# The RDKit Book

## Misc Cheminformatics Topics

### Aromaticity

Aromaticity is one of those unpleasant topics that is simultaneously simple and impossibly complicated. Since neither experimental nor theoretical chemists can agree with each other about a definition, it’s necessary to pick something arbitrary and stick to it. This is the approach taken in the RDKit.

Instead of using patterns to match known aromatic systems, the aromaticity perception code in the RDKit uses a set of rules. The rules are relatively straightforward.

Aromaticity is a property of atoms and bonds in rings. An aromatic bond must be between aromatic atoms, but a bond between aromatic atoms does not need to be aromatic.

For example the fusing bonds here are not considered to be aromatic by the RDKit:

    >>> from rdkit import Chem
    >>> m = Chem.MolFromSmiles('C1=CC2=C(C=C1)C1=CC=CC=C21')
    >>> m.GetAtomWithIdx(3).GetIsAromatic()
    True
    >>> m.GetAtomWithIdx(6).GetIsAromatic()
    True
    >>> m.GetBondBetweenAtoms(3,6).GetIsAromatic()
    False

The RDKit supports a number of different aromaticity models and allows the user to define their own by providing a function that assigns aromaticity.

#### The RDKit Aromaticity Model

A ring, or fused ring system, is considered to be aromatic if it obeys the 4N+2 rule. Contributions to the electron count are determined by atom type and environment. Some examples:

Fragment | Number of pi electrons
c(a)a | 1
n(a)a | 1
An(a)a | 2
o(a)a | 2
s(a)a | 2
se(a)a | 2
te(a)a | 2
O=c(a)a | 0
N=c(a)a | 0
*(a)a | 0, 1, or 2

Notation a: any aromatic atom; A: any atom, include H; *: a dummy atom

Notice that exocyclic bonds to electronegative atoms “steal” the valence electron from the ring atom and that dummy atoms contribute whatever count is necessary to make the ring aromatic.

The use of fused rings for aromaticity can lead to situations where individual rings are not aromatic, but the fused system is. An example of this is azulene:

An extreme example, demonstrating both fused rings and the influence of exocyclic double bonds:

    >>> m=Chem.MolFromSmiles('O=C1C=CC(=O)C2=C1OC=CO2')
    >>> m.GetAtomWithIdx(6).GetIsAromatic()
    True
    >>> m.GetAtomWithIdx(7).GetIsAromatic()
    True
    >>> m.GetBondBetweenAtoms(6,7).GetIsAromatic()
    False

A special case, heteroatoms with radicals are not considered candidates for aromaticity:

    >>> m = Chem.MolFromSmiles('C1=C[N]C=C1')
    >>> m.GetAtomWithIdx(0).GetIsAromatic()
    False
    >>> m.GetAtomWithIdx(2).GetIsAromatic()
    False
    >>> m.GetAtomWithIdx(2).GetNumRadicalElectrons()
    1

Charged carbons with radicals are also not considered:

    >>> m = Chem.MolFromSmiles('C1=CC=CC=C[C+]1')
    >>> m.GetAtomWithIdx(0).GetIsAromatic()
    False
    >>> m.GetAtomWithIdx(6).GetIsAromatic()
    False
    >>> m.GetAtomWithIdx(6).GetFormalCharge()
    1
    >>> m.GetAtomWithIdx(6).GetNumRadicalElectrons()
    1

Neutral carbons with radicals, however, are still considered:

    >>> m = Chem.MolFromSmiles('C1=[C]NC=C1')
    >>> m.GetAtomWithIdx(0).GetIsAromatic()
    True
    >>> m.GetAtomWithIdx(1).GetIsAromatic()
    True
    >>> m.GetAtomWithIdx(1).GetNumRadicalElectrons()
    1

#### The Simple Aromaticity Model

This one is quite simple: only five- and six-membered simple rings are considered candidates for aromaticity. The same electron-contribution counts listed above are used.

#### The MDL Aromaticity Model

This isn’t well documented (at least not publicly), so we tried to reproduce what’s provided in the oechem documentation (https://docs.eyesopen.com/toolkits/python/oechemtk/aromaticity.html)

- fused rings (i.e. azulene) can be aromatic
- five-membered rings are not aromatic (though they can be part of fused aromatic systems)
- only C and N can be aromatic
- only one electron donors are accepted
- atoms with exocyclic double bonds are not aromatic

Note: For reasons of computational expediency, aromaticity perception is only done for fused-ring systems where all members are at most 24 atoms in size.

### SMILES Support and Extensions

The RDKit covers all of the standard features of Daylight SMILES [2] as well as some useful extensions.

Here’s the (likely partial) list of extensions:

#### Aromaticity

`te` (aromatic Te) is accepted. Here is an example with tellurophene-2-carboxylic acid:

    >>> m = Chem.MolFromSmiles('OC(=O)c1[te]ccc1')
    >>> m.GetAtomWithIdx(4).GetIsAromatic()
    True

#### Dative bonds

`<-` and `->` create a dative bond between the atoms, direction does matter.

Here’s an example of a bipy-copper complex:

    >>> bipycu = Chem.MolFromSmiles('c1cccn->2c1-c1n->3cccc1.[Cu]23(Cl)Cl')
    >>> bipycu.GetBondBetweenAtoms(4,12).GetBondType()
    rdkit.Chem.rdchem.BondType.DATIVE
    >>> Chem.MolToSmiles(bipycu)
    'Cl[Cu]1(Cl)<-n2ccccc2-c2ccccn->12'

Dative bonds have the special characteristic that they don’t affect the valence on the start atom, but do affect the end atom. So in this case, the N atoms involved in the dative bond have the valence of 3 that we expect from bipy, while the Cu has a valence of 4:

    >>> bipycu.GetAtomWithIdx(4).GetTotalValence()
    3
    >>> bipycu.GetAtomWithIdx(12).GetTotalValence()
    4

#### Ring closures

`%(N)` notation is supported for ring closures, where N is a single digit `%(N)` up to five digits `%(NNNNN)`. Here is an example:

    >>> m = Chem.MolFromSmiles('C%(1000)OC%(1000)')
    >>> m.GetAtomWithIdx(0).IsInRing()
    True
    >>> m.GetAtomWithIdx(2).IsInRing()
    True

#### Specifying atoms by atomic number

The `[#6]` construct from SMARTS is supported in SMILES.

