation

Virtual Screening Using Pharmacophore Models Retrieved from Molecular Dynamic Simulations
Pavel Polishchuk, Alina Kutlushina, Dayana Bashirova, Olena Mokshyna, Timur Madzhidov
Int. J. Mol. Sci. 2019, 20(23), 5834
https://doi.org/10.3390/ijms20235834

Issues

License

BSD-3 clause

Owner

  • Name: Chemical intelligence lab
  • Login: ci-lab-cz
  • Kind: organization
  • Location: Czech Republic

Chemoinformatics and drug design group at IMTM UPOL

GitHub Events

Total
  • Watch event: 2
Last Year
  • Watch event: 2

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 10
  • Total Committers: 1
  • Avg Commits per committer: 10.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Pavel Polishchuk p****k@u****t 10
Committer Domains (Top 20 + Academic)
ukr.net: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 5
  • Total pull requests: 4
  • Average time to close issues: 1 day
  • Average time to close pull requests: 7 months
  • Total issue authors: 3
  • Total pull request authors: 3
  • Average comments per issue: 4.4
  • Average comments per pull request: 1.0
  • Merged pull requests: 3
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 0
  • Average time to close issues: 1 day
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 0
  • Average comments per issue: 0.5
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • sherifelsabbagh (3)
  • lotus0905 (1)
  • SuparnaGhosh01 (1)
Pull Request Authors
  • meddwl (2)
  • DrrDom (1)
  • stsouko (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 14 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 2
  • Total maintainers: 1
pypi.org: pharmd

PharMD: MD pharmacophores and virtual screening

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 14 Last month
Rankings
Forks count: 9.3%
Dependent packages count: 10.0%
Stargazers count: 12.6%
Average: 21.4%
Dependent repos count: 21.7%
Downloads: 53.4%
Maintainers (1)
DrDom
Last synced: 6 months ago

Dependencies

setup.py pypi
  • mdtraj >=1.9.3
  • plip >=1.4.2
  • pmapper >=0.3
  • psearch >=0.0.2