I have created a graph for every frame. Nodes in the graph are the solvent
molecules, and two nodes are connected with and edge if the distance
between the associated solvent molecules is below a cutoff I chose. I have
systems with different solvation levels, some of then featuring "pockets"
of solvent molecules. These pockets are the clusters I'm interested in.
Algorithm networkx.connected_components
<
https://networkx.github.io/documentation/latest/reference/generated/networkx.algorithms.components.connected.connected_components.html>can
find the connected clusters from a graph. To create the graph, I am using
MDAnalysis to obtain the contact map between solvent molecules. Regarding
time, it takes 2.2seconds to create a contact map for 4132 solvent
molecules, which I think is reasonable (unless you have many thousands of
frames)
On Mon, Aug 3, 2015 at 1:17 PM, Jason Swails <jason.swails.gmail.com> wrote:
> On Mon, 2015-08-03 at 12:57 -0400, Jose Borreguero wrote:
> > Thanks a lot, Jason. I'll go along with python for the clustering step. I
> > found module networkx which is very straighforward for clustering, and
> > quite fast.
>
> How are you using networkx for this? I've used it to define a bond
> graph in molecular topology before, but I don't see how networkx maps to
> clustering here. Do you have a fully connected graph whose edge weights
> are the distance between the nodes or something? That sounds like it
> would be an expensive graph to create. Keep in mind that the PBC will
> have an effect on the clusters, so how you pick the unit cell
> representation is likely important.
>
> I've used sklearn to cluster in the past, and I've found it to be pretty
> easy to use, for what that's worth.
>
> All the best,
> Jason
>
> --
> Jason M. Swails
> BioMaPS,
> Rutgers University
> Postdoctoral Researcher
>
>
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Received on Mon Aug 03 2015 - 12:30:03 PDT