#### CXSMILES extensions

The RDKit supports parsing and writing a subset of the extended SMILES functionality introduced by ChemAxon [4].

The features which are parsed include:

- atomic coordinates
- atomic values
- atomic labels
- atomic properties
- coordinate bonds (these are translated into double bonds)
- radicals
- enhanced stereo (these are converted into `StereoGroups`)

The features which are written by `rdkit.Chem.rdmolfiles.MolToCXSmiles()` (note the specialized writer function) include:

- atomic coordinates
- atomic values
- atomic labels
- atomic properties
- radicals
- enhanced stereo

    >>> m = Chem.MolFromSmiles('OC')
    >>> m.GetAtomWithIdx(0).SetProp('p1','2')
    >>> m.GetAtomWithIdx(1).SetProp('p1','5')
    >>> m.GetAtomWithIdx(1).SetProp('p2','A1')
    >>> m.GetAtomWithIdx(0).SetProp('atomLabel','O1')
    >>> m.GetAtomWithIdx(1).SetProp('atomLabel','C2')
    >>> Chem.MolToCXSmiles(m)
    'CO |$C2;O1$,atomProp:0.p1.5:0.p2.A1:1.p1.2|'

### SMARTS Support and Extensions

The RDKit covers most of the standard features of Daylight SMARTS [3] as well as some useful extensions.

Here’s the (hopefully complete) list of SMARTS features that are not supported:

- Non-tetrahedral chiral classes
- the `@?` operator
- explicit atomic masses (though isotope queries are supported)
- component level grouping requiring matches in different components, i.e. `(C).(C)`

Here’s the (likely partial) list of extensions:

#### Hybridization queries

> - `^0` matches S hybridized atoms
> - `^1` matches SP hybridized atoms
> - `^2` matches SP2 hybridized atoms
> - `^3` matches SP3 hybridized atoms
> - `^4` matches SP3D hybridized atoms
> - `^5` matches SP3D2 hybridized atoms

    >> Chem.MolFromSmiles('CC=CF').GetSubstructMatches(Chem.MolFromSmarts('[^2]'))
    ((1,), (2,))

#### Dative bonds

`<-` and `->` match the corresponding dative bonds, direction does matter.

    >>> Chem.MolFromSmiles('C1=CC=CC=N1->[Fe]').GetSubstructMatches(Chem.MolFromSmarts('[#7]->*'))
    ((5, 6),)
    >>> Chem.MolFromSmiles('C1=CC=CC=N1->[Fe]').GetSubstructMatches(Chem.MolFromSmarts('*<-[#7]'))
    ((6, 5),)

#### Heteroatom neighbor queries

> - the atom query `z` matches atoms that have the specified number of heteroatom (i.e. not C or H) neighbors. For example, `z2` would match the second C in `CC(=O)O`.
> - the atom query `Z` matches atoms that have the specified number of aliphatic heteroatom (i.e. not C or H) neighbors.

    >>> Chem.MolFromSmiles('O=C(O)c1nc(O)ccn1').GetSubstructMatches(Chem.MolFromSmarts('[z2]'))
    ((1,), (3,), (5,))
    >>> Chem.MolFromSmiles('O=C(O)c1nc(O)ccn1').GetSubstructMatches(Chem.MolFromSmarts('[Z2]'))
    ((1,),)
    >>> Chem.MolFromSmiles('O=C(O)c1nc(O)ccn1').GetSubstructMatches(Chem.MolFromSmarts('[Z1]'))
    ((5,),)

#### Range queries

Ranges of values can be provided for many query types that expect numeric values. The query types that currently support range queries are: `D`, `h`, `r`, `R`, `v`, `x`, `X`, `z`, `Z`, `+`, `-`

Here are some examples:
  - `D{2-4}` matches atoms that have between 2 and 4 (inclusive) explicit connections.
  - `D{-3}` matches atoms that have less than or equal to 3 explicit connections.
  - `D{2-}` matches atoms that have at least 2 explicit connections.

    >>> Chem.MolFromSmiles('CC(=O)OC').GetSubstructMatches(Chem.MolFromSmarts('[z{1-}]'))
    ((1,), (4,))
    >>> Chem.MolFromSmiles('CC(=O)OC').GetSubstructMatches(Chem.MolFromSmarts('[D{2-3}]'))
    ((1,), (3,))
    >>> Chem.MolFromSmiles('CC(=O)OC.C').GetSubstructMatches(Chem.MolFromSmarts('[D{-2}]'))
    ((0,), (2,), (3,), (4,), (5,))

#### SMARTS Reference

Note that the text versions of the tables below include some backslash characters to escape special characters. This is a wart from the documentation system we are using. Please ignore those characters.

