oddt package¶
Subpackages¶
Submodules¶
oddt.datasets module¶
Datasets wrapped in conviniet models
oddt.interactions module¶
Module calculates interactions between two molecules (proein-protein, protein-ligand, small-small). Currently following interacions are implemented:
- hydrogen bonds
- halogen bonds
- pi stacking (parallel and perpendicular)
- salt bridges
- hydrophobic contacts
- pi-cation
- metal coordination
- pi-metal
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oddt.interactions.
close_contacts
(x, y, cutoff, x_column='coords', y_column='coords')[source]¶ Returns pairs of atoms which are within close contac distance cutoff.
Parameters: x, y : atom_dict-type numpy array
Atom dictionaries generated by oddt.toolkit.Molecule objects.
- cutoff : float
Cutoff distance for close contacts
- x_column, ycolumn : string, (default=’coords’)
Column containing coordinates of atoms (or pseudo-atoms, i.e. ring centroids)
Returns: x_, y_ : atom_dict-type numpy array
Aligned pairs of atoms in close contact for further processing.
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oddt.interactions.
hbond_acceptor_donor
(mol1, mol2, cutoff=3.5, base_angle=120, tolerance=30)[source]¶ Returns pairs of acceptor-donor atoms, which meet H-bond criteria
Parameters: mol1, mol2 : oddt.toolkit.Molecule object
Molecules to compute H-bond acceptor and H-bond donor pairs
- cutoff : float, (default=3.5)
Distance cutoff for A-D pairs
- base_angle : int, (default=120)
Base angle determining allowed direction of hydrogen bond formation, which is devided by the number of neighbors of acceptor atom to establish final directional angle
- tolerance : int, (default=30)
Range (+/- tolerance) from perfect direction (base_angle/n_neighbors) in which H-bonds are considered as strict.
Returns: a, d : atom_dict-type numpy array
Aligned arrays of atoms forming H-bond, firstly acceptors, secondly donors.
- strict : numpy array, dtype=bool
Boolean array align with atom pairs, informing whether atoms form ‘strict’ H-bond (pass all angular cutoffs). If false, only distance cutoff is met, therefore the bond is ‘crude’.
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oddt.interactions.
hbond
(mol1, mol2, *args, **kwargs)[source]¶ Calculates H-bonds between molecules
Parameters: mol1, mol2 : oddt.toolkit.Molecule object
Molecules to compute H-bond acceptor and H-bond donor pairs
- cutoff : float, (default=3.5)
Distance cutoff for A-D pairs
- base_angle : int, (default=120)
Base angle determining allowed direction of hydrogen bond formation, which is devided by the number of neighbors of acceptor atom to establish final directional angle
- tolerance : int, (default=30)
Range (+/- tolerance) from perfect direction (base_angle/n_neighbors) in which H-bonds are considered as strict.
Returns: mol1_atoms, mol2_atoms : atom_dict-type numpy array
Aligned arrays of atoms forming H-bond
- strict : numpy array, dtype=bool
Boolean array align with atom pairs, informing whether atoms form ‘strict’ H-bond (pass all angular cutoffs). If false, only distance cutoff is met, therefore the bond is ‘crude’.
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oddt.interactions.
halogenbond_acceptor_halogen
(mol1, mol2, base_angle_acceptor=120, base_angle_halogen=180, tolerance=30, cutoff=4)[source]¶ Returns pairs of acceptor-halogen atoms, which meet halogen bond criteria
Parameters: mol1, mol2 : oddt.toolkit.Molecule object
Molecules to compute halogen bond acceptor and halogen pairs
- cutoff : float, (default=4)
Distance cutoff for A-H pairs
- base_angle_acceptor : int, (default=120)
Base angle determining allowed direction of halogen bond formation, which is devided by the number of neighbors of acceptor atom to establish final directional angle
- base_angle_halogen : int (default=180)
Ideal base angle between halogen bond and halogen-neighbor bond
- tolerance : int, (default=30)
Range (+/- tolerance) from perfect direction (base_angle/n_neighbors) in which halogen bonds are considered as strict.
