R11301/79bcac3b5cb3master
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README.md
- Data
- Structures
The matrices for 3 target structures (to synthesize) and a database of 7165 query structures (to combine to build the target) are compressed in data.npz
Within python, it can be read like:
data = np.load("data.npz", allow_pickle=True)
where data.files will return the names of the numpy arrays (should be target_labels, target_CMs, target_ncharges, database_labels, database_CMs, database_ncharges) where CMs are the matrices (of target and database respectively) and the corresponding arrays can be accessed like:
data["target_labels"]
For more details see the documentation: https://het.as.utexas.edu/HET/Software/Numpy/reference/generated/numpy.savez.html
Connectivity / functional group information
Adjacency matrices and functional group information derived from the connectivity are compressed in connectivity_data.npz.
Within python, it can be read like:
connectivity_data = np.load("connectivity_data.npz")
the corresponding keys are fg_counts_targets for the functional group counts of each of the 3 target molecules,fg_counts_frags for the functional group counts of each of the fragment molecules, frag_adj_matrices for the adjacency matrices of the fragments and target_adj_matrices for the adjacency matrices of the target molecules. The order is the same as those in data containing the structures.