Marianski Lab · Python 0.3.9

From coordinates to understanding.

Explore molecular structures, run machine-learning molecular dynamics, characterize carbohydrates, and turn computational chemistry data into figures. Learn matmalib by running it, one experiment at a time.

Start your first analysis Run an MLMD simulation Explore the API

A working example, right here

This example loads a bundled glucose structure, identifies its stereochemistry, and draws an SNFG symbol. Select Run, then change show_names to False and run it again. The first run downloads Python and the scientific packages; later examples on the same page reuse them.

from matmalib import Glycan

glucose = Glycan.from_file("data/glucose.xyz")
print(glucose.identify_sugars())
print("Ring shape:", glucose.identify_ring_puckers()[0]["label"])
fig, ax = glucose.snfg(show_names=True)

The name is Glc, with D configuration and beta anomeric stereochemistry. Its canonical ring label is 4C1. The blue circle is the SNFG symbol for glucose. How browser examples work →

Choose your next experiment

01 / BUILD & MEASURE

Understand your molecule

Create atoms and molecular states. Measure bonds, angles, and torsions, then edit the geometry.

Work with molecules →

02 / RECOGNIZE & CLASSIFY

Read a carbohydrate

Identify monomers and stereochemistry, draw SNFG diagrams, and assign canonical ring puckers.

Explore glycans →

03 / SIMULATE & ANALYZE

Run molecular dynamics

Choose a potential, solvate and equilibrate a system, run production or metadynamics, and analyze the resulting free energy.

Start with MLMD →

Learn a complete workflow

Start with	Follow this path	Finish with
Atomic coordinates	Molecules → File I/O → Energies	A measured structure and calculation inputs
A carbohydrate structure	Glycans → Puckering → Trajectories	Ring assignments, torsions, and population maps
Many candidate conformers	Conformer clustering → Plotting	Representative structures and a dendrogram
Numerical simulation data	File I/O → Free energy → Spectra	Interpretable figures and reusable exports
A structure and an ML potential	Run MLMD → Solvation → Metadynamics → FES analysis	A streamed trajectory, restart checkpoints, and free-energy reports

The feature map connects every public feature family to a tutorial and reference. The API reference takes signatures and source directly from the documented checkout. See conventions for units and atom ordering, and core limitations before using legacy file workflows.