oddt.scoring.functions package

Submodules

oddt.scoring.functions.NNScore module

class oddt.scoring.functions.NNScore.nnscore(protein=None, n_jobs=-1)[source]

Bases: oddt.scoring.scorer

Methods

fit(ligands, target, *args, **kwargs) Trains model on supplied ligands and target values
gen_training_data(pdbbind_dir[, …])
load([filename, pdbbind_version])
predict(ligands, *args, **kwargs) Predicts values (eg.
predict_ligand(ligand) Local method to score one ligand and update it’s scores.
predict_ligands(ligands) Method to score ligands lazily
save(filename) Saves scoring function to a pickle file.
score(ligands, target, *args, **kwargs) Methods estimates the quality of prediction as squared correlation coefficient (R^2)
set_protein(protein) Proxy method to update protein in all relevant places.
train([home_dir, sf_pickle, pdbbind_version])
fit(ligands, target, *args, **kwargs)

Trains model on supplied ligands and target values

Parameters:

ligands: array-like of ligands

Ground truth (correct) target values.

target: array-like of shape = [n_samples] or [n_samples, n_outputs]

Estimated target values.

gen_training_data(pdbbind_dir, pdbbind_versions=(2007, 2012, 2013, 2014, 2015, 2016), home_dir=None)[source]
classmethod load(filename='', pdbbind_version=2016)[source]
predict(ligands, *args, **kwargs)

Predicts values (eg. affinity) for supplied ligands

Parameters:

ligands: array-like of ligands

Ground truth (correct) target values.

target: array-like of shape = [n_samples] or [n_samples, n_outputs]

Estimated target values.

Returns:

predicted: np.array or array of np.arrays of shape = [n_ligands]

Predicted scores for ligands

predict_ligand(ligand)

Local method to score one ligand and update it’s scores.

Parameters:

ligand: oddt.toolkit.Molecule object

Ligand to be scored

Returns:

ligand: oddt.toolkit.Molecule object

Scored ligand with updated scores

predict_ligands(ligands)

Method to score ligands lazily

Parameters:

ligands: iterable of oddt.toolkit.Molecule objects

Ligands to be scored

Returns:

ligand: iterator of oddt.toolkit.Molecule objects

Scored ligands with updated scores

save(filename)

Saves scoring function to a pickle file.

Parameters:

filename: string

Pickle filename

score(ligands, target, *args, **kwargs)

Methods estimates the quality of prediction as squared correlation coefficient (R^2)

Parameters:

ligands: array-like of ligands

Ground truth (correct) target values.

target: array-like of shape = [n_samples] or [n_samples, n_outputs]

Estimated target values.

Returns:

r2: float

Squared correlation coefficient (R^2) for prediction

set_protein(protein)

Proxy method to update protein in all relevant places.

Parameters:

protein: oddt.toolkit.Molecule object

New default protein

train(home_dir=None, sf_pickle='', pdbbind_version=2016)[source]

oddt.scoring.functions.RFScore module

class oddt.scoring.functions.RFScore.rfscore(protein=None, n_jobs=-1, version=1, spr=0, **kwargs)[source]

Bases: oddt.scoring.scorer

Methods

fit(ligands, target, *args, **kwargs) Trains model on supplied ligands and target values
gen_training_data(pdbbind_dir[, …])
load([filename, version, pdbbind_version])
predict(ligands, *args, **kwargs) Predicts values (eg.
predict_ligand(ligand) Local method to score one ligand and update it’s scores.
predict_ligands(ligands) Method to score ligands lazily
save(filename) Saves scoring function to a pickle file.
score(ligands, target, *args, **kwargs) Methods estimates the quality of prediction as squared correlation coefficient (R^2)
set_protein(protein) Proxy method to update protein in all relevant places.
train([home_dir, sf_pickle, pdbbind_version])
fit(ligands, target, *args, **kwargs)

Trains model on supplied ligands and target values

Parameters:

ligands: array-like of ligands

Ground truth (correct) target values.

target: array-like of shape = [n_samples] or [n_samples, n_outputs]

Estimated target values.

gen_training_data(pdbbind_dir, pdbbind_versions=(2007, 2012, 2013, 2014, 2015, 2016), home_dir=None)[source]
classmethod load(filename='', version=1, pdbbind_version=2016)[source]
predict(ligands, *args, **kwargs)

Predicts values (eg. affinity) for supplied ligands

Parameters:

ligands: array-like of ligands

Ground truth (correct) target values.

target: array-like of shape = [n_samples] or [n_samples, n_outputs]

Estimated target values.

Returns:

predicted: np.array or array of np.arrays of shape = [n_ligands]

Predicted scores for ligands

predict_ligand(ligand)

Local method to score one ligand and update it’s scores.

Parameters:

ligand: oddt.toolkit.Molecule object

Ligand to be scored

Returns:

ligand: oddt.toolkit.Molecule object

Scored ligand with updated scores

predict_ligands(ligands)

Method to score ligands lazily

Parameters:

ligands: iterable of oddt.toolkit.Molecule objects

Ligands to be scored

Returns:

ligand: iterator of oddt.toolkit.Molecule objects

Scored ligands with updated scores

save(filename)

Saves scoring function to a pickle file.

Parameters:

filename: string

Pickle filename

score(ligands, target, *args, **kwargs)

Methods estimates the quality of prediction as squared correlation coefficient (R^2)

Parameters:

ligands: array-like of ligands

Ground truth (correct) target values.

target: array-like of shape = [n_samples] or [n_samples, n_outputs]

Estimated target values.

Returns:

r2: float

Squared correlation coefficient (R^2) for prediction

set_protein(protein)

Proxy method to update protein in all relevant places.

