Welcome to ODDT’s documentation!

Installation

Requirements

  • Python 2.7.x
  • OpenBabel (2.3.2+) or/and RDKit (2014.03)
  • Numpy (1.8+)
  • Scipy (0.13+)
  • Sklearn (0.13+)
  • ffnet (0.7.1+) only for neural network functionality.
  • joblib (0.8+)

Note

All installation methods assume that one of toolkits is installed. For detailed installation procedure visit toolkit’s website (OpenBabel, RDKit)

Most convenient way of installing ODDT is using PIP. All required python modules will be installed automatically, although toolkits, either OpenBabel (pip install openbabel) or RDKit need to be installed manually

pip install oddt

If you want to install cutting edge version (master branch from GitHub) of ODDT also using PIP

pip install git+https://github.com/oddt/oddt.git@master

Finally you can install ODDT straight from the source

wget https://github.com/oddt/oddt/archive/0.1.1.tar.gz
tar zxvf 0.1.1.tar.gz
cd oddt-0.1.1/
python setup.py install

Common installation problems

ffnet requires numpy.distutils during installation, and you are trying to install ffnet without numpy. You have to install numpy first.

pip install numpy

Then you can install ODDT

pip install oddt

Usage Instructions

You can use any supported toolkit united under common API (for reference see Pybel or Cinfony). All methods and software which based on Pybel/Cinfony should be drop in compatible with ODDT toolkits. In contrast to it’s predecessors, which were aimed to have minimalistic API, ODDT introduces extended methods and additional handles. This extensions allow to use toolkits at all it’s grace and some features may be backported from others to introduce missing functionalities. To name a few:

  • coordinates are returned as Numpy Arrays
  • atoms and residues methods of Molecule class are lazy, ie. not returning a list of pointers, rather an object which allows indexing and iterating through atoms/residues
  • Bond object (similar to Atom)
  • atom_dict, ring_dict, res_dict - comprehensive Numpy Arrays containing common information about given entity, particularly useful for high performance computing, ie. interactions, scoring etc.
  • lazy Molecule (asynchronous), which is not converted to an object in reading phase, rather passed as a string and read in when underlying object is called
  • pickling introduced for Pybel Molecule (internally saved to mol2 string)

Atom, residues, bonds iteration

One of the most common operation would be iterating through molecules atoms

mol = oddt.toolkit.readstring(‘smi’, ‘c1cccc1’)
for atom in mol:
    print atom.idx

Note

mol.atoms, returns an object (AtomStack) which can be access via indexes or iterated

Iterating over residues is also very convenient, especially for proteins

for res in mol.residues:
    print res.name

Additionally residues can fetch atoms belonging to them:

for res in mol.residues:
    for atom in res:
        print atom.idx

Bonds are also iterable, similar to residues:

for bond in mol.bonds:
    print bond.order
    for atom in bond:
        print atom.idx

Reading molecules

Reading molecules is mostly identical to Pybel.

Reading from file

for mol in oddt.toolkit.readfile(‘smi’, ‘test.smi’):
    print mol.title

Reading from string

mol = oddt.toolkit.readstring(‘smi’, ‘c1ccccc1 benzene’):
    print mol.title

Note

You can force molecules to be read in asynchronously, aka “lazy molecules”. Current default is not to produce lazy molecules due to OpenBabel’s Memory Leaks in OBConverter. Main advantage of lazy molecules is using them in multiprocessing, then conversion is spreaded on all jobs.

Reading molecules from file in asynchronous manner

for mol in oddt.toolkit.readfile(‘smi’, ‘test.smi’, lazy=True):
    pass

This example will execute instantaneously, since no molecules were evaluated.

Numpy Dictionaries - store your molecule as an uniform structure

Most important and handy property of Molecule in ODDT are Numpy dictionaries containing most properties of supplied molecule. Some of them are straightforward, other require some calculation, ie. atom features. Dictionaries are provided for major entities of molecule: atoms, bonds, residues and rings. It was primarily used for interactions calculations, although it is applicable for any other calculation. The main benefit is marvelous Numpy broadcasting and subsetting.

Each dictionary is defined as a format in Numpy.

atom_dict

Atom basic information

  • ‘coords‘, type: float32, shape: (3) - atom coordinates
  • ‘charge‘, type: float32 - atom’s charge
  • ‘atomicnum‘, type: int8 - atomic number
  • ‘*atomtype’, type: a4 - Sybyl atom’s type
  • ‘hybridization‘, type: int8 - atoms hybrydization
  • ‘neighbors‘, type: float32, shape: (4,3) - coordinates of non-H neighbors coordinates for angles (max of 4 neighbors should be enough)

Residue information for current atom

  • ‘resid‘, type: int16 - residue ID
  • ‘resname‘, type: a3 - Residue name (3 letters)
  • ‘isbackbone‘, type: bool - is atom part of backbone

Atom properties

  • ‘isacceptor‘, type: bool - is atom H-bond acceptor
  • ‘isdonor‘, type: bool - is atom H-bond donor
  • ‘isdonorh‘, type: bool - is atom H-bond donor Hydrogen
  • ‘ismetal‘, type: bool - is atom a metal
  • ‘ishydrophobe‘, type: bool - is atom hydrophobic
  • ‘isaromatic‘, type: bool - is atom aromatic
  • ‘isminus‘, type: bool - is atom negatively charged/chargable
  • ‘isplus‘, type: bool - is atom positively charged/chargable
  • ‘ishalogen‘, type: bool - is atom a halogen

Secondary structure

  • ‘isalpha‘, type: bool - is atom a part of alpha helix
  • ‘isbeta‘, type: bool' - is atom a part of beta strand

ring_dict

  • ‘centroid‘, type: float32, shape: 3 - coordinates of ring’s centroid
  • ‘vector‘, type: float32, shape: 3 - normal vector for ring
  • ‘isalpha‘, type: bool - is ring a part of alpha helix
  • ‘isbeta‘, type: bool' - is ring a part of beta strand

res_dict

  • ‘id‘, type: int16 - residue ID
  • ‘resname‘, type: a3 - Residue name (3 letters)
  • ‘N‘, type: float32, shape: 3 - cordinates of backbone N atom
  • ‘CA‘, type: float32, shape: 3 - cordinates of backbone CA atom
  • ‘C‘, type: float32, shape: 3 - cordinates of backbone C atom
  • ‘isalpha‘, type: bool - is residue a part of alpha helix
  • ‘isbeta‘, type: bool' - is residue a part of beta strand

Note

All aforementioned dictionaries are generated “on demand”, and are cached for molecule, thus can be shared between calculations. Caching of dictionaries brings incredible performance gain, since in some applications their generation is the major time consuming task.

Get all acceptor atoms:

mol.atom_dict[‘is_acceptor’]

References

To be announced.