oddt package

Submodules

oddt.datasets module

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
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.

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’.

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’.

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’.

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’.

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’.

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

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

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

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’.

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’.

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

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

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:

  1. 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.

  2. Y = cdist(XA, XB, 'minkowski', p)

    Computes the distances using the Minkowski distance \(||u-v||_p\) (\(p\)-norm) where \(p \geq 1\).

  3. Y = cdist(XA, XB, 'cityblock')

    Computes the city block or Manhattan distance between the points.

  4. Y = cdist(XA, XB, 'seuclidean', V=None)

    Computes the standardized Euclidean distance. The standardized Euclidean distance between two n-vectors u and v 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.

  5. Y = cdist(XA, XB, 'sqeuclidean')

    Computes the squared Euclidean distance \(||u-v||_2^2\) between the vectors.

  6. 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 \(v\).

  7. 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 \(y\).

  8. Y = cdist(XA, XB, 'hamming')

    Computes the normalized Hamming distance, or the proportion of those vector elements between two n-vectors u and v which disagree. To save memory, the matrix X can be of type boolean.

  9. Y = cdist(XA, XB, 'jaccard')

    Computes the Jaccard distance between the points. Given two vectors, u and v, the Jaccard distance is the proportion of those elements u[i] and v[i] that disagree where at least one of them is non-zero.

  10. Y = cdist(XA, XB, 'chebyshev')

Computes the Chebyshev distance between the points. The Chebyshev distance between two n-vectors u and v is the maximum norm-1 distance between their respective elements. More precisely, the distance is given by

\[d(u,v) = \max_i {|u_i-v_i|}.\]
  1. Y = cdist(XA, XB, 'canberra')

Computes the Canberra distance between the points. The Canberra distance between two points u and v is

\[d(u,v) = \sum_i \frac{|u_i-v_i|} {|u_i|+|v_i|}.\]
  1. Y = cdist(XA, XB, 'braycurtis')

Computes the Bray-Curtis distance between the points. The Bray-Curtis distance between two points u and v is

\[d(u,v) = \frac{\sum_i (u_i-v_i)} {\sum_i (u_i+v_i)}\]
  1. Y = cdist(XA, XB, 'mahalanobis', VI=None)
Computes the Mahalanobis distance between the points. The Mahalanobis distance between two points u and v is \((u-v)(1/V)(u-v)^T\) where \((1/V)\) (the VI variable) is the inverse covariance. If VI is not None, VI will be used as the inverse covariance matrix.
  1. Y = cdist(XA, XB, 'yule')
Computes the Yule distance between the boolean vectors. (see yule function documentation)
  1. Y = cdist(XA, XB, 'matching')
Computes the matching distance between the boolean vectors. (see matching function documentation)
  1. Y = cdist(XA, XB, 'dice')
Computes the Dice distance between the boolean vectors. (see dice function documentation)
  1. Y = cdist(XA, XB, 'kulsinski')
Computes the Kulsinski distance between the boolean vectors. (see kulsinski function documentation)
  1. Y = cdist(XA, XB, 'rogerstanimoto')
Computes the Rogers-Tanimoto distance between the boolean vectors. (see rogerstanimoto function documentation)
  1. Y = cdist(XA, XB, 'russellrao')
Computes the Russell-Rao distance between the boolean vectors. (see russellrao function documentation)
  1. Y = cdist(XA, XB, 'sokalmichener')
Computes the Sokal-Michener distance between the boolean vectors. (see sokalmichener function documentation)
  1. Y = cdist(XA, XB, 'sokalsneath')
Computes the Sokal-Sneath distance between the vectors. (see sokalsneath function documentation)
  1. Y = cdist(XA, XB, 'wminkowski')
Computes the weighted Minkowski distance between the vectors. (see sokalsneath function documentation)
  1. 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.

XB : ndarray

An \(m_B\) by \(n\) array of \(m_B\) original observations in an \(n\)-dimensional space.

metric : string or function

The distance metric to use. 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

The weight vector (for weighted Minkowski).

p : double

The p-norm to apply (for Minkowski, weighted and unweighted)

V : ndarray

The variance vector (for standardized Euclidean).

VI : ndarray

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:

An exception is thrown if ``XA`` and ``XB`` do not have

the same number of columns.

oddt.virtualscreening module

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