rnaglib.tasks.LigandIdentification

class rnaglib.tasks.LigandIdentification(size_thresholds=(15, 500), admissible_ligands=('PAR', 'LLL', '8UZ'), use_balanced_sampler=False, **kwargs)

Binding pocket-level task where the job is to predict the (small molecule) ligand which is the most likely to bind a binding pocket with a given structure

Task type: multi-class classification Task level: substructure-level

Parameters:

• size_thresholds (tuple[int]) – range of RNA sizes to keep in the task dataset(default (15, 500))

• admissible_ligands (tuple[str]) – list of the names of the ligands to include in the dataset (default (‘PAR’, ‘LLL’, ‘8UZ’)). By default, they are paromomycin (PAR), LLL and 8UZ since these are the four most frequent small molecules binding RNAs in our database.

• use_balanced_sampler (bool) – whether to sample RNAs according to the distribution of their classes

__init__(size_thresholds=(15, 500), admissible_ligands=('PAR', 'LLL', '8UZ'), use_balanced_sampler=False, **kwargs)

Methods

__init__([size_thresholds, ...])
add_feature(feature[, feature_level, is_input])	Add a feature to the dataset.
add_representation(representation)	Add a representation transform to the dataset.
add_rna_to_building_list(all_rnas, rna)	Add an RNA to the building list.
compute_distances()	Compute similarity distances between RNAs in the dataset.
compute_metrics(all_preds, all_probs, all_labels)	Compute classification metrics aggregated across all predictions.
compute_one_metric(preds, probs, labels)	Compute classification metrics for a single set of predictions.
create_dataset_from_list(rnas)	Compute an RNADataset object from the lists touched in add_rna_to_building_list.
describe()	Get description of task dataset.
dummy_inference()	Run dummy inference on the test dataset.
evaluate(model, loader)	Evaluate model performance on a dataset.
from_scratch(size_thresholds)	Create task dataset from scratch.
from_zenodo()	Download the task dataset from Zenodo and load it.
get_split_datasets([recompute])	Get train, validation, and test datasets.
get_split_loaders([recompute])	Get train, validation, and test dataloaders.
get_task_vars()	Specifies the FeaturesComputer object of the tasks which defines the features which have to be added to the RNAs (graphs) and nucleotides (graph nodes)
init_metadata([additional_metadata])	Initialize dictionary to hold key/value pairs to self.metadata.
load()	Load dataset and splits from disk.
post_process()	The task-specific post processing steps to remove redundancy and compute distances which will be used by the splitters.
process()	Creates the task-specific dataset.
remove_redundancy()	Remove redundant RNAs from the dataset based on similarity.
remove_representation(representation_name)	Remove a representation transform from the dataset.
set_datasets([recompute])	Set the train, val and test datasets.
set_loaders([recompute])	Sets the dataloader properties.
split(dataset)	Calls the splitter and returns train, val, test splits.
to_csv(path)	Write a single CSV with all task data.
write()	Save task data and splits to root.

Attributes

default_metric
default_splitter	Returns the splitting strategy to be used for this specific task.
dummy_model	Get a dummy model for testing purposes.
input_var
name
target_var
task_id	Task hash is a hash of all RNA ids and node IDs in the dataset.
version