Returns: a, h : atom_dict-type numpy array
Aligned arrays of atoms forming halogen bond, firstly acceptors, secondly halogens
- strict : numpy array, dtype=bool
Boolean array align with atom pairs, informing whether atoms form ‘strict’ halogen bond (pass all angular cutoffs). If false, only distance cutoff is met, therefore the bond is ‘crude’.
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oddt.interactions.
halogenbond
(mol1, mol2, **kwargs)[source]¶ Calculates halogen bonds between molecules
Parameters: mol1, mol2 : oddt.toolkit.Molecule object
Molecules to compute halogen bond acceptor and halogen pairs
- cutoff : float, (default=4)
Distance cutoff for A-H pairs
- base_angle_acceptor : int, (default=120)
Base angle determining allowed direction of halogen bond formation, which is devided by the number of neighbors of acceptor atom to establish final directional angle
- base_angle_halogen : int (default=180)
Ideal base angle between halogen bond and halogen-neighbor bond
- tolerance : int, (default=30)
Range (+/- tolerance) from perfect direction (base_angle/n_neighbors) in which halogen bonds are considered as strict.
Returns: mol1_atoms, mol2_atoms : atom_dict-type numpy array
Aligned arrays of atoms forming halogen bond
- strict : numpy array, dtype=bool
Boolean array align with atom pairs, informing whether atoms form ‘strict’ halogen bond (pass all angular cutoffs). If false, only distance cutoff is met, therefore the bond is ‘crude’.
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oddt.interactions.
pi_stacking
(mol1, mol2, cutoff=5, tolerance=30)[source]¶ Returns pairs of rings, which meet pi stacking criteria
Parameters: mol1, mol2 : oddt.toolkit.Molecule object
Molecules to compute ring pairs
- cutoff : float, (default=5)
Distance cutoff for Pi-stacking pairs
- tolerance : int, (default=30)
Range (+/- tolerance) from perfect direction (parallel or perpendicular) in which pi-stackings are considered as strict.
Returns: r1, r2 : ring_dict-type numpy array
Aligned arrays of rings forming pi-stacking
- strict_parallel : numpy array, dtype=bool
Boolean array align with ring pairs, informing whether rings form ‘strict’ parallel pi-stacking. If false, only distance cutoff is met, therefore the stacking is ‘crude’.
- strict_perpendicular : numpy array, dtype=bool
Boolean array align with ring pairs, informing whether rings form ‘strict’ perpendicular pi-stacking (T-shaped, T-face, etc.). If false, only distance cutoff is met, therefore the stacking is ‘crude’.
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oddt.interactions.
salt_bridge_plus_minus
(mol1, mol2, cutoff=4)[source]¶ Returns pairs of plus-mins atoms, which meet salt bridge criteria
Parameters: mol1, mol2 : oddt.toolkit.Molecule object
Molecules to compute plus and minus pairs
- cutoff : float, (default=4)
Distance cutoff for A-H pairs
Returns: plus, minus : atom_dict-type numpy array
Aligned arrays of atoms forming salt bridge, firstly plus, secondly minus
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oddt.interactions.
salt_bridges
(mol1, mol2, *args, **kwargs)[source]¶ Calculates salt bridges between molecules
Parameters: mol1, mol2 : oddt.toolkit.Molecule object
Molecules to compute plus and minus pairs
- cutoff : float, (default=4)
Distance cutoff for plus-minus pairs
Returns: mol1_atoms, mol2_atoms : atom_dict-type numpy array
Aligned arrays of atoms forming salt bridges
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oddt.interactions.
hydrophobic_contacts
(mol1, mol2, cutoff=4)[source]¶ Calculates hydrophobic contacts between molecules
Parameters: mol1, mol2 : oddt.toolkit.Molecule object
Molecules to compute hydrophobe pairs
- cutoff : float, (default=4)
Distance cutoff for hydrophobe pairs
Returns: mol1_atoms, mol2_atoms : atom_dict-type numpy array
Aligned arrays of atoms forming hydrophobic contacts
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oddt.interactions.
pi_cation
(mol1, mol2, cutoff=5, tolerance=30)[source]¶ Returns pairs of ring-cation atoms, which meet pi-cation criteria
Parameters: mol1, mol2 : oddt.toolkit.Molecule object
Molecules to compute ring-cation pairs
- cutoff : float, (default=5)
Distance cutoff for Pi-cation pairs
- tolerance : int, (default=30)
Range (+/- tolerance) from perfect direction (perpendicular) in which pi-cation are considered as strict.
