runCADD.py

Computer Aided Drug Discovery (CADD) Pipeline

Install

All systems
curl cmd.cat/runCADD.py.sh
Ubuntu
apt-get install mgltools-cadd
image/svg+xml Kali Linux
apt-get install mgltools-cadd
Windows (WSL2)
sudo apt-get update sudo apt-get install mgltools-cadd
Raspbian
apt-get install mgltools-cadd
Dockerfile
dockerfile.run/runCADD.py

mgltools-cadd

Computer Aided Drug Discovery (CADD) Pipeline

This package is part of the mgltools set of Python libraries which provide an infrastructure for the analysis of protein structures and their docking of chemical compounds. The Computer Aided Drug Discovery (CADD) Pipeline is a workflow environment designed to support molecular dyanmics simulations and virtual screening experiments for in silico drug discovery, with a special focus on supporting the use of the Relaxed Complex Scheme. It includes web based access to applications such as NAMD, AutoDock, PDB2PQR, APBS, MGLToos and couples them in a flexible and scalable fashion through cloud computing. It is developed as a standalone application, using Vision (https://www.nbcr.net/pub/wiki/index.php?title=MGLTools#Vision) as the backend engine for visual programming and workflow execution. The scientific applications are made accessible through CADD using Opal Web services (https://www.nbcr.net/pub/wiki/index.php?title=Opal) for scalable and distributed computation. The workflow components of the CADD pipeline are currently released as Vision networks packaged for specific processes in a modular fashion. These modules may be coupled at ease for more complex processes. In the future, they may also be accessible from workflow repositories such as MyExperiment.org, and from AutoDockTools. The Opal services used in the CADD workflow may be accessed using programmatic access, the Opal Server Dashboard or other workflow clients such as Kepler, VisTrails or Taverna through Opal plugins available at Opal Sourceforge website (http://opal.nbcr.net). Features * Automatic launching of NAMD simulation on TeraGrid and NBCR resources, including experimental support for migration of simulation between resources. * Selection of representative snapshots/conformations from MD simulations using clustering tools such as QR factorization from VMD and Ptraj from Amber. * Support of Virtual Screening using AutoDock, AutoDock Vina * Support of Relaxed Complex Scheme based Virtual Screening and Rescoring * Visualization and analysis of Virtual Screening hits