Atoms

Primitive | Property | “Default value” | Range? | Notes
--- | --- | --- | --- | ---
a | “aromatic atom” |  |  |
A | “aliphatic atom” |  |  |
D | “explicit degree” | 1 | Y |
h | “number of implicit hs” | >0 | Y |
H | “total number of Hs” | 1 |  |
r | “in SSSR ring of size” | >0 | Y |
R | “number of SSSR rings” | >0 | Y |
v | “total valence” | 1 | Y |
x | “number of ring bonds” | >0 | Y |
X | “total degree” | 1 | Y |
z | “number of heteroatom neighbors” | >0 | Y | extension
Z | “number of alphatic heteroatom neighbors” | >0 | Y | extension
* | “any atom” |  |  |
+ | “positive charge” | 1 | Y |
++ | “+2 charge” |  |  |
- | “negative charge” | 1 | Y |
– | “-2 charge” |  |  |
^0 | “S hybridized” | n/a | N | extension
^1 | “SP hybridized” | n/a | N | extension
^2 | “SP2 hybridized” | n/a | N | extension
^3 | “SP3 hybridized” | n/a | N | extension
^4 | “SP3D hybridized” | n/a | N | extension
^5 | “SP3D2 hybridized” | n/a | N | extension

Bonds

Primitive | Property | Notes
--- | --- | ---
“” | “single or aromatic” | “unspecified bonds”
- | single |
= | double |
# | triple |
: | aromatic |
~ | “any bond” |
@ | “ring bond” |
/ | “directional” |
\ | “directional” |
-> | “dative right” | extension
<- | “dative left” | extension

### Ring Finding and SSSR

[Section taken from “Getting Started” document]

As others have ranted about with more energy and eloquence than I intend to, the definition of a molecule’s smallest set of smallest rings is not unique. In some high symmetry molecules, a “true” SSSR will give results that are unappealing. For example, the SSSR for cubane only contains 5 rings, even though there are “obviously” 6. This problem can be fixed by implementing a small (instead of smallest) set of smallest rings algorithm that returns symmetric results. This is the approach that we took with the RDKit.

Because it is sometimes useful to be able to count how many SSSR rings are present in the molecule, there is a GetSSSR function, but this only returns the SSSR count, not the potentially non-unique set of rings.

For situations where you just care about knowing whether or not atoms/bonds are in rings, the RDKit provides the function `rdkit.Chem.rdmolops.FastFindRings()`. This does a depth-first traversal of the molecule graph and identifies atoms and bonds that are in rings.

## Chemical Reaction Handling

### Reaction SMARTS

Not SMIRKS [1] , not reaction SMILES [2], derived from SMARTS [3].

The general grammar for a reaction SMARTS is :

    reaction  ::=   reactants ">>" products
    reactants ::=  molecules
    products  ::=   molecules
    molecules ::=  molecule
                   molecules "." molecule
    molecule  ::=   a valid SMARTS string without "." characters
                    "(" a valid SMARTS string without "." characters ")"

#### Some features

Mapped dummy atoms in the product template are replaced by the corresponding atom in the reactant:

    >>> from rdkit.Chem import AllChem
    >>> rxn = AllChem.ReactionFromSmarts('[C:1]=[O,N:2]>>[C:1][*:2]')
    >>> [Chem.MolToSmiles(x,1) for x in rxn.RunReactants((Chem.MolFromSmiles('CC=O'),))[0]]
    ['CCO']
    >>> [Chem.MolToSmiles(x,1) for x in rxn.RunReactants((Chem.MolFromSmiles('CC=N'),))[0]]
    ['CCN']

but unmapped dummy atoms are left as dummies:

    >>> rxn = AllChem.ReactionFromSmarts('[C:1]=[O,N:2]>>*[C:1][*:2]')
    >>> [Chem.MolToSmiles(x,1) for x in rxn.RunReactants((Chem.MolFromSmiles('CC=O'),))[0]]
    ['*C(C)O']

“Any” bonds in the products are replaced by the corresponding bond in the reactant:

    >>> rxn = AllChem.ReactionFromSmarts('[C:1]~[O,N:2]>>*[C:1]~[*:2]')
    >>> [Chem.MolToSmiles(x,1) for x in rxn.RunReactants((Chem.MolFromSmiles('C=O'),))[0]]
    ['*C=O']
    >>> [Chem.MolToSmiles(x,1) for x in rxn.RunReactants((Chem.MolFromSmiles('CO'),))[0]]
    ['*CO']
    >>> [Chem.MolToSmiles(x,1) for x in rxn.RunReactants((Chem.MolFromSmiles('C#N'),))[0]]
    ['*C#N']

Intramolecular reactions can be expressed flexibly by including reactants in parentheses. This is demonstrated in this ring-closing metathesis example [5]:

    >>> rxn = AllChem.ReactionFromSmarts("([C:1]=[C;H2].[C:2]=[C;H2])>>[*:1]=[*:2]")
    >>> m1 = Chem.MolFromSmiles('C=CCOCC=C')
    >>> ps = rxn.RunReactants((m1,))
    >>> Chem.MolToSmiles(ps[0][0])
    'C1=CCOC1'

#### Chirality

This section describes how chirality information in the reaction defition is handled. A consistent example, esterification of secondary alcohols, is used throughout [6].

If no chiral information is present in the reaction definition, the stereochemistry of the reactants is preserved, as is membership in enhanced stereo groups:

    >>> alcohol1 = Chem.MolFromSmiles('CC(CCN)O')
    >>> alcohol2 = Chem.MolFromSmiles('C[C@H](CCN)O')
    >>> alcohol3 = Chem.MolFromSmiles('C[C@@H](CCN)O')
    >>> acid = Chem.MolFromSmiles('CC(=O)O')
    >>> rxn = AllChem.ReactionFromSmarts('[CH1:1][OH:2].[OH][C:3]=[O:4]>>[C:1][O:2][C:3]=[O:4]')
    >>> ps=rxn.RunReactants((alcohol1,acid))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(=O)OC(C)CCN'
    >>> ps=rxn.RunReactants((alcohol2,acid))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(=O)O[C@H](C)CCN'
    >>> ps=rxn.RunReactants((alcohol3,acid))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(=O)O[C@@H](C)CCN'

You get the same result (retention of stereochemistry) if a mapped atom has the same chirality in both reactants and products:

    >>> rxn = AllChem.ReactionFromSmarts('[C@H1:1][OH:2].[OH][C:3]=[O:4]>>[C@:1][O:2][C:3]=[O:4]')
    >>> ps=rxn.RunReactants((alcohol1,acid))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(=O)OC(C)CCN'
    >>> ps=rxn.RunReactants((alcohol2,acid))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(=O)O[C@H](C)CCN'
    >>> ps=rxn.RunReactants((alcohol3,acid))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(=O)O[C@@H](C)CCN'