Returns: r1 : ring_dict-type numpy array
Aligned rings forming pi-stacking
- plus2 : atom_dict-type numpy array
Aligned cations forming pi-cation
- strict_parallel : numpy array, dtype=bool
Boolean array align with ring-cation pairs, informing whether they form ‘strict’ pi-cation. If false, only distance cutoff is met, therefore the interaction is ‘crude’.
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oddt.interactions.
acceptor_metal
(mol1, mol2, base_angle=120, tolerance=30, cutoff=4)[source]¶ Returns pairs of acceptor-metal atoms, which meet metal coordination criteria Note: This function is directional (mol1 holds acceptors, mol2 holds metals)
Parameters: mol1, mol2 : oddt.toolkit.Molecule object
Molecules to compute acceptor and metal pairs
- cutoff : float, (default=4)
Distance cutoff for A-M pairs
- base_angle : int, (default=120)
Base angle determining allowed direction of metal coordination, which is devided by the number of neighbors of acceptor atom to establish final directional angle
- tolerance : int, (default=30)
Range (+/- tolerance) from perfect direction (base_angle/n_neighbors) in metal coordination are considered as strict.
Returns: a, d : atom_dict-type numpy array
Aligned arrays of atoms forming metal coordination, firstly acceptors, secondly metals.
- strict : numpy array, dtype=bool
Boolean array align with atom pairs, informing whether atoms form ‘strict’ metal coordination (pass all angular cutoffs). If false, only distance cutoff is met, therefore the interaction is ‘crude’.
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oddt.interactions.
pi_metal
(mol1, mol2, cutoff=5, tolerance=30)[source]¶ Returns pairs of ring-metal atoms, which meet pi-metal criteria
Parameters: mol1, mol2 : oddt.toolkit.Molecule object
Molecules to compute ring-metal pairs
- cutoff : float, (default=5)
Distance cutoff for Pi-metal pairs
- tolerance : int, (default=30)
Range (+/- tolerance) from perfect direction (perpendicular) in which pi-metal are considered as strict.
Returns: r1 : ring_dict-type numpy array
Aligned rings forming pi-metal
- m : atom_dict-type numpy array
Aligned metals forming pi-metal
- strict_parallel : numpy array, dtype=bool
Boolean array align with ring-metal pairs, informing whether they form ‘strict’ pi-metal. If false, only distance cutoff is met, therefore the interaction is ‘crude’.
oddt.metrics module¶
Metrics for estimating performance of drug discovery methods implemented in ODDT
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oddt.metrics.
roc
(y_true, y_score, pos_label=None, sample_weight=None, drop_intermediate=True)¶ Compute Receiver operating characteristic (ROC)
Note: this implementation is restricted to the binary classification task.
Read more in the User Guide.
Parameters: y_true : array, shape = [n_samples]
True binary labels in range {0, 1} or {-1, 1}. If labels are not binary, pos_label should be explicitly given.
y_score : array, shape = [n_samples]
Target scores, can either be probability estimates of the positive class or confidence values.
pos_label : int
Label considered as positive and others are considered negative.
sample_weight : array-like of shape = [n_samples], optional
Sample weights.
drop_intermediate : boolean, optional (default=True)
Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve. This is useful in order to create lighter ROC curves.
New in version 0.17: parameter drop_intermediate.
Returns: fpr : array, shape = [>2]
Increasing false positive rates such that element i is the false positive rate of predictions with score >= thresholds[i].
tpr : array, shape = [>2]
Increasing true positive rates such that element i is the true positive rate of predictions with score >= thresholds[i].
thresholds : array, shape = [n_thresholds]
Decreasing thresholds on the decision function used to compute fpr and tpr. thresholds[0] represents no instances being predicted and is arbitrarily set to max(y_score) + 1.