Parameters:

protein: oddt.toolkit.Molecule object

New default protein

train(home_dir=None, sf_pickle='', pdbbind_version=2016)[source]

Module contents

class oddt.scoring.functions.rfscore(protein=None, n_jobs=-1, version=1, spr=0, **kwargs)[source]

Bases: oddt.scoring.scorer

Methods

fit(ligands, target, *args, **kwargs) Trains model on supplied ligands and target values
gen_training_data(pdbbind_dir[, …])
load([filename, version, pdbbind_version])
predict(ligands, *args, **kwargs) Predicts values (eg.
predict_ligand(ligand) Local method to score one ligand and update it’s scores.
predict_ligands(ligands) Method to score ligands lazily
save(filename) Saves scoring function to a pickle file.
score(ligands, target, *args, **kwargs) Methods estimates the quality of prediction as squared correlation coefficient (R^2)
set_protein(protein) Proxy method to update protein in all relevant places.
train([home_dir, sf_pickle, pdbbind_version])
fit(ligands, target, *args, **kwargs)

Trains model on supplied ligands and target values

Parameters:

ligands: array-like of ligands

Ground truth (correct) target values.

target: array-like of shape = [n_samples] or [n_samples, n_outputs]

Estimated target values.

gen_training_data(pdbbind_dir, pdbbind_versions=(2007, 2012, 2013, 2014, 2015, 2016), home_dir=None)[source]
classmethod load(filename='', version=1, pdbbind_version=2016)[source]
predict(ligands, *args, **kwargs)

Predicts values (eg. affinity) for supplied ligands

Parameters:

ligands: array-like of ligands

Ground truth (correct) target values.

target: array-like of shape = [n_samples] or [n_samples, n_outputs]

Estimated target values.

Returns:

predicted: np.array or array of np.arrays of shape = [n_ligands]

Predicted scores for ligands

predict_ligand(ligand)

Local method to score one ligand and update it’s scores.

Parameters:

ligand: oddt.toolkit.Molecule object

Ligand to be scored

Returns:

ligand: oddt.toolkit.Molecule object

Scored ligand with updated scores

predict_ligands(ligands)

Method to score ligands lazily

Parameters:

ligands: iterable of oddt.toolkit.Molecule objects

Ligands to be scored

Returns:

ligand: iterator of oddt.toolkit.Molecule objects

Scored ligands with updated scores

save(filename)

Saves scoring function to a pickle file.

Parameters:

filename: string

Pickle filename

score(ligands, target, *args, **kwargs)

Methods estimates the quality of prediction as squared correlation coefficient (R^2)

Parameters:

ligands: array-like of ligands

Ground truth (correct) target values.

target: array-like of shape = [n_samples] or [n_samples, n_outputs]

Estimated target values.

Returns:

r2: float

Squared correlation coefficient (R^2) for prediction

set_protein(protein)

Proxy method to update protein in all relevant places.

Parameters:

protein: oddt.toolkit.Molecule object

New default protein

train(home_dir=None, sf_pickle='', pdbbind_version=2016)[source]
class oddt.scoring.functions.nnscore(protein=None, n_jobs=-1)[source]

Bases: oddt.scoring.scorer

Methods

fit(ligands, target, *args, **kwargs) Trains model on supplied ligands and target values
gen_training_data(pdbbind_dir[, …])
load([filename, pdbbind_version])
predict(ligands, *args, **kwargs) Predicts values (eg.
predict_ligand(ligand) Local method to score one ligand and update it’s scores.
predict_ligands(ligands) Method to score ligands lazily
save(filename) Saves scoring function to a pickle file.
score(ligands, target, *args, **kwargs) Methods estimates the quality of prediction as squared correlation coefficient (R^2)
set_protein(protein) Proxy method to update protein in all relevant places.
train([home_dir, sf_pickle, pdbbind_version])
fit(ligands, target, *args, **kwargs)

Trains model on supplied ligands and target values

Parameters:

ligands: array-like of ligands

Ground truth (correct) target values.

target: array-like of shape = [n_samples] or [n_samples, n_outputs]

Estimated target values.

gen_training_data(pdbbind_dir, pdbbind_versions=(2007, 2012, 2013, 2014, 2015, 2016), home_dir=None)[source]
classmethod load(filename='', pdbbind_version=2016)[source]
predict(ligands, *args, **kwargs)

Predicts values (eg. affinity) for supplied ligands

Parameters:

ligands: array-like of ligands

Ground truth (correct) target values.

target: array-like of shape = [n_samples] or [n_samples, n_outputs]

Estimated target values.

Returns:

predicted: np.array or array of np.arrays of shape = [n_ligands]

Predicted scores for ligands

predict_ligand(ligand)

Local method to score one ligand and update it’s scores.

Parameters:

ligand: oddt.toolkit.Molecule object

Ligand to be scored

Returns:

ligand: oddt.toolkit.Molecule object

Scored ligand with updated scores

predict_ligands(ligands)

Method to score ligands lazily

Parameters:

ligands: iterable of oddt.toolkit.Molecule objects

Ligands to be scored

Returns:

ligand: iterator of oddt.toolkit.Molecule objects

Scored ligands with updated scores

save(filename)

Saves scoring function to a pickle file.

Parameters:

filename: string

Pickle filename

score(ligands, target, *args, **kwargs)

Methods estimates the quality of prediction as squared correlation coefficient (R^2)

Parameters:

ligands: array-like of ligands

Ground truth (correct) target values.

target: array-like of shape = [n_samples] or [n_samples, n_outputs]

Estimated target values.

Returns:

r2: float

Squared correlation coefficient (R^2) for prediction

set_protein(protein)

Proxy method to update protein in all relevant places.

Parameters:

protein: oddt.toolkit.Molecule object

New default protein

train(home_dir=None, sf_pickle='', pdbbind_version=2016)[source]