network_analysis#

konnektor.network_analysis.network_analysis.get_component_connectivities(ligand_network: LigandNetwork, normalize: bool = False) → dict[SmallMoleculeComponent, float | int]#

Calculate the connectivity for all nodes in the graph.

Parameters:

  • ligand_network (LigandNetwork)

  • normalize (bool, optional) – If True, normalize each node’s connectivity by the total number of edges in the LigandNetwork, by default False.

Returns:

Dict of nodes and their corresponding connectivity (int if normalize=False, otherwise float).

Return type:

dict[SmallMoleculeComponent, float | int]

konnektor.network_analysis.network_analysis.get_component_number_cycles(ligand_network: LigandNetwork, higher_bound: int = 4) → dict[SmallMoleculeComponent, int | int64]#

Calculate the number of cycles each node is present in.

Parameters:

  • ligand_network (LigandNetwork)

  • higher_bound (int, optional) – Largest cycle (by number of nodes) to be considered a “cycle” when counting, by default 4.

Returns:

Dict of nodes (SmallMoleculeComponent) and their corresponding number of cycles counted.

Return type:

dict[SmallMoleculeComponent, int|np.int64]

konnektor.network_analysis.network_analysis.get_component_scores(ligand_network: LigandNetwork, normalize: bool = True) → dict[SmallMoleculeComponent, float]#

Calculate the score for all nodes in the graph, where a node’s score is defined as the sum of the scores of all its edges.

Parameters:

  • ligand_network (LigandNetwork)

  • normalize (bool, optional) – If True, normalize each node’s score by the total number of edges in the LigandNetwork, by default True.

Returns:

Dict of nodes (SmallMoleculeComponent) and their corresponding scores.

Return type:

dict[SmallMoleculeComponent, float]

konnektor.network_analysis.network_analysis.get_is_connected(ligand_network: LigandNetwork) → bool#

Check whether all nodes in the LigandNetwork are connected to each other, ignoring edge direction.

A False value indicates that either some nodes have no edges or that there are separate networks that do not link to each other.

Deprecated since version 0.4.0: This function is deprecated. Use the .is_connected() method of ligand_network instead. See gufe.LigandNetwork docs for more details.

Parameters:

ligand_network (LigandNetwork)

Returns:

True if the LigandNetwork is connected, otherwise False.

Return type:

bool

konnektor.network_analysis.network_analysis.get_network_cost(ligand_network: LigandNetwork) → float#

Calculate the network cost based on summation of the edge costs, cost = 1 - score.

Parameters:

ligand_network (LigandNetwork)

Returns:

Summation of all edge costs in the LigandNetwork.

Return type:

float

konnektor.network_analysis.network_analysis.get_network_efficiency(ligand_network: LigandNetwork) → float#

Calculate the network efficiency, defined as the network score normalized by the number of edges in the network.

Parameters:

ligand_network (LigandNetwork)

Returns:

Network efficiency score.

Return type:

float

konnektor.network_analysis.network_analysis.get_network_score(ligand_network: LigandNetwork) → float#

Calculate the network score based on summation of the edge scores.

Parameters:

ligand_network (LigandNetwork)

Returns:

Summation of all edge scores in the LigandNetwork.

Return type:

float

konnektor.network_analysis.network_analysis.get_number_of_network_cycles(ligand_network: LigandNetwork, higher_bound: int = 3) → int#

Calculate the number of cycles present in the network for cycles up to size higher_bound.

Parameters:

  • ligand_network (LigandNetwork)

  • higher_bound (int, optional) – Largest number of nodes in cycle to count, by default 3.

Returns:

Number of cycles of size <= higher_bound present in the LigandNetwork.

Return type:

int

konnektor.network_analysis.network_analysis.get_transformation_failure_robustness(ligand_network: LigandNetwork, failure_rate: float = 0.05, nrepeats: int = 100, seed: int | None = None) → float64#
Estimate the robustness of a LigandNetwork by:

  1. Removing a number of edges corresponding to the percentage given by failure_rate.

  2. Randomly repeating step 1 nrepeats times.

  3. Average of the results of the repeats in step 2 for a result between 0 (was always disconnected) and 1 (never was disconnected).

Parameters:

  • ligand_network (LigandNetwork)

  • failure_rate (float, optional) – Failure rate of edges to use in the estimation, by default 0.05.

  • nrepeats (int, optional) – Number of repeat samples to use in the estimation, by default 100.

  • seed (int | None, optional) – Seed to use for the random number generator, by default None.

Returns:

Average probability that the network remained connected when failure_rate fraction of edges was removed. Between 0.0 (the network was always disconnected) and 1.0 (network was never disconnected).

Return type:

np.float64