See also
roc_auc_score
- Compute Area Under the Curve (AUC) from prediction scores
Notes
Since the thresholds are sorted from low to high values, they are reversed upon returning them to ensure they correspond to both
fpr
andtpr
, which are sorted in reversed order during their calculation.References
[R1] Wikipedia entry for the Receiver operating characteristic Examples
>>> import numpy as np >>> from sklearn import metrics >>> y = np.array([1, 1, 2, 2]) >>> scores = np.array([0.1, 0.4, 0.35, 0.8]) >>> fpr, tpr, thresholds = metrics.roc_curve(y, scores, pos_label=2) >>> fpr array([ 0. , 0.5, 0.5, 1. ]) >>> tpr array([ 0.5, 0.5, 1. , 1. ]) >>> thresholds array([ 0.8 , 0.4 , 0.35, 0.1 ])
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oddt.metrics.
auc
(x, y, reorder=False)[source]¶ Compute Area Under the Curve (AUC) using the trapezoidal rule
This is a general function, given points on a curve. For computing the area under the ROC-curve, see
roc_auc_score()
.Parameters: x : array, shape = [n]
x coordinates.
y : array, shape = [n]
y coordinates.
reorder : boolean, optional (default=False)
If True, assume that the curve is ascending in the case of ties, as for an ROC curve. If the curve is non-ascending, the result will be wrong.
Returns: auc : float
See also
roc_auc_score
- Computes the area under the ROC curve
precision_recall_curve
- Compute precision-recall pairs for different probability thresholds
Examples
>>> import numpy as np >>> from sklearn import metrics >>> y = np.array([1, 1, 2, 2]) >>> pred = np.array([0.1, 0.4, 0.35, 0.8]) >>> fpr, tpr, thresholds = metrics.roc_curve(y, pred, pos_label=2) >>> metrics.auc(fpr, tpr) 0.75
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oddt.metrics.
roc_auc
(y_true, y_score, pos_label=None, ascending_score=True)[source]¶ Computes ROC AUC score
Parameters: y_true : array, shape=[n_samples]
True binary labels, in range {0,1} or {-1,1}. If positive label is different than 1, it must be explicitly defined.
- y_score : array, shape=[n_samples]
Scores for tested series of samples
- pos_label: int
Positive label of samples (if other than 1)
- ascending_score: bool (default=True)
Indicates if your score is ascendig
Returns: ef : float
Enrichment Factor for given percenage in range 0:1
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oddt.metrics.
roc_log_auc
(y_true, y_score, pos_label=None, ascending_score=True, log_min=0.001, log_max=1.0)[source]¶ Computes area under semi-log ROC for random distribution.
Parameters: y_true : array, shape=[n_samples]
True binary labels, in range {0,1} or {-1,1}. If positive label is different than 1, it must be explicitly defined.
- y_score : array, shape=[n_samples]
Scores for tested series of samples
- pos_label: int
Positive label of samples (if other than 1)
- ascending_score: bool (default=True)
Indicates if your score is ascendig
- log_min : float (default=0.001)
Minimum logarithm value for estimating AUC
- log_max : float (default=1.)
Maximum logarithm value for estimating AUC.
Returns: auc : float
semi-log ROC AUC
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oddt.metrics.
enrichment_factor
(y_true, y_score, percentage=1, pos_label=None)[source]¶ Computes enrichment factor for given percentage, i.e. EF_1% is enrichment factor for first percent of given samples.
Parameters: y_true : array, shape=[n_samples]
True binary labels, in range {0,1} or {-1,1}. If positive label is different than 1, it must be explicitly defined.
- y_score : array, shape=[n_samples]
Scores for tested series of samples
- percentage : int or float
The percentage for which EF is being calculated
- pos_label: int
Positive label of samples (if other than 1)
Returns: ef : float
Enrichment Factor for given percenage in range 0:1
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oddt.metrics.
random_roc_log_auc
(log_min=0.001, log_max=1.0)[source]¶ Computes area under semi-log ROC for random distribution.
