VSDMIP

VSDMIP integrates ligand-based and structure-based virtual screening workflows within PyMOL to manage and execute large-scale virtual screening studies for lead identification.


Key Features:

  • PyMOL integration: Implemented as a plugin for PyMOL to couple virtual screening workflows with molecular visualization.
  • Software integration: Integrates local and cluster-based software packages to run screening computations across heterogeneous environments.
  • Ligand-Based Virtual Screening (LBVS) module: Provides LBVS for cases when the receptor 3D structure is unknown or as a pre-filtering step, and has been reported to outperform the SBVS module in certain contexts.
  • Structure-Based Virtual Screening (SBVS) module: Provides SBVS for analyses when 3D receptor structures are available.
  • Scalability and performance: Scales to cluster deployments and can screen several million molecules in under one month on approximately 100 modern processors.
  • Benchmarking: Evaluated using a reduced set from the Directory of Useful Decoys database.

Scientific Applications:

  • Large-scale virtual screening: Enables screening of extensive chemical libraries using both LBVS and SBVS approaches.
  • Drug discovery lead identification: Facilitates identification of potential lead compounds across millions of molecules.
  • Screening strategy selection: Supports tailoring between ligand-based and structure-based strategies depending on availability of receptor 3D structures.

Methodology:

Applies a dual computational approach using both LBVS and SBVS techniques, integrates local and cluster-based software packages, and was tested on a reduced set from the Directory of Useful Decoys database while scaling to multi-processor clusters.

Topics

Details

Tool Type:
desktop application
Operating Systems:
Linux, Windows
Added:
8/3/2017
Last Updated:
12/10/2018

Operations

Publications

Cabrera ÁC, et al. VSDMIP 1.5: an automated structure- and ligand-based virtual screening platform with a PyMOL graphical user interface. J Comput Aided Mol Des. 2011; 25:813-24. doi: 10.1007/s10822-011-9465-6

PMID: 21826555

Documentation

Links