torchdrug.metrics#
Basic Metrics#
AUROC#
- area_under_roc(pred, target)[source]#
Area under receiver operating characteristic curve (ROC).
- Parameters
pred (Tensor) – predictions of shape \((n,)\)
target (Tensor) – binary targets of shape \((n,)\)
- AUROC()#
alias of
torchdrug.metrics.area_under_roc
AUPRC#
- area_under_prc(pred, target)[source]#
Area under precision-recall curve (PRC).
- Parameters
pred (Tensor) – predictions of shape \((n,)\)
target (Tensor) – binary targets of shape \((n,)\)
- AUPRC()#
alias of
torchdrug.metrics.area_under_prc
R2#
Variadic Accuracy#
- variadic_accuracy(input, target, size)[source]#
Compute classification accuracy over variadic sizes of categories.
Suppose there are \(N\) samples, and the number of categories in all samples is summed to :math`B`.
- Parameters
input (Tensor) – prediction of shape \((B,)\)
target (Tensor) – target of shape \((N,)\). Each target is a relative index in a sample.
size (Tensor) – number of categories of shape \((N,)\)
Chemical Metrics#
SA#
- SA(pred)[source]#
Synthetic accesibility score.
- Parameters
pred (PackedMolecule) – molecules to evaluate
QED#
- QED(pred)[source]#
Quantitative estimation of drug-likeness.
- Parameters
pred (PackedMolecule) – molecules to evaluate
Chemical Validity#
- chemical_validity(pred)[source]#
Chemical validity of molecules.
- Parameters
pred (PackedMolecule) – molecules to evaluate
LogP#
- logP(pred)[source]#
Logarithm of partition coefficient between octanol and water for a compound.
- Parameters
pred (PackedMolecule) – molecules to evaluate
Penalized LogP#
- penalized_logP(pred)[source]#
Logarithm of partition coefficient, penalized by cycle length and synthetic accessibility.
- Parameters
pred (PackedMolecule) – molecules to evaluate