Parameters: log_min : float (default=0.001)
Minimum logarithm value for estimating AUC
- log_max : float (default=1.)
Maximum logarithm value for estimating AUC.
Returns: auc : float
semi-log ROC AUC for random distribution
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oddt.metrics.
rmse
(y_true, y_pred)[source]¶ Compute Root Mean Squared Error (RMSE)
Parameters: y_true : array-like of shape = [n_samples] or [n_samples, n_outputs]
Ground truth (correct) target values.
- y_pred : array-like of shape = [n_samples] or [n_samples, n_outputs]
Estimated target values.
Returns: rmse : float
A positive floating point value (the best value is 0.0).
oddt.spatial module¶
Spatial functions included in ODDT Mainly used by other modules, but can be accessed directly.
-
oddt.spatial.
angle
(p1, p2, p3)[source]¶ Returns an angle from a series of 3 points (point #2 is centroid).Angle is returned in degrees.
Parameters: p1,p2,p3 : numpy arrays, shape = [n_points, n_dimensions]
Triplets of points in n-dimensional space, aligned in rows.
Returns: angles : numpy array, shape = [n_points]
Series of angles in degrees
-
oddt.spatial.
angle_2v
(v1, v2)[source]¶ Returns an angle between two vecors.Angle is returned in degrees.
Parameters: v1,v2 : numpy arrays, shape = [n_vectors, n_dimensions]
Pairs of vectors in n-dimensional space, aligned in rows.
Returns: angles : numpy array, shape = [n_vectors]
Series of angles in degrees
-
oddt.spatial.
dihedral
(p1, p2, p3, p4)[source]¶ Returns an dihedral angle from a series of 4 points. Dihedral is returned in degrees. Function distingishes clockwise and antyclockwise dihedrals.
Parameters: p1,p2,p3,p4 : numpy arrays, shape = [n_points, n_dimensions]
Quadruplets of points in n-dimensional space, aligned in rows.
Returns: angles : numpy array, shape = [n_points]
Series of angles in degrees
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oddt.spatial.
distance
(XA, XB, metric='euclidean', p=2, V=None, VI=None, w=None)¶ Computes distance between each pair of the two collections of inputs.
The following are common calling conventions:
Y = cdist(XA, XB, 'euclidean')
Computes the distance between \(m\) points using Euclidean distance (2-norm) as the distance metric between the points. The points are arranged as \(m\) \(n\)-dimensional row vectors in the matrix X.
Y = cdist(XA, XB, 'minkowski', p)
Computes the distances using the Minkowski distance \(||u-v||_p\) (\(p\)-norm) where \(p \geq 1\).
Y = cdist(XA, XB, 'cityblock')
Computes the city block or Manhattan distance between the points.
Y = cdist(XA, XB, 'seuclidean', V=None)
Computes the standardized Euclidean distance. The standardized Euclidean distance between two n-vectors
u
andv
is\[\sqrt{\sum {(u_i-v_i)^2 / V[x_i]}}.\]V is the variance vector; V[i] is the variance computed over all the i’th components of the points. If not passed, it is automatically computed.
Y = cdist(XA, XB, 'sqeuclidean')
Computes the squared Euclidean distance \(||u-v||_2^2\) between the vectors.
Y = cdist(XA, XB, 'cosine')
Computes the cosine distance between vectors u and v,
\[1 - \frac{u \cdot v} {{||u||}_2 {||v||}_2}\]where \(||*||_2\) is the 2-norm of its argument
*
, and \(u \cdot v\) is the dot product of \(u\) and \(v\).Y = cdist(XA, XB, 'correlation')
Computes the correlation distance between vectors u and v. This is
\[1 - \frac{(u - \bar{u}) \cdot (v - \bar{v})} {{||(u - \bar{u})||}_2 {||(v - \bar{v})||}_2}\]where \(\bar{v}\) is the mean of the elements of vector v, and \(x \cdot y\) is the dot product of \(x\) and \(y\).
