AABBA

AABBA is a Graph Kernel Python library for applying autocorrelation (AC) functions. It transforms molecular graphs into a fixed-length vector that combines generic properties (GP) and natural bond orbital (NBO) properties.

**Generic properties (GP) and periodic-table (PT) features are employed indistinctly.

Requirements

• Python 3.7.0+
• NumPy 1.21.5+
• NetworkX 2.6.3

Scripts

• graph_info.py - read the chemical graph and extract the indexes of the nodes and edges at different depths. It also provides the labels of the features.
• ac_functions.py - perform the autocorrelation functions.
• utilities.py - tools for manipulating the data.
• ac_PT_multithread.py - parallel implementation to perform the autocorrelation functions with periodic-table features.
• ac_NBO_multithread.py - parallel implementation to perform the autocorrelation functions with nbo features.

How to use

Clone the code.

git clone

Install the requirements.

Manipulate ac_NBO_multithread.py and ac_PT_multithread.py files:

1. Select the parameters to perform the autocorrelation functions.

• ac_operator: arithmetic operator applied to the properties.

ac_operator (str)
MA	metal-centered product autocorrelation
MD	metal-centered deltametric autocorrelation
MS	metal-centered summetric autocorrelation
MR	metal-centered ratiometric autocorrelation
FA	full product autocorrelation
FD	full deltametric autocorrelation
FS	full summetric autocorrelation
FR	full ratiometric autocorrelation

**MC refers to metal-centered autocorrelation and F refers to full autocorrelation

• walk: types of autocorrelation. The different modes to read the chemical graph.

walk variable (str)
AA	atom-atom correlation
BBavg	bond-bond autocorrelation with bond average (only MC)
BB	bond-bond autocorrelation with superbond (for MC), and with full bond-bond (F)
AB	bond-atom autocorrelation
ABBAavg	implicit autocorrelation with bond average (only MC); select the model 1, 2, 3, 4, 5
ABBA	implicit autocorrelation with individual bond (only F); select the model 1, 2, 3, 4, 5

• model_number: 1, 2, 3 to be performed with periodic table features (PT); and 4, 5 to be performed with nbo features (NBO). The attributes contain the following features:

model_number (str)
1	Zi, Zj, Ti, Tj, Xi, Xj, d, BO, I
2	Zi, Zj, Ti, Tj, Xi-Xj, d, BO, I
3	Zi, Zj, Ti, Tj, Xi-Xj, Si, Sj, BO, I
4	qNati, qNatj, VNati, VNatj, Nsi, Nsj, Npi, Npj, Ndi, Ndj, NLPi, NLPj, NLVi, NLVj, BD, BONat, NBN , BNs, BNp, BNd, NBN∗ , BN∗s , BN∗p , BN∗d , I
5	qNati, qNatj, VNati, VNatj, NLPi, NLPj, LPEi, LPEj, LP∆Ei, LP∆Ej, NLVi, NLVj, LVEi, LVEj, LV∆Ei, LV∆Ej, BD, BONat, NBN , BNE , BN∆E , NBN∗ , BN∗E , BN∗∆E , I

(i and j are the nodes of the edges)

• depth_max (int): maximum depth of the autocorrelation function.

2. Select the autocorrelation function to be performed and its return vector. Comment the rest of the functions.

3. Select the feature type (feature_type)

4. Adjust the path to the folder that contains the .gml files (path_to_gml).

5. Create a folder to save the results (path_to_folder).