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Welcome to the PyMOL Wiki!
The community-run support site for the PyMOL molecular viewer.
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News & Updates
POSF PyMOL Open-Source Fellowship program accepting applications for 2018-2019
New Plugin ProBiS H2O is a new plugin for identification of conserved waters in protein structures.
Official Release PyMOL v2.1 has been released on March 13, 2018.
Selection keywords New polymer.protein and polymer.nucleic selection keywords. Thanks everyone who participated in the poll!
Plugin Update MOLE 2.5 is an updated version of channel analysis software in PyMOL
New Script dssr_block is a wrapper for DSSR (3dna) and creates block-shaped nucleic acid cartoons
New Plugin LiSiCA is a new plugin for 2D and 3D ligand based virtual screening using a fast maximum clique algorithm.
New Plugin PyANM is a new plugin for easier Anisotropic Network Model (ANM) building and visualising in PyMOL.
New Plugin Bondpack is a collection of PyMOL plugins for easy visualization of atomic bonds.
New Plugin MOLE 2.0 is a new plugin for rapid analysis of biomacromolecular channels in PyMOL.
3D using Geforce PyMOL can now be visualized in 3D using Nvidia GeForce video cards (series 400+) with 120Hz monitors and Nvidia 3D Vision, this was previously only possible with Quadro video cards.
Older News See Older News.
Did you know...

Cluster mols

Cluster mols py pymol.png

cluster_mols is a PyMOL plugin that allows the user to quickly select compounds from a virtual screen to be purchased or synthesized.

It helps the user by automatically clustering input compounds based on their molecular fingerprints [1] and loading them into the PyMOL window. cluster_mols also highlights both good and bad polar interactions between the ligands and a user specified receptor. Additionally there are a number of keyboard controls for selecting and extracting compounds, as well as functionality for searching online to see if there are vendors for a selected compound.


The basic work flow of can be broken up into three parts.

  1. Computing a similarity matrix from the input compounds
  2. Performing hierarchical clustering on the results from 1)
  3. Cutting the tree at a user-specified height and creating and sorting clusters

The results of 1 and 2 are saved to python pickle files so you do not have to recompute them in ..→

A Random PyMOL-generated Cover. See Covers.