A mapped atom with different chirality in reactants and products leads to inversion of stereochemistry:

    >>> rxn = AllChem.ReactionFromSmarts('[C@H1:1][OH:2].[OH][C:3]=[O:4]>>[C@@:1][O:2][C:3]=[O:4]')
    >>> ps=rxn.RunReactants((alcohol1,acid))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(=O)OC(C)CCN'
    >>> ps=rxn.RunReactants((alcohol2,acid))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(=O)O[C@@H](C)CCN'
    >>> ps=rxn.RunReactants((alcohol3,acid))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(=O)O[C@H](C)CCN'

If a mapped atom has chirality specified in the reactants, but not in the products, the reaction destroys chirality at that center:

    >>> rxn = AllChem.ReactionFromSmarts('[C@H1:1][OH:2].[OH][C:3]=[O:4]>>[C:1][O:2][C:3]=[O:4]')
    >>> ps=rxn.RunReactants((alcohol1,acid))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(=O)OC(C)CCN'
    >>> ps=rxn.RunReactants((alcohol2,acid))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(=O)OC(C)CCN'
    >>> ps=rxn.RunReactants((alcohol3,acid))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(=O)OC(C)CCN'

And, finally, if chirality is specified in the products, but not the reactants, the reaction creates a stereocenter with the specified chirality:

    >>> rxn = AllChem.ReactionFromSmarts('[CH1:1][OH:2].[OH][C:3]=[O:4]>>[C@:1][O:2][C:3]=[O:4]')
    >>> ps=rxn.RunReactants((alcohol1,acid))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(=O)O[C@H](C)CCN'
    >>> ps=rxn.RunReactants((alcohol2,acid))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(=O)O[C@H](C)CCN'
    >>> ps=rxn.RunReactants((alcohol3,acid))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(=O)O[C@H](C)CCN'

Note that this doesn’t make sense without including a bit more context around the stereocenter in the reaction definition:

    >>> rxn = AllChem.ReactionFromSmarts('[CH3:5][CH1:1]([C:6])[OH:2].[OH][C:3]=[O:4]>>[C:5][C@:1]([C:6])[O:2][C:3]=[O:4]')
    >>> ps=rxn.RunReactants((alcohol1,acid))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(=O)O[C@H](C)CCN'
    >>> ps=rxn.RunReactants((alcohol2,acid))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(=O)O[C@H](C)CCN'
    >>> ps=rxn.RunReactants((alcohol3,acid))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(=O)O[C@H](C)CCN'

Note that the chirality specification is not being used as part of the query: a molecule with no chirality specified can match a reactant with specified chirality.

In general, the reaction machinery tries to preserve as much stereochemistry information as possible. This works when a single new bond is formed to a chiral center:

    >>> rxn = AllChem.ReactionFromSmarts('[C:1][C:2]-O>>[C:1][C:2]-S')
    >>> alcohol2 = Chem.MolFromSmiles('C[C@@H](O)CCN')
    >>> ps=rxn.RunReactants((alcohol2,))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'C[C@@H](S)CCN'

But it fails if two or more bonds are formed:

    >>> rxn = AllChem.ReactionFromSmarts('[C:1][C:2](-O)-F>>[C:1][C:2](-S)-Cl')
    >>> alcohol = Chem.MolFromSmiles('C[C@@H](O)F')
    >>> ps=rxn.RunReactants((alcohol,))
    >>> Chem.MolToSmiles(ps[0][0],True)
    'CC(S)Cl'

In this case, there’s just not sufficient information present to allow the information to be preserved. You can help by providing mapping information:

#### Rules and caveats

1. Include atom map information at the end of an atom query. So do [C,N,O:1] or [C;R:1].
2. Don’t forget that unspecified bonds in SMARTS are either single or aromatic. Bond orders in product templates are assigned when the product template itself is constructed and it’s not always possible to tell if the bond should be single or aromatic:

    >>> rxn = AllChem.ReactionFromSmarts('[#6:1][#7,#8:2]>>[#6:1][#6:2]')
    >>> [Chem.MolToSmiles(x,1) for x in rxn.RunReactants((Chem.MolFromSmiles('C1NCCCC1'),))[0]]
    ['C1CCCCC1']
    >>> [Chem.MolToSmiles(x,1) for x in rxn.RunReactants((Chem.MolFromSmiles('c1ncccc1'),))[0]]
    ['c1ccccc-1']

    So if you want to copy the bond order from the reactant, use an “Any” bond:

    >>> rxn = AllChem.ReactionFromSmarts('[#6:1][#7,#8:2]>>[#6:1]~[#6:2]')
    >>> [Chem.MolToSmiles(x,1) for x in rxn.RunReactants((Chem.MolFromSmiles('c1ncccc1'),))[0]]
    ['c1ccccc1']

## The Feature Definition File Format

An FDef file contains all the information needed to define a set of chemical features. It contains definitions of feature types that are defined from queries built up using Daylight’s SMARTS language. [3] The FDef file can optionally also include definitions of atom types that are used to make feature definitions more readable.

### Chemical Features

Chemical features are defined by a Feature Type and a Feature Family. The Feature Family is a general classification of the feature (such as “Hydrogen-bond Donor” or “Aromatic”) while the Feature Type provides additional, higher-resolution, information about features. Pharmacophore matching is done using Feature Family’s. Each feature type contains the following pieces of information:

- A SMARTS pattern that describes atoms (one or more) matching the feature type.
- Weights used to determine the feature’s position based on the positions of its defining atoms.

