Condensed-phase molecular representation to link structure and thermodynamics in molecular dynamics

Code accompanying the publication at https://doi.org/10.1021/acs.jctc.3c00201

The scripts were created specifically for this project, with specific expectations for directory paths and some hard-coded filenames and other settings.

• Python version >= 3.9
• package list requirements.txt

Inserting angles and constraints into the GROMACS topology files for small molecule graphs:

• isomorphism_find_unique.py
  • A small number of structures make up the entire set, the variations between the small molecules are based on different combinations of coarse-grained bead types.
• make_template_file.py
  • Suitable angles and constraints are manually optimized for one example of each of the recurring structures and subsequently used as a template.
• isomorphs_insert_constraints.py
  • Iterating over the entire set of small molecule graphs, identifiying the correct template and inserting the settings for angles and constraints.

Generating the SLATM representations:

The Spectrum of London and Axilrod-Teller-Muto (SLATM) potential was defined by Huang, Symonds and von Lilienfeld, https://arxiv.org/abs/1807.04259.

• analyze_structures.py
  • Handles loading of trajectory files and the required steps to translate the coarse-grained MD trajectories into SLATM representations.
  • clean_trajectories.py
    • Corrects for periodic boundary conditions, centers systems around the solutes, selects frames by solute position.
  • preprocessing.py
    • Selects solutes and environment particles within the long-range interaction cutoff distance around the solutes' center of mass.
  • generate_representations.py
    • Handles the generation of the list of possible many-body interactions and unique particle identifiers required by the QML SLATM method.
    • Generates the SLATM representations.
    • Saves the results as pickled pandas dataframes.

Analyzing a set of SLATM representations with PCA:

• analyze_SLATMs.py
  • Loads the SLATM representations and required additional files,
  • Handles preprocessing of SLATM representations including normalization for PCA
  • PCA embedding of the SLATM representations

Visualizing the PCA results:

• generate_labels.py
  • Loads principal components and additional information, generates descriptors for further analysis.
• plot_cross_correlations.py
  • Cross-correlates a descriptor to principal components, performs linear regression.
• correlate_loadings_interactions.py
  • Visualizes the most relevant interactions selected by their loading values (eigenvecor * sqrt(eigenvalue)) to provide an idea about 3D structural aspects.
• biplot_scores_weights.py
  • Plots most pairs of principal components and their most relevant eigenvector coefficients. Colored by a previously generated descriptor.