tldr-tools 0.1.0

Use TLDR for dockopt, decoy generation, and job management.

What is it?

A toolkit to interface the https://tldr.docking.org/ webserver, a webserver home to a collection of docking optimization and benchmark molecular docking programs.

• BSD license - a business friendly license for open source
• Core data structures and algorithms in C++
• Python 3.x wrapper generated using Boost.Python
• Java and C# wrappers generated with SWIG
• JavaScript (generated with emscripten) and CFFI wrappers around important functionality
• 2D and 3D molecular operations
• Descriptor and Fingerprint generation for machine learning
• Molecular database cartridge for PostgreSQL supporting substructure and similarity searches as well as many descriptor calculators
• Cheminformatics nodes for KNIME
• Contrib folder with useful community-contributed software harnessing the power of the RDKit

Installation

Installation is super easy and pip installable. Pleaes make sure you have Python 3.6 installed.

$ pip install tldr-tools

Usage

To view the current implemented modules in tldr-tools, please run:

$ tldr-submit --list-modules

For example, if running decoy generation is desired:

tldr-submit --module decoys --activesism input_files/actives.ism --decoygenin input_files/decoy_generation.in --memo "Decoy generation for ADA, replicate 1"

Documenting runs with the optional memo parameter is encouraged.

Pass in a job number to check on a status of a run:

tldr-status --job-number 14886

Once a run is successful, you can download the output to a local directory:

tldr-download --job-number 14886 --output some_folder

Extending tldr-tools

Community and expansion is encouraged and made easy with this codebase. Adding new modules that are newly introduced on https://tldr.docking.org/ is painless, and is as simple as adding a new Endpoint and required/optional list of files.