### Syntax of the FDef file

#### AtomType definitions

An AtomType definition allows you to assign a shorthand name to be used in place of a SMARTS string defining an atom query. This allows FDef files to be made much more readable. For example, defining a non-polar carbon atom like this:

    AtomType Carbon_NonPolar [C&!$(C=[O,N,P,S])&!$(C#N)]

creates a new name that can be used anywhere else in the FDef file that it would be useful to use this SMARTS. To reference an AtomType, just include its name in curly brackets. For example, this excerpt from an FDef file defines another atom type - Hphobe - which references the Carbon_NonPolar definition:

    AtomType Carbon_NonPolar [C&!$(C=[O,N,P,S])&!$(C#N)]
    AtomType Hphobe [{Carbon_NonPolar},c,s,S&H0&v2,F,Cl,Br,I]

Note that `{Carbon_NonPolar}` is used in the new AtomType definition without any additional decoration (no square brackes or recursive SMARTS markers are required).

Repeating an AtomType results in the two definitions being combined using the SMARTS “,” (or) operator. Here’s an example:

    AtomType d1 [N&!H0]
    AtomType d1 [O&!H0]

This is equivalent to:

    AtomType d1 [N&!H0,O&!H0]

Which is equivalent to the more efficient:

    AtomType d1 [N,O;!H0]

Note that these examples tend to use SMARTS’s high-precendence and operator “&” and not the low-precedence and “;”. This can be important when AtomTypes are combined or when they are repeated. The SMARTS “,” operator is higher precedence than “;”, so definitions that use “;” can lead to unexpected results.

It is also possible to define negative AtomType queries:

    AtomType d1 [N,O,S]
    AtomType !d1 [H0]

The negative query gets combined with the first to produce a definition identical to this:

    AtomType d1 [!H0;N,O,S]

Note that the negative AtomType is added to the beginning of the query.

#### Feature definitions

A feature definition is more complex than an AtomType definition and stretches across multiple lines:

    DefineFeature HDonor1 [N,O;!H0]
    Family HBondDonor
    Weights 1.0
    EndFeature

The first line of the feature definition includes the feature type and the SMARTS string defining the feature. The next two lines (order not important) define the feature’s family and its atom weights (a comma-delimited list that is the same length as the number of atoms defining the feature). The atom weights are used to calculate the feature’s locations based on a weighted average of the positions of the atom defining the feature. More detail on this is provided below. The final line of a feature definition must be EndFeature. It is perfectly legal to mix AtomType definitions with feature definitions in the FDef file. The one rule is that AtomTypes must be defined before they are referenced.

#### Additional syntax notes:

- Any line that begins with a # symbol is considered a comment and will be ignored.

- A backslash character, , at the end of a line is a continuation character, it indicates that the data from that line is continued on the next line of the file. Blank space at the beginning of these additional lines is ignored. For example, this AtomType definition:

      AtomType tButylAtom [$([C;!R](-[CH3])(-[CH3])(-[CH3])),\
      $([CH3](-[C;!R](-[CH3])(-[CH3])))]

  is exactly equivalent to this one:

      AtomType tButylAtom [$([C;!R](-[CH3])(-[CH3])(-[CH3])),$([CH3](-[C;!R](-[CH3])(-[CH3])))]

  (though the first form is much easier to read!)

#### Atom weights and feature locations

### Frequently Asked Question(s)

- What happens if a Feature Type is repeated in the file? Here’s an example:

      DefineFeature HDonor1 [O&!H0]
      Family HBondDonor
      Weights 1.0
      EndFeature

      DefineFeature HDonor1 [N&!H0]
      Family HBondDonor
      Weights 1.0
      EndFeature

  In this case both definitions of the HDonor1 feature type will be active. This is functionally identical to:

      DefineFeature HDonor1 [O,N;!H0]
      Family HBondDonor
      Weights 1.0
      EndFeature

  However the formulation of this feature definition with a duplicated feature type is considerably less efficient and more confusing than the simpler combined definition.

## Representation of Pharmacophore Fingerprints

In the RDKit scheme the bit ids in pharmacophore fingerprints are not hashed: each bit corresponds to a particular combination of features and distances. A given bit id can be converted back to the corresponding feature types and distances to allow interpretation. An illustration for 2D pharmacophores is shown in Figure 1: Bit numbering in pharmacophore fingerprints.

Figure 1: Bit numbering in pharmacophore fingerprints

## Atom-Atom Matching in Substructure Queries

When doing substructure matches for queries derived from SMARTS the rules for which atoms in the molecule should match which atoms in the query are well defined.[#smarts]_ The same is not necessarily the case when the query molecule is derived from a mol block or SMILES.

The general rule used in the RDKit is that if you don’t specify a property in the query, then it’s not used as part of the matching criteria and that Hs are ignored. This leads to the following behavior:

Molecule | Query | Match
--- | --- | ---
CCO | CCO | Yes
CC[O-] | CCO | Yes
CCO | CC[O-] | No
CC[O-] | CC[O-] | Yes
CC[O-] | CC[OH] | Yes
CCOC | CC[OH] | Yes
CCOC | CCO | Yes
CCC | CCC | Yes
CC[14C] | CCC | Yes
CCC | CC[14C] | No
CC[14C] | CC[14C] | Yes
OCO | C | Yes
OCO | [CH] | No
OCO | [CH2] | No
OCO | [CH3] | No
OCO | O[CH3] | Yes
O[CH2]O | C | Yes
O[CH2]O | [CH2] | No

Demonstrated here:

