🚧 wip fix api references

Data Classes

============

DeepChem featurizers often transform members into "data classes". These are

classes that hold all the information needed to train a model on that data

point. Models then transform these into the tensors for training in their

:code:`default_generator` methods.

Graph Convolutions

------------------

These classes document the data classes for graph convolutions. We plan to simplify these classes into a joint data representation for all graph convolutions in a future version of DeepChem, so these APIs may not remain stable.

.. autoclass:: deepchem.feat.mol_graphs.ConvMol

  :members:

.. autoclass:: deepchem.feat.mol_graphs.MultiConvMol

  :members:

.. autoclass:: deepchem.feat.mol_graphs.WeaveMol

  :members:

.. autoclass:: deepchem.feat.graph_data.GraphData

  :members:

.. autoclass:: deepchem.feat.graph_data.BatchGraphData

  :members:

Data Loaders

============

Processing large amounts of input data to construct a :code:`dc.data.Dataset` object can require some amount of hacking. To simplify this process for you, you can use the :code:`dc.data.DataLoader` classes. These classes provide utilities for you to load and process large amounts of data.

DataLoader

----------

.. autoclass:: deepchem.data.DataLoader

  :members:

CSVLoader

^^^^^^^^^

.. autoclass:: deepchem.data.CSVLoader

  :members:

UserCSVLoader

^^^^^^^^^^^^^

.. autoclass:: deepchem.data.UserCSVLoader

  :members:

JsonLoader

^^^^^^^^^^

JSON is a flexible file format that is human-readable, lightweight, 

and more compact than other open standard formats like XML. JSON files

are similar to python dictionaries of key-value pairs. All keys must

be strings, but values can be any of (string, number, object, array,

boolean, or null), so the format is more flexible than CSV. JSON is

used for describing structured data and to serialize objects. It is

conveniently used to read/write Pandas dataframes with the

`pandas.read_json` and `pandas.write_json` methods.

.. autoclass:: deepchem.data.JsonLoader

  :members:

FASTALoader

^^^^^^^^^^^

.. autoclass:: deepchem.data.FASTALoader

  :members:

ImageLoader

^^^^^^^^^^^

.. autoclass:: deepchem.data.ImageLoader

  :members:

SDFLoader

^^^^^^^^^

.. autoclass:: deepchem.data.SDFLoader

  :members:

InMemoryLoader

^^^^^^^^^^^^^^

The :code:`dc.data.InMemoryLoader` is designed to facilitate the processing of large datasets where you already hold the raw data in-memory (say in a pandas dataframe).

.. autoclass:: deepchem.data.InMemoryLoader

  :members:

Datasets

========

DeepChem :code:`dc.data.Dataset` objects are one of the core building blocks of DeepChem programs. :code:`Dataset` objects hold representations of data for machine learning and are widely used throughout DeepChem.

Dataset

-------

The :code:`dc.data.Dataset` class is the abstract parent class for all

datasets. This class should never be directly initialized, but

contains a number of useful method implementations.

The goal of the :code:`Dataset` class is to be maximally interoperable with other common representations of machine learning datasets. For this reason we provide interconversion methods mapping from :code:`Dataset` objects to pandas dataframes, tensorflow Datasets, and PyTorch datasets.

.. autoclass:: deepchem.data.Dataset

  :members:

NumpyDataset

------------

The :code:`dc.data.NumpyDataset` class provides an in-memory implementation of the abstract :code:`Dataset` which stores its data in :code:`numpy.ndarray` objects.

.. autoclass:: deepchem.data.NumpyDataset

  :members:

DiskDataset

-----------

The :code:`dc.data.DiskDataset` class allows for the storage of larger

datasets on disk. Each :code:`DiskDataset` is associated with a

directory in which it writes its contents to disk. Note that a

:code:`DiskDataset` can be very large, so some of the utility methods

to access fields of a :code:`Dataset` can be prohibitively expensive.

.. autoclass:: deepchem.data.DiskDataset

  :members:

ImageDataset

------------

The :code:`dc.data.ImageDataset` class is optimized to allow for convenient processing of image based datasets.

.. autoclass:: deepchem.data.ImageDataset

  :members:

Docking

=======

Thanks to advances in biophysics, we are often able to find the

structure of proteins from experimental techniques like Cryo-EM or

X-ray crystallography. These structures can be powerful aides in

designing small molecules. The technique of Molecular docking performs

geometric calculations to find a "binding pose" with the small

molecule interacting with the protein in question in a suitable

binding pocket (that is, a region on the protein which has a groove in

which the small molecule can rest). For more information about

docking, check out the Autodock Vina paper:

Trott, Oleg, and Arthur J. Olson. "AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading." Journal of computational chemistry 31.2 (2010): 455-461.

