rnaglib.tasks

Task objects hold everything you need to feed a prediction model and evaluate its performance on a variety of tasks, as well as to easily implement your own tasks.

Abstract classes

Subclass these to create your own tasks.

Task(root[, debug, in_memory, recompute, ...])	Abstract class for a benchmarking task using the rnaglib datasets.
ClassificationTask(**kwargs)	Base class for classification tasks.
ResidueClassificationTask([additional_metadata])	Classification task at the residue level.
RNAClassificationTask([additional_metadata])	Classification task at the RNA (graph) level.

RNA-level Classification

These tasks take as input an RNA and predict a property of the whole molecule.

RNAGo([size_thresholds])

Predict the GO terms associated with the Rfam family of a given RNA chain.

Residue-level Classification

These tasks take as input an RNA and predict a property of each residue of the molecule.

BindingSite([redundancy, cutoff, ...])	Predict the RNA residues which are the most likely to be part of binding sites for small molecule ligands
BenchmarkBindingSite([cutoff])	Version of RNA-Site implemented using the data and splitting of the experiment by Su et al. (2021).
ChemicalModification([size_thresholds, ...])	Residue-level binary classification task to predict whether a given residue is chemically modified.
ProteinBindingSite([size_thresholds])	The job is to predict a binary variable at each residue representing the probability that a residue belongs to a protein-binding interface
InverseFolding([size_thresholds])	RNA design task, taking as input the structures with the identity of the residues masked and trying to find it back
gRNAde([size_thresholds])	This class is a subclass of InverseFolding and is used to train a model on the gRNAde dataset.

Substructure-level Classification

Classification to predict properties of substructures of a whole molecule (e.g. binding pockets)

LigandIdentification([size_thresholds, ...])

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

Substructure-level Regression

Regression to predict properties of substructures of a whole molecule (e.g. binding pockets)

VirtualScreening(root[, ligand_framework, ...])

RNA binding pocket-small molecule binding affinity prediction