    >>> Chem.MolFromSmiles('CCO').HasSubstructMatch(Chem.MolFromSmiles('CCO'))
    True
    >>> Chem.MolFromSmiles('CC[O-]').HasSubstructMatch(Chem.MolFromSmiles('CCO'))
    True
    >>> Chem.MolFromSmiles('CCO').HasSubstructMatch(Chem.MolFromSmiles('CC[O-]'))
    False
    >>> Chem.MolFromSmiles('CC[O-]').HasSubstructMatch(Chem.MolFromSmiles('CC[O-]'))
    True
    >>> Chem.MolFromSmiles('CC[O-]').HasSubstructMatch(Chem.MolFromSmiles('CC[OH]'))
    True
    >>> Chem.MolFromSmiles('CCOC').HasSubstructMatch(Chem.MolFromSmiles('CC[OH]'))
    True
    >>> Chem.MolFromSmiles('CCOC').HasSubstructMatch(Chem.MolFromSmiles('CCO'))
    True
    >>> Chem.MolFromSmiles('CCC').HasSubstructMatch(Chem.MolFromSmiles('CCC'))
    True
    >>> Chem.MolFromSmiles('CC[14C]').HasSubstructMatch(Chem.MolFromSmiles('CCC'))
    True
    >>> Chem.MolFromSmiles('CCC').HasSubstructMatch(Chem.MolFromSmiles('CC[14C]'))
    False
    >>> Chem.MolFromSmiles('CC[14C]').HasSubstructMatch(Chem.MolFromSmiles('CC[14C]'))
    True
    >>> Chem.MolFromSmiles('OCO').HasSubstructMatch(Chem.MolFromSmiles('C'))
    True
    >>> Chem.MolFromSmiles('OCO').HasSubstructMatch(Chem.MolFromSmiles('[CH]'))
    False
    >>> Chem.MolFromSmiles('OCO').HasSubstructMatch(Chem.MolFromSmiles('[CH2]'))
    False
    >>> Chem.MolFromSmiles('OCO').HasSubstructMatch(Chem.MolFromSmiles('[CH3]'))
    False
    >>> Chem.MolFromSmiles('OCO').HasSubstructMatch(Chem.MolFromSmiles('O[CH3]'))
    True
    >>> Chem.MolFromSmiles('O[CH2]O').HasSubstructMatch(Chem.MolFromSmiles('C'))
    True
    >>> Chem.MolFromSmiles('O[CH2]O').HasSubstructMatch(Chem.MolFromSmiles('[CH2]'))
    False

## Molecular Sanitization

The molecule parsing functions all, by default, perform a “sanitization” operation on the molecules read. The idea is to generate useful computed properties (like hybridization, ring membership, etc.) for the rest of the code and to ensure that the molecules are “reasonable”: that they can be represented with octet-complete Lewis dot structures.

Here are the steps involved, in order.

> 1. `clearComputedProps`: removes any computed properties that already exist

>      on the molecule and its atoms and bonds. This step is always performed.

> 2. `cleanUp`: standardizes a small number of non-standard valence states. The clean up operations are:

>    > - Neutral 5 valent Ns with double bonds to Os are converted to the zwitterionic form. Example: `N(=O)=O -> [N+](=O)O-]`
>    > - Neutral 5 valent Ns with triple bonds to another N are converted to the zwitterionic form. Example: `C-N=N#N -> C-N=[N+]=[N-]`
>    > - Neutral 5 valent phosphorus with one double bond to an O and another to either a C or a P are converted to the zwitterionic form. Example: `C=P(=O)O -> C=[P+]([O-])O`
>    > - Neutral Cl, Br, or I with exclusively O neighbors, and a valence of 3, 5, or 7, are converted to the zwitterionic form. This covers things like chlorous acid, chloric acid, and perchloric acid. Example: `O=Cl(=O)O -> [O-][Cl+2][O-]O`

>    This step should not generate execptions.

> 3. `updatePropertyCache`: calculates the explicit and implicit valences on all atoms. This generates exceptions for atoms in higher-than-allowed valence states. This step is always performed, but if it is “skipped” the test for non-standard valences will not be carried out.

> 4. `symmetrizeSSSR`: calls the symmetrized smallest set of smallest rings algorithm (discussed in the Getting Started document).

> 5. `Kekulize`: converts aromatic rings to their Kekule form. Will raise an exception if a ring cannot be kekulized or if aromatic bonds are found outside of rings.

> 6. `assignRadicals`: determines the number of radical electrons (if any) on each atom.

> 7. `setAromaticity`: identifies the aromatic rings and ring systems (see above), sets the aromatic flag on atoms and bonds, sets bond orders to aromatic.

> 8. `setConjugation`: identifies which bonds are conjugated

> 9. `setHybridization`: calculates the hybridization state of each atom

> 10. `cleanupChirality`: removes chiral tags from atoms that are not sp3 hybridized.

> 11. `adjustHs`: adds explicit Hs where necessary to preserve the chemistry. This is typically needed for heteroatoms in aromatic rings. The classic example is the nitrogen atom in pyrrole.

The individual steps can be toggled on or off when calling `MolOps::sanitizeMol` or `Chem.SanitizeMol`.

## Implementation Details

### “Magic” Property Values

The following property values are regularly used in the RDKit codebase and may be useful to client code.

#### ROMol (Mol in Python)

Property Name | Use
--- | ---
MolFileComments | Read from/written to the comment line of CTABs.
MolFileInfo | Read from/written to the info line of CTABs.
_MolFileChiralFlag | Read from/written to the chiral flag of CTABs.
_Name | Read from/written to the name line of CTABs.
_smilesAtomOutputOrder | The order in which atoms were written to SMILES

#### Atom

Property Name | Use
--- | ---
_CIPCode | the CIP code (R or S) of the atom
_CIPRank | the integer CIP rank of the atom
_ChiralityPossible | set if an atom is a possible chiral center
_MolFileRLabel | integer R group label for an atom, read from/written to CTABs.
_ReactionDegreeChanged | set on an atom in a product template of a reaction if its degree changes in the reaction
_protected | atoms with this property set will not be considered as matching reactant queries in reactions
dummyLabel | (on dummy atoms) read from/written to CTABs as the atom symbol
molAtomMapNumber | the atom map number for an atom, read from/written to SMILES and CTABs
molfileAlias | the mol file alias for an atom (follows A tags), read from/written to CTABs
molFileValue | the mol file value for an atom (follows V tags), read from/written to CTABs
molFileInversionFlag | used to flag whether stereochemistry at an atom changes in a reaction, read from/written to CTABs, determined automatically from SMILES
molRxnComponent | which component of a reaction an atom belongs to, read from/written to CTABs
molRxnRole | which role an atom plays in a reaction (1=Reactant, 2=Product, 3=Agent), read from/written to CTABs
smilesSymbol | determines the symbol that will be written to a SMILES for the atom

### Thread safety and the RDKit

While writing the RDKit, we did attempt to ensure that the code would work in a multi-threaded environment by avoiding use of global variables, etc. However, making code thread safe is not a completely trivial thing, so there are no doubt some gaps. This section describes which pieces of the code base have explicitly been tested for thread safety.

Note: With the exception of the small number of methods/functions
  that take a `numThreads` argument, this section does not apply to using the RDKit from Python threads. Boost.Python ensures that only one thread is calling into the C++ code at any point. To get concurrent execution in Python, use the multiprocessing module or one of the other standard python approaches for this .

#### What has been tested

> - Reading molecules from SMILES/SMARTS/Mol blocks
> - Writing molecules to SMILES/SMARTS/Mol blocks
> - Generating 2D coordinates
> - Generating 3D conformations with the distance geometry code
> - Optimizing molecules with UFF or MMFF
> - Generating fingerprints
> - The descriptor calculators in $RDBASE/Code/GraphMol/Descriptors
> - Substructure searching (Note: if a query molecule contains recursive queries, it may not be safe to use it concurrently on multiple threads, see below)
> - The Subgraph code
> - The ChemTransforms code
> - The chemical reactions code
> - The Open3DAlign code
> - The MolDraw2D drawing code

#### Known Problems

> - InChI generation and (probably) parsing. This seems to be a limitation of the IUPAC InChI code. In order to allow the code to be used in a multi-threaded environment, a mutex is used to ensure that only one thread is using the IUPAC code at a time. This is only enabled if the RDKit is built with the `RDK_TEST_MULTITHREADED` option enabled.
> - The MolSuppliers (e.g. SDMolSupplier, SmilesMolSupplier?) change their internal state when a molecule is read. It is not safe to use one supplier on more than one thread.
> - Substructure searching using query molecules that include recursive queries. The recursive queries modify their internal state when a search is run, so it’s not safe to use the same query concurrently on multiple threads. If the code is built using the `RDK_BUILD_THREADSAFE_SSS` argument (the default for the binaries we provide), a mutex is used to ensure that only one thread is using a given recursive query at a time.

### Implementation of the TPSA Descriptor

The topological polar surface area (TPSA) descriptor implemented in the RDKit is described in a publication by Peter Ertl et al. (https://pubs.acs.org/doi/abs/10.1021/jm000942e) The RDKit’s implementation differs from what is described in that publication. This section describes the difference and why it’s there.

The RDKit’s TPSA implementation only includes, by default, contributions from N and O atoms. Table 1 of the TPSA publication. however, includes parameters for polar S and P in addition to N and O. What’s going on?

The original TPSA implementation that is in the Daylight Contrib dir (http://www.daylight.com/download/contrib/tpsa.html) does not include contributions from polar S or P and, it turns out, the reference values that are included in the TPSA paper also don’t include S or P contributions. For example, the TPSA provided in Table 3 for foscarnet (SMILES OC(=O)P(=O)(O)O), 94.8, corresponds the sum of the O contributions - 3x20.23 + 2*17.07 = 94.8. Adding the P contribution - 9.81- would give a PSA of 104.6. This is also true for the other P and S containing compounds in Table 3.

In the RDKit implementation, we chose to reproduce the behavior of the tpsa.c Contrib program and what is provided in Table 3 of the paper, so polar S and P are ignored. Based on a couple of user requests, for the 2018.09 release of the RDKit we added the option to include S and P contributions:

    >>> from rdkit.Chem import Descriptors
    >>> Descriptors.TPSA(Chem.MolFromSmiles('OC(=O)P(=O)(O)O')) # foscarnet
    94.83
    >>> Descriptors.TPSA(Chem.MolFromSmiles('OC(=O)P(=O)(O)O'), includeSandP=True)
    104.64...
    >>> Descriptors.TPSA(Chem.MolFromSmiles('Cc1ccccc1N1C(=O)c2cc(S(N)(=O)=O)c(Cl)cc2NC1C')) # metolazone
    92.5
    >>> Descriptors.TPSA(Chem.MolFromSmiles('Cc1ccccc1N1C(=O)c2cc(S(N)(=O)=O)c(Cl)cc2NC1C'), includeSandP=True)
    100.88

## Atom Properties and SDF files

Note This section describes functionality added in the 2019.03.1 release of the RDKit.

By default the `rdkit.Chem.rdmolfiles.SDMolSupplier` and `rdkit.Chem.rdmolfiles.ForwardSDMolSupplier` classes (`RDKit::SDMolSupplier` and `RDKit::ForwardMolSupplier` in C++) can now recognize some molecular properties as property lists and them into atomic properties. Properties with names that start with `atom.prop`, `atom.iprop`, `atom.dprop`, or `atom.bprop` are converted to atomic properties of type string, int (64 bit), double, or bool respectively.

Here’s a sample block from an SDF that demonstrates all of the features, they are explained below:

    property_example
        RDKit  2D

      3  3  0  0  0  0  0  0  0  0999 V2000
        0.8660    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
      -0.4330    0.7500    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0
      -0.4330   -0.7500    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
      1  2  1  0
      2  3  1  0
      3  1  1  0
    M  END
    >  <atom.dprop.PartialCharge>  (1)
    0.008 -0.314 0.008

    >  <atom.iprop.NumHeavyNeighbors>  (1)
    2 2 2

    >  <atom.prop.AtomLabel>  (1)
    C1 N2 C3

    >  <atom.bprop.IsCarbon>  (1)
    1 0 1

    >  <atom.prop.PartiallyMissing>  (1)
    one n/a three

    >  <atom.iprop.PartiallyMissingInt>  (1)
    [?] 2 2 ?

    $$$$

Every atom property list should contain a number of space-delimited elements equal to the number of atoms. Missing values are, by default, indicated with the string `n/a`. The missing value marker can be changed by beginning the property list with a value in square brackets. So, for example, the property `PartiallyMissing` is set to “one” for atom 0, “three” for atom 2, and is not set for atom 1. Similarly the property `PartiallyMissingInt` is set to 2 for atom 0, 2 for atom 1, and is not set for atom 2.

This behavior is enabled by default and can be turned on/off with the `rdkit.Chem.rdmolfiles.SetProcessPropertyLists` method.

If you have atom properties that you would like to have written to SDF files, you can use the functions `rdkit.Chem.rdmolfiles.CreateAtomStringPropertyList()`, `rdkit.Chem.rdmolfiles.CreateAtomIntPropertyList()`, `rdkit.Chem.rdmolfiles.CreateAtomDoublePropertyList()`, or `rdkit.Chem.rdmolfiles.CreateAtomBoolPropertyList()` :

    >>> m = Chem.MolFromSmiles('CO')
    >>> m.GetAtomWithIdx(0).SetDoubleProp('foo',3.14)
    >>> Chem.CreateAtomDoublePropertyList(m,'foo')
    >>> m.GetProp('atom.dprop.foo')
    '3.1400000000000001 n/a'
    >>> from io import StringIO
    >>> sio = StringIO()
    >>> w = Chem.SDWriter(sio)
    >>> w.write(m)
    >>> w=None
    >>> print(sio.getvalue())

         RDKit          2D

      2  1  0  0  0  0  0  0  0  0999 V2000
        0.0000    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
        1.2990    0.7500    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0
      1  2  1  0
    M  END
    >  <atom.dprop.foo>  (1)
    3.1400000000000001 n/a

    $$$$

## Support for Enhanced Stereochemistry

### Overview

We are going to follow, at least for the initial implementation, the enhanced stereo representation used in V3k mol files: groups of atoms with specified stereochemistry with an `ABS`, `AND`, or `OR` marker indicating what is known. The general idea is that `AND` indicates mixtures and `OR` indicates unknown single substances.

Here are some illustrations of what the various combinations mean:

What’s drawn | Mixture? | What it means
--- | --- | ---
| mixture |
| mixture |
| mixture |
| single |
| single |
| single |
| mixture |
| single |

### Representation

Stored as a vector of `rdkit.Chem.rdchem.StereoGroup` objects on a molecule. Each `StereoGroup` keeps track of its type and the set of atoms that make it up.

### Use cases

The initial target is to not lose data on an `V3k mol -> RDKit -> V3k mol` round trip. Manipulation, depiction, and searching are future goals.

It is possible to enumerate the elements of a `StereoGroup` using the function `rdkit.Chem.EnumerateStereoisomers.EumerateStereoisomers()`, which also preserves membership in the original `StereoGroup`.

    >>> m = Chem.MolFromSmiles('C[C@H](F)C[C@H](O)Cl |&1:1|')
    >>> m.GetStereoGroups()[0].GetGroupType()
    rdkit.Chem.rdchem.StereoGroupType.STEREO_AND
    >>> [x.GetIdx() for x in m.GetStereoGroups()[0].GetAtoms()]
    [1]
    >>> from rdkit.Chem.EnumerateStereoisomers import EnumerateStereoisomers
    >>> [Chem.MolToCXSmiles(x) for x in EnumerateStereoisomers(m)]
    ['C[C@@H](F)C[C@H](O)Cl |&1:1|', 'C[C@H](F)C[C@H](O)Cl |&1:1|']

Reactions also preserve `StereoGroup``s. Product atoms are included in the ``StereoGroup` as long as the reaction doesn’t create or destroy chirality at the atom.

    >>> def clearAllAtomProps(mol):
    ...  """So that atom mapping isn't shown"""
    ...  for atom in mol.GetAtoms():
    ...   for key in atom.GetPropsAsDict():
    ...    atom.ClearProp(key)
    ...
    >>> rxn = AllChem.ReactionFromSmarts('[C:1]F >> [C:1]Br')
    >>> ps=rxn.RunReactants([m])
    >>> clearAllAtomProps(ps[0][0])
    >>> Chem.MolToCXSmiles(ps[0][0])
    'C[C@H](Br)C[C@H](O)Cl |&1:1|'

## Additional Information About the Fingerprints

This section, which is not currently comprehensive, is intended to provide some documentation about the types of fingerprints available in the RDKit. We don’t reproduce information that can be found in the literature, but try and capture the unpublished bits (of which there are quite a few).

### RDKit Fingerprints

This is an RDKit-specific fingerprint that is inspired by (though it differs significantly from) public descriptions of the Daylight fingerprint [7]. The fingerprinting algorithm identifies all subgraphs in the molecule within a particular range of sizes, hashes each subraphs to generate a raw bit ID, mods that raw bit ID to fit in the assigned fingerprint size, and then sets the corresponding bit. Options are available to generate count-based forms of the fingerprint or “non-folded” forms (using a sparse representation).

The default scheme for hashing subgraphs is to hash the individual bonds based on:
  - the types of the two atoms. Atom types include the atomic number (mod 128), and whether or not the atom is aromatic.
  - the degrees of the two atoms in the path.
  - the bond type (or `AROMATIC` if the bond is marked as aromatic)

#### Fingerprint-specific options

> - `minPath` and `maxPath` control the size (in bonds) of the subgraphs/paths considered
> - `nBitsPerHash`: If this is greater than one, each subgraph will set more than one bit. The additional bits will be generated by seeding a random number generator with the original raw bit ID and generating the appropriate number of random numbers.
> - `useHs`: toggles whether or not Hs are included in the subgraphs/paths (assuming that there are Hs in the molecule graph.
> - `tgtDensity`: if this is greather than zero, the fingerprint will be repeatedly folded in half until the density of set bits is greater than or equal to this value or the fingerprint only contains minSize bits. Note that this means that the resulting fingerprint will not necessarily be the size you requested.
> - `branchedPaths`: if this is true (the default value), the algorithm will use subgraphs (i.e features can