Y = cdist(XA, XB, 'hamming')
Computes the normalized Hamming distance, or the proportion of those vector elements between two n-vectors
u
andv
which disagree. To save memory, the matrixX
can be of type boolean.Y = cdist(XA, XB, 'jaccard')
Computes the Jaccard distance between the points. Given two vectors,
u
andv
, the Jaccard distance is the proportion of those elementsu[i]
andv[i]
that disagree where at least one of them is non-zero.Y = cdist(XA, XB, 'chebyshev')
Computes the Chebyshev distance between the points. The Chebyshev distance between two n-vectors
u
andv
is the maximum norm-1 distance between their respective elements. More precisely, the distance is given by\[d(u,v) = \max_i {|u_i-v_i|}.\]Y = cdist(XA, XB, 'canberra')
Computes the Canberra distance between the points. The Canberra distance between two points
u
andv
is\[d(u,v) = \sum_i \frac{|u_i-v_i|} {|u_i|+|v_i|}.\]Y = cdist(XA, XB, 'braycurtis')
Computes the Bray-Curtis distance between the points. The Bray-Curtis distance between two points
u
andv
is\[d(u,v) = \frac{\sum_i (u_i-v_i)} {\sum_i (u_i+v_i)}\]Y = cdist(XA, XB, 'mahalanobis', VI=None)
Computes the Mahalanobis distance between the points. The Mahalanobis distance between two pointsu
andv
is \((u-v)(1/V)(u-v)^T\) where \((1/V)\) (theVI
variable) is the inverse covariance. IfVI
is not None,VI
will be used as the inverse covariance matrix.Y = cdist(XA, XB, 'yule')
Computes the Yule distance between the boolean vectors. (see yule function documentation)Y = cdist(XA, XB, 'matching')
Computes the matching distance between the boolean vectors. (see matching function documentation)Y = cdist(XA, XB, 'dice')
Computes the Dice distance between the boolean vectors. (see dice function documentation)Y = cdist(XA, XB, 'kulsinski')
Computes the Kulsinski distance between the boolean vectors. (see kulsinski function documentation)Y = cdist(XA, XB, 'rogerstanimoto')
Computes the Rogers-Tanimoto distance between the boolean vectors. (see rogerstanimoto function documentation)Y = cdist(XA, XB, 'russellrao')
Computes the Russell-Rao distance between the boolean vectors. (see russellrao function documentation)Y = cdist(XA, XB, 'sokalmichener')
Computes the Sokal-Michener distance between the boolean vectors. (see sokalmichener function documentation)Y = cdist(XA, XB, 'sokalsneath')
Computes the Sokal-Sneath distance between the vectors. (see sokalsneath function documentation)Y = cdist(XA, XB, 'wminkowski')
Computes the weighted Minkowski distance between the vectors. (see wminkowski function documentation)Y = cdist(XA, XB, f)
Computes the distance between all pairs of vectors in X using the user supplied 2-arity function f. For example, Euclidean distance between the vectors could be computed as follows:
dm = cdist(XA, XB, lambda u, v: np.sqrt(((u-v)**2).sum()))
Note that you should avoid passing a reference to one of the distance functions defined in this library. For example,:
dm = cdist(XA, XB, sokalsneath)
would calculate the pair-wise distances between the vectors in X using the Python function sokalsneath. This would result in sokalsneath being called \({n \choose 2}\) times, which is inefficient. Instead, the optimized C version is more efficient, and we call it using the following syntax:
dm = cdist(XA, XB, 'sokalsneath')
Parameters: XA : ndarray
An \(m_A\) by \(n\) array of \(m_A\) original observations in an \(n\)-dimensional space. Inputs are converted to float type.
XB : ndarray
An \(m_B\) by \(n\) array of \(m_B\) original observations in an \(n\)-dimensional space. Inputs are converted to float type.
metric : str or callable, optional
The distance metric to use. If a string, the distance function can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘wminkowski’, ‘yule’.
w : ndarray, optional
The weight vector (for weighted Minkowski).
p : scalar, optional
The p-norm to apply (for Minkowski, weighted and unweighted)
V : ndarray, optional
The variance vector (for standardized Euclidean).