Binding Pocket Discovery

------------------------

DeepChem has some utilities to help find binding pockets on proteins

automatically. For now, these utilities are simple, but we will

improve these in future versions of DeepChem.

.. autoclass:: deepchem.dock.binding_pocket.BindingPocketFinder

  :members:

.. autoclass:: deepchem.dock.binding_pocket.ConvexHullPocketFinder

  :members:

Pose Generation

---------------

Pose generation is the task of finding a "pose", that is a geometric

configuration of a small molecule interacting with a protein. Pose

generation is a complex process, so for now DeepChem relies on

external software to perform pose generation. This software is invoked

and installed under the hood.

.. autoclass:: deepchem.dock.pose_generation.PoseGenerator

  :members:

.. autoclass:: deepchem.dock.pose_generation.VinaPoseGenerator

  :members:

Docking

-------

The :code:`dc.dock.docking` module provides a generic docking

implementation that depends on provide pose generation and pose

scoring utilities to perform docking. This implementation is generic.

.. autoclass:: deepchem.dock.docking.Docker

  :members:

Pose Scoring

------------

This module contains some utilities for computing docking scoring

functions directly in Python. For now, support for custom pose scoring

is limited.

.. autofunction:: deepchem.dock.pose_scoring.pairwise_distances

.. autofunction:: deepchem.dock.pose_scoring.cutoff_filter

.. autofunction:: deepchem.dock.pose_scoring.vina_nonlinearity

.. autofunction:: deepchem.dock.pose_scoring.vina_repulsion

.. autofunction:: deepchem.dock.pose_scoring.vina_hydrophobic

.. autofunction:: deepchem.dock.pose_scoring.vina_hbond

.. autofunction:: deepchem.dock.pose_scoring.vina_gaussian_first

.. autofunction:: deepchem.dock.pose_scoring.vina_gaussian_second

.. autofunction:: deepchem.dock.pose_scoring.vina_energy_term

Featurizers

===========

DeepChem contains an extensive collection of featurizers. If you

haven't run into this terminology before, a "featurizer" is chunk of

code which transforms raw input data into a processed form suitable

for machine learning. Machine learning methods often need data to be

pre-chewed for them to process. Think of this like a mama penguin

chewing up food so the baby penguin can digest it easily.

Now if you've watched a few introductory deep learning lectures, you

might ask, why do we need something like a featurizer? Isn't part of

the promise of deep learning that we can learn patterns directly from

raw data?

Unfortunately it turns out that deep learning techniques need

featurizers just like normal machine learning methods do. Arguably,

they are less dependent on sophisticated featurizers and more capable

of learning sophisticated patterns from simpler data. But

nevertheless, deep learning systems can't simply chew up raw files.

For this reason, :code:`deepchem` provides an extensive collection of

featurization methods which we will review on this page.

Featurizer

----------

The :code:`dc.feat.Featurizer` class is the abstract parent class for all featurizers.

.. autoclass:: deepchem.feat.Featurizer

  :members:

MolecularFeaturizer

-------------------

Molecular Featurizers are those that work with datasets of molecules.

.. autoclass:: deepchem.feat.MolecularFeaturizer

  :members:

Here are some constants that are used by the graph convolutional featurizers for molecules.

.. autoclass:: deepchem.feat.graph_features.GraphConvConstants

  :members:

  :undoc-members:

There are a number of helper methods used by the graph convolutional classes which we document here.

.. autofunction:: deepchem.feat.graph_features.one_of_k_encoding

.. autofunction:: deepchem.feat.graph_features.one_of_k_encoding_unk

.. autofunction:: deepchem.feat.graph_features.get_intervals

.. autofunction:: deepchem.feat.graph_features.safe_index

.. autofunction:: deepchem.feat.graph_features.get_feature_list

.. autofunction:: deepchem.feat.graph_features.features_to_id

.. autofunction:: deepchem.feat.graph_features.id_to_features

.. autofunction:: deepchem.feat.graph_features.atom_to_id

This function helps compute distances between atoms from a given base atom.

.. autofunction:: deepchem.feat.graph_features.find_distance

This function is important and computes per-atom feature vectors used by

graph convolutional featurizers. 

.. autofunction:: deepchem.feat.graph_features.atom_features

This function computes the bond features used by graph convolutional

featurizers.

.. autofunction:: deepchem.feat.graph_features.bond_features

This function computes atom-atom features (for atom pairs which may not have bonds between them.)

.. autofunction:: deepchem.feat.graph_features.pair_features

ConvMolFeaturizer

^^^^^^^^^^^^^^^^^

.. autoclass:: deepchem.feat.ConvMolFeaturizer

  :members:

WeaveFeaturizer

^^^^^^^^^^^^^^^

.. autoclass:: deepchem.feat.WeaveFeaturizer

  :members:

CircularFingerprint

^^^^^^^^^^^^^^^^^^^

.. autoclass:: deepchem.feat.CircularFingerprint

  :members:

Mol2VecFingerprint

^^^^^^^^^^^^^^^^^^^

.. autoclass:: deepchem.feat.Mol2VecFingerprint

  :members:

RDKitDescriptors

^^^^^^^^^^^^^^^^

.. autoclass:: deepchem.feat.RDKitDescriptors

  :members:

MordredDescriptors

^^^^^^^^^^^^^^^^^^

.. autoclass:: deepchem.feat.MordredDescriptors

  :members:

CoulombMatrix

^^^^^^^^^^^^^

.. autoclass:: deepchem.feat.CoulombMatrix

  :members:

CoulombMatrixEig

^^^^^^^^^^^^^^^^

.. autoclass:: deepchem.feat.CoulombMatrixEig

  :members:

AtomCoordinates

^^^^^^^^^^^^^^^

.. autoclass:: deepchem.feat.AtomicCoordinates

  :members:

SmilesToSeq

^^^^^^^^^^^

.. autoclass:: deepchem.feat.SmilesToSeq

  :members:

SmilesToImage

^^^^^^^^^^^^^

.. autoclass:: deepchem.feat.SmilesToImage

  :members:

OneHotFeaturizer

^^^^^^^^^^^^^^^^

.. autoclass:: deepchem.feat.OneHotFeaturizer

  :members:

ComplexFeaturizer

-----------------

The :code:`dc.feat.ComplexFeaturizer` class is the abstract parent class for all featurizers that work with three dimensional molecular complexes. 

.. autoclass:: deepchem.feat.ComplexFeaturizer

  :members:

RdkitGridFeaturizer

^^^^^^^^^^^^^^^^^^^

.. autoclass:: deepchem.feat.RdkitGridFeaturizer

  :members:

AtomConvFeaturizer

^^^^^^^^^^^^^^^^^^

.. autoclass:: deepchem.feat.NeighborListComplexAtomicCoordinates

  :members:

MaterialStructureFeaturizer

---------------------------

Material Structure Featurizers are those that work with datasets of crystals with

periodic boundary conditions. For inorganic crystal structures, these

featurizers operate on pymatgen.Structure objects, which include a

lattice and 3D coordinates that specify a periodic crystal structure. 

They should be applied on systems that have periodic boundary conditions.

Structure featurizers are not designed to work with molecules. 

.. autoclass:: deepchem.feat.MaterialStructureFeaturizer

  :members:

SineCoulombMatrix

^^^^^^^^^^^^^^^^^

.. autoclass:: deepchem.feat.SineCoulombMatrix

  :members:

CGCNNFeaturizer

^^^^^^^^^^^^^^^^^^^^^^^^

.. autoclass:: deepchem.feat.CGCNNFeaturizer

  :members:

MaterialCompositionFeaturizer

-----------------------------

Material Composition Featurizers are those that work with datasets of crystal

compositions with periodic boundary conditions. 

For inorganic crystal structures, these featurizers operate on chemical

compositions (e.g. "MoS2"). They should be applied on systems that have

periodic boundary conditions. Composition featurizers are not designed 

to work with molecules. 

.. autoclass:: deepchem.feat.MaterialCompositionFeaturizer

  :members:

ElementPropertyFingerprint

^^^^^^^^^^^^^^^^^^^^^^^^^^

.. autoclass:: deepchem.feat.ElementPropertyFingerprint

  :members:

BindingPocketFeaturizer

-----------------------

.. autoclass:: deepchem.feat.BindingPocketFeaturizer

  :members:

UserDefinedFeaturizer

---------------------

.. autoclass:: deepchem.feat.UserDefinedFeaturizer

  :members:

BPSymmetryFunctionInput

-----------------------

.. autoclass:: deepchem.feat.BPSymmetryFunctionInput

  :members:

RawFeaturizer

-------------

.. autoclass:: deepchem.feat.RawFeaturizer

  :members: