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[ Module Hierarchy | Class Hierarchy ]

# Module Hierarchy

- Chem: A module for molecules and stuff...
  - Chem.AllChem: Import all RDKit chemistry modules...
  - Chem.AtomPairs
    - Chem.AtomPairs.Pairs: Contains an implementation of Atom-pair fingerprints, as described in: R.E.
    - Chem.AtomPairs.Torsions: Contains an implementation of Topological-torsion fingerprints, as described in: R.
    - Chem.AtomPairs.Utils
  - Chem.AvailDescriptors: constructs the list of available descriptors
  - Chem.BuildFragmentCatalog: command line utility for working with FragmentCatalogs (CASE-type analysis) **Usage** BuildFragmentCatalog [optional args] <filename> filename, the name of a delimited text file containing InData, is required for some modes of operation (see below) **Command Line Arguments** - -n *maxNumMols*: specify the maximum number of molecules to be processed - -b: build the catalog and OnBitLists *requires InData* - -s: score compounds *requires InData and a Catalog, can use OnBitLists* - -g: calculate info gains *requires Scores* - -d: show details about high-ranking fragments *requires a Catalog and Gains* - --catalog=*filename*: filename with the pickled catalog.
  - Chem.ChemUtils
    - Chem.ChemUtils.AlignDepict
    - Chem.ChemUtils.BulkTester
    - Chem.ChemUtils.SDFToCSV
    - Chem.ChemUtils.TemplateExpand
  - Chem.ChemicalFeatures
  - Chem.ChemicalForceFields
  - Chem.Crippen: Atom-based calculation of LogP and MR using Crippen's approach Reference: S.
  - Chem.Descriptors
  - Chem.Draw
    - Chem.Draw.DrawUtils
    - Chem.Draw.MolDrawing
    - Chem.Draw.aggCanvas
    - Chem.Draw.mplCanvas
    - Chem.Draw.spingCanvas
  - Chem.EState: A module for Kier and Hall's EState Descriptors Unless otherwise noted, all definitions here can be found in: L.B.
    - Chem.EState.AtomTypes: contains SMARTS definitions and calculators for EState atom types...
    - Chem.EState.EState: Basic EState definitions
    - Chem.EState.EState_VSA: Hybrid EState-VSA descriptors (like the MOE VSA descriptors)
    - Chem.EState.Fingerprinter: EState fingerprinting
  - Chem.FastSDMolSupplier
  - Chem.FeatFinderCLI
  - Chem.FeatMaps
    - Chem.FeatMaps.FeatMapParser
    - Chem.FeatMaps.FeatMapPoint
    - Chem.FeatMaps.FeatMapUtils
    - Chem.FeatMaps.FeatMaps
  - Chem.Features
    - Chem.Features.FeatDirUtilsRD
    - Chem.Features.ShowFeats
  - Chem.Fingerprints
    - Chem.Fingerprints.ClusterMols: utility functionality for clustering molecules using fingerprints...
    - Chem.Fingerprints.DbFpSupplier: Supplies a class for working with fingerprints from databases...
    - Chem.Fingerprints.FingerprintMols: utility functionality for fingerprinting sets of molecules...
    - Chem.Fingerprints.MolSimilarity: utility functionality for molecular similarity...
    - Chem.Fingerprints.SimilarityScreener: class definitions for similarity screening...
  - Chem.FragmentCatalog
  - Chem.FragmentMatcher: exposes a class for matching fragments of molecules.
  - Chem.Fragments: functions to match a bunch of fragment descriptors from a file No user-servicable parts inside.
  - Chem.GraphDescriptors: Calculation of topological/topochemical descriptors.
  - Chem.Graphs: Python functions for manipulating molecular graphs In theory much of the functionality in here should be migrating into the C/C++ codebase.
  - Chem.Lipinski: Calculation of Lipinski parameters for molecules
  - Chem.MACCSkeys: SMARTS definitions for the publically available MACCS keys and a MACCS fingerprinter I compared the MACCS fingerprints generated here with those from two other packages (not MDL, unfortunately).
  - Chem.MolCatalog
  - Chem.MolDb
    - Chem.MolDb.Loader
  - Chem.MolSurf: Exposes functionality for MOE-like approximate molecular surface area descriptors.
  - Chem.PeriodicTable': periodic table data, **obsolete**...
  - Chem.Pharm2D: module with functionality for 2D pharmacophores
    - Chem.Pharm2D.Generate: generation of 2D pharmacophores **Notes** - The terminology for this gets a bit rocky, so here's a glossary of what terms used here mean: 1) *N-point pharmacophore* a combination of N features along with distances betwen them.
    - Chem.Pharm2D.Gobbi_Pharm2D: Definitions for 2D Pharmacophores from: Gobbi and Poppinger, Biotech.
    - Chem.Pharm2D.LazyGenerator: lazy generator of 2D pharmacophore signature data
    - Chem.Pharm2D.Matcher: functionality for finding pharmacophore matches in molecules See Docs/Chem/Pharm2D.triangles.jpg for an illustration of the way pharmacophores are broken into triangles and labelled.
    - Chem.Pharm2D.SigFactory: contains factory class for producing signatures
    - Chem.Pharm2D.Signature: data structures for holding 2D pharmacophore signatures See Docs/Chem/Pharm2D.triangles.jpg for an illustration of the way pharmacophores are broken into triangles and labelled.
    - Chem.Pharm2D.Utils: utility functionality for the 2D pharmacophores code See Docs/Chem/Pharm2D.triangles.jpg for an illustration of the way pharmacophores are broken into triangles and labelled.
  - Chem.Pharm3D
    - Chem.Pharm3D.EmbedLib
    - Chem.Pharm3D.ExcludedVolume
    - Chem.Pharm3D.Pharmacophore
  - Chem.PyMol: uses pymol to interact with molecules
  - Chem.Randomize
  - Chem.Recap: Implementation of the RECAP algorithm from Lewell et al.
  - Chem.SATIS: Functionality for SATIS typing atoms
  - Chem.ShowMols
  - Chem.Subshape
    - Chem.Subshape.BuilderUtils
    - Chem.Subshape.SubshapeAligner
    - Chem.Subshape.SubshapeBuilder
    - Chem.Subshape.SubshapeObjects
  - Chem.Suppliers
    - Chem.Suppliers.DbMolSupplier: Supplies a class for working with molecules from databases...
    - Chem.Suppliers.MolSupplier: Supplies an abstract class for working with sequences of molecules
  - Chem.TemplateAlign
  - Chem.rdChemReactions: Module containing classes and functions for working with chemical reactions.
  - Chem.rdChemicalFeatures: Module containing free chemical feature functionality These are feature that are not associated with molecules.
  - Chem.rdDepictor: Module containing the functionality to compute 2D coordinates for a molecule
  - Chem.rdDistGeom: Module containing functions to compute atomic coordinates in 3D using distance geometry
  - Chem.rdForceFieldHelpers: Module containing functions to handle molecular force fields (currently UFF)
  - Chem.rdMolAlign: Module containing functions to align a molecule to a second molecule
  - Chem.rdMolCatalog
  - Chem.rdMolChemicalFeatures: Module containing from chemical feature and functions to generate the
  - Chem.rdMolDescriptors: Module containing functions to compute molecular descriptors
  - Chem.rdMolTransforms: Module containing functions to perform 3D operations like rotate and translate conformations
  - Chem.rdPartialCharges: Module containing functions to set partial charges - currently Gasteiger Charges
  - Chem.rdShapeHelpers: Module containing functions to encode ans compre the shapes of molecules
  - Chem.rdchem: Module containing the core chemistry functionality of the RDKit
  - Chem.rdfragcatalog
  - Chem.rdmolfiles: Module containing RDKit functionality for working with molecular file formats.
  - Chem.rdmolops: Module containing RDKit functionality for manipulating and querying molecules.
- DataManip
  - DataManip.Metric
    - DataManip.Metric.rdMetricMatrixCalc: Module containing the calculator for metric matrix calculation, e.g.
- DataStructs: Module containing an assortment of functionality for basic data structures.
  - DataStructs.BitEnsemble: #DOC
  - DataStructs.BitEnsembleDb: This functionality gets mixed into the BitEnsemble class
  - DataStructs.BitUtils
  - DataStructs.BitVect: This has all be re-implemented in the C++ code
  - DataStructs.HierarchyVis: functionality for drawing hierarchical catalogs on sping...
  - DataStructs.LazySignature
  - DataStructs.SparseIntVect
  - DataStructs.TopNContainer
  - DataStructs.VectCollection
  - DataStructs.cDataStructs: Module containing an assortment of functionality for basic data structures.
- Dbase: a module for interacting with databases
  - Dbase.DbConnection: defines class _DbConnect_, for abstracting connections to databases
  - Dbase.DbInfo
  - Dbase.DbModule
  - Dbase.DbReport
  - Dbase.DbResultSet: defines class _DbResultSet_ for lazy interactions with Db query results **Note** this uses the Python iterator interface, so you'll need python 2.2 or above.
  - Dbase.DbUtils: a set of functions for interacting with databases...
  - Dbase.Pubmed
    - Dbase.Pubmed.PubmedUtils
    - Dbase.Pubmed.QueryParams
    - Dbase.Pubmed.Records
    - Dbase.Pubmed.Searches: Tools for doing PubMed searches and processing the results NOTE: much of the example code in the documentation here uses XML files from the test_data directory in order to avoid having to call out to PubMed itself.
  - Dbase.StorageUtils: Various storage (molecular and otherwise) functionality
- DistanceGeometry
  - DistanceGeometry.DistGeom: Module containing functions for basic distance geometry operations
- ForceField
  - ForceField.rdForceField: Exposes the ForceField class
- Geometry: A module for Geometry stuff ...
  - Geometry.rdGeometry: Module containing geometry objects like points, grids etc
- Logger
  - Logger.Logger: Provides a Logger class which can be wrapped around another python class to log method calls and attribute changes.
  - Logger.SpingDemo: demo code for the Logger class
  - Logger.demo
- ML: module containing machine learning code
  - ML.AnalyzeComposite: command line utility to report on the contributions of descriptors to tree-based composite models Usage: AnalyzeComposite [optional args] <models> <models>: file name(s) of pickled composite model(s) (this is the name of the db table if using a database) Optional Arguments: -n number: the number of levels of each model to consider -d dbname: the database from which to read the models -N Note: the note string to search for to pull models from the database -X: Send the results to Excel.
  - ML.BuildComposite: command line utility for building composite models #DOC **Usage** BuildComposite [optional args] filename Unless indicated otherwise (via command line arguments), _filename_ is a QDAT file.
  - ML.Cluster
    - ML.Cluster.Butina: Implementation of the clustering algorithm published in:...
    - ML.Cluster.ClusterUtils: utility functions for clustering
    - ML.Cluster.ClusterVis: Cluster tree visualization using Sping
    - ML.Cluster.Clustering
    - ML.Cluster.Clusters: contains the Cluster class for representing hierarchical cluster trees
    - ML.Cluster.Murtagh: Interface to the C++ Murtagh hierarchic clustering code
    - ML.Cluster.Resemblance: code for dealing with resemblance (metric) matrices...
    - ML.Cluster.Standardize: contains code for standardization of data matrices for clustering
  - ML.Composite
    - ML.Composite.AdjustComposite: functionality to allow adjusting composite model contents
    - ML.Composite.BayesComposite: code for dealing with Bayesian composite models For a model to be useable here, it should support the following API: - _ClassifyExample(example)_, returns a classification Other compatibility notes: 1) To use _Composite.Grow_ there must be some kind of builder functionality which returns a 2-tuple containing (model,percent accuracy).
    - ML.Composite.Composite: code for dealing with composite models For a model to be useable here, it should support the following API: - _ClassifyExample(example)_, returns a classification Other compatibility notes: 1) To use _Composite.Grow_ there must be some kind of builder functionality which returns a 2-tuple containing (model,percent accuracy).
  - ML.CompositeRun: contains a class to store parameters for and results from...
  - ML.Data
    - ML.Data.DataUtils: Utilities for data manipulation **FILE FORMATS:** - *.qdat files* contain quantized data suitable for feeding to learning algorithms.
    - ML.Data.FindQuantBounds
    - ML.Data.MLData: classes to be used to help work with data sets
    - ML.Data.Quantize: Automatic search for quantization bounds This uses the expected informational gain to determine where quantization bounds should lie.
    - ML.Data.SplitData
    - ML.Data.Stats: various statistical operations on data
    - ML.Data.Transforms
  - ML.DecTree: Here we're implementing the Decision Tree stuff found in Chapter 3 of Tom Mitchell's Machine Learning Book.
    - ML.DecTree.BuildQuantTree:
    - ML.DecTree.BuildSigTree:
    - ML.DecTree.CrossValidate: handles doing cross validation with decision trees This is, perhaps, a little misleading.
    - ML.DecTree.DecTree: Defines the class _DecTreeNode_, used to represent decision trees...
    - ML.DecTree.Forest: code for dealing with forests (collections) of decision trees **NOTE** This code should be obsolete now that ML.Composite.Composite is up and running.
    - ML.DecTree.ID3: ID3 Decision Trees...
    - ML.DecTree.PruneTree: Contains functionality for doing tree pruning
    - ML.DecTree.QuantTree: Defines the class _QuantTreeNode_, used to represent decision trees with automatic...
    - ML.DecTree.SigTree: Defines the class SigTreeNode, used to represent trees that use signatures (bit vectors) to represent data.
    - ML.DecTree.Tree: Implements a class used to represent N-ary trees
    - ML.DecTree.TreeUtils: Utilities for working with trees
    - ML.DecTree.TreeVis: functionality for drawing trees on sping canvases
  - ML.Descriptors
    - ML.Descriptors.CompoundDescriptors: descriptor calculator for compounds defined by a composition alone...
    - ML.Descriptors.Descriptors: Various bits and pieces for calculating descriptors
    - ML.Descriptors.MoleculeDescriptors: Various bits and pieces for calculating Molecular descriptors
    - ML.Descriptors.Parser: The "parser" for compound descriptors.
  - ML.EnrichPlot: Command line tool to construct an enrichment plot from saved composite models Usage: EnrichPlot [optional args] -d dbname -t tablename <models> Required Arguments: -d "dbName": the name of the database for screening -t "tablename": provide the name of the table with the data to be screened <models>: file name(s) of pickled composite model(s).
  - ML.FeatureSelect
    - ML.FeatureSelect.CMIM: for the moment this is using Francois Fleuret's cmim library to do the feature selection Reference: F.
    - ML.FeatureSelect.rdFeatSelect: Module containing functions for feature selection
  - ML.GrowComposite: command line utility for growing composite models **Usage** _GrowComposite [optional args] filename_ **Command Line Arguments** - -n *count*: number of new models to build - -C *pickle file name*: name of file containing composite upon which to build.
  - ML.InfoTheory: Information Theory functionality
    - ML.InfoTheory.BitClusterer
    - ML.InfoTheory.BitRank: Functionality for ranking bits using info gains **Definitions used in this module** - *sequence*: an object capable of containing other objects which supports __getitem__() and __len__().
    - ML.InfoTheory.entropy: Informational Entropy functions...
    - ML.InfoTheory.rdInfoTheory: Module containing bunch of functions for information metrics and a ranker to rank bits
  - ML.KNN: Here is the implementation for K-nearest neighbors
    - ML.KNN.CrossValidate: handles doing cross validation with k-nearest neighbors model...
    - ML.KNN.DistFunctions
    - ML.KNN.KNNClassificationModel: Define the class _KNNClassificationModel_, used to represent a k-nearest neighbhors classification model...
    - ML.KNN.KNNModel: Define the class _KNNModel_, used to represent a k-nearest neighbhors model
    - ML.KNN.KNNRegressionModel: Define the class _KNNRegressionModel_, used to represent a k-nearest neighbhors...
  - ML.MLUtils
    - ML.MLUtils.VoteImg: functionality for generating an image showing the results of a composite model voting on a data set...
  - ML.MatOps: Matrix operations which may or may not come in handy some day...
  - ML.ModelPackage
    - ML.ModelPackage.Packager
  - ML.NaiveBayes: An implementation of the Naive Bayes Classifier
    - ML.NaiveBayes.ClassificationModel: Defines Naive Baysean classification model...
    - ML.NaiveBayes.CrossValidate: handles doing cross validation with naive bayes models...
  - ML.Neural
    - ML.Neural.ActFuncs: Activation functions for neural network nodes Activation functions should implement the following API: - _Eval(input)_: returns the value of the function at a given point - _Deriv(input)_: returns the derivative of the function at a given point The current Backprop implementation also requires: - _DerivFromVal(val)_: returns the derivative of the function when its value is val In all cases _input_ is a float as is the value returned.
    - ML.Neural.CrossValidate: handles doing cross validation with neural nets This is, perhaps, a little misleading.
    - ML.Neural.NetNode: Contains the class _NetNode_ which is used to represent nodes in neural nets **Network Architecture:** A tacit assumption in all of this stuff is that we're dealing with feedforward networks.
    - ML.Neural.Network: Contains the class _Network_ which is used to represent neural nets **Network Architecture:** A tacit assumption in all of this stuff is that we're dealing with feedforward networks.
    - ML.Neural.Trainers: Training algorithms for feed-forward neural nets Unless noted otherwise, algorithms and notation are taken from: "Artificial Neural Networks: Theory and Applications", Dan W.
  - ML.SLT
    - ML.SLT.Risk: code for calculating empirical risk
  - ML.ScreenComposite: command line utility for screening composite models **Usage** _ScreenComposite [optional args] modelfile(s) datafile_ Unless indicated otherwise (via command line arguments), _modelfile_ is a file containing a pickled composite model and _filename_ is a QDAT file.
- Numerics: A module for Numerics stuff ...
  - Numerics.rdAlignment: Module containing functions to align pairs of points in 3D
- PySVD
- RDConfig: Configuration for Rational Discovery's Python code
- RDLogger
- RDRandom: making random numbers consistent so we get good regressions
- SimDivFilters
  - SimDivFilters.SimilarityPickers
  - SimDivFilters.rdSimDivPickers: Module containing the diversity and similarity pickers
- VLib
  - VLib.Filter
  - VLib.Node
  - VLib.NodeLib
    - VLib.NodeLib.DbMolSupply
    - VLib.NodeLib.DbPickleSupplier
    - VLib.NodeLib.SDSupply
    - VLib.NodeLib.SmartsMolFilter
    - VLib.NodeLib.SmartsRemover
    - VLib.NodeLib.SmilesDupeFilter
    - VLib.NodeLib.SmilesOutput
    - VLib.NodeLib.SmilesSupply
    - VLib.NodeLib.demo
  - VLib.Output
  - VLib.Supply
  - VLib.Transform
- rdBase: Module containing basic definitions for wrapped C++ code No user-serviceable parts inside.
- re: Support for regular expressions (RE).