VI : ndarray, optional
The inverse of the covariance matrix (for Mahalanobis).
Returns: Y : ndarray
A \(m_A\) by \(m_B\) distance matrix is returned. For each \(i\) and \(j\), the metric
dist(u=XA[i], v=XB[j])
is computed and stored in the \(ij\) th entry.Raises: ValueError
An exception is thrown if XA and XB do not have the same number of columns.
Examples
Find the Euclidean distances between four 2-D coordinates:
>>> from scipy.spatial import distance >>> coords = [(35.0456, -85.2672), ... (35.1174, -89.9711), ... (35.9728, -83.9422), ... (36.1667, -86.7833)] >>> distance.cdist(coords, coords, 'euclidean') array([[ 0. , 4.7044, 1.6172, 1.8856], [ 4.7044, 0. , 6.0893, 3.3561], [ 1.6172, 6.0893, 0. , 2.8477], [ 1.8856, 3.3561, 2.8477, 0. ]])
Find the Manhattan distance from a 3-D point to the corners of the unit cube:
>>> a = np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], [1, 0, 0], [1, 0, 1], [1, 1, 0], [1, 1, 1]]) >>> b = np.array([[ 0.1, 0.2, 0.4]]) >>> distance.cdist(a, b, 'cityblock') array([[ 0.7], [ 0.9], [ 1.3], [ 1.5], [ 1.5], [ 1.7], [ 2.1], [ 2.3]])
oddt.virtualscreening module¶
ODDT pipeline framework for virtual screening
-
class
oddt.virtualscreening.
virtualscreening
(n_cpu=-1, verbose=False)[source]¶ Virtual Screening pipeline stack
Parameters: n_cpu: int (default=-1)
The number of parallel procesors to use
- verbose: bool (default=False)
Verbosity flag for some methods
Methods
apply_filter
(expression[, filter_type, ...])Filtering method, can use raw expressions (strings to be evaled in if statement, can use oddt.toolkit.Molecule methods, eg. dock
(engine, protein, *args, **kwargs)Docking procedure. fetch
()load_ligands
(fmt, ligands_file, *args, **kwargs)Loads file with ligands. score
(function[, protein])Scoring procedure. write
(fmt, filename[, csv_filename])Outputs molecules to a file write_csv
(csv_filename[, keep_pipe])Outputs molecules to a csv file -
apply_filter
(expression, filter_type='expression', soft_fail=0)[source]¶ Filtering method, can use raw expressions (strings to be evaled in if statement, can use oddt.toolkit.Molecule methods, eg. ‘mol.molwt < 500’) Currently supported presets:
- Lipinski Rule of 5 (‘r5’ or ‘l5’)
- Fragment Rule of 3 (‘r3’)
Parameters: expression: string or list of strings
Expresion(s) to be used while filtering.
- filter_type: ‘expression’ or ‘preset’ (default=’expression’)
Specify filter type: ‘expression’ or ‘preset’. Default strings are treated as expressions.
- soft_fail: int (default=0)
The number of faulures molecule can have to pass filter, aka. soft-fails.
-
dock
(engine, protein, *args, **kwargs)[source]¶ Docking procedure.
Parameters: engine: string
Which docking engine to use.
-
load_ligands
(fmt, ligands_file, *args, **kwargs)[source]¶ Loads file with ligands.
Parameters: file_type: string
Type of molecular file
- ligands_file: string
Path to a file, which is loaded to pipeline
-
score
(function, protein=None, *args, **kwargs)[source]¶ Scoring procedure.
Parameters: function: string
Which scoring function to use.
- protein: oddt.toolkit.Molecule
Default protein to use as reference
Module contents¶
Open Drug Discovery Toolkit¶
Universal and easy to use resource for various drug discovery tasks, ie docking, virutal screening, rescoring.
- toolkit : module,
- Toolkits backend module, currenlty OpenBabel [ob] and RDKit [rdk]. This setting is toolkit-wide, and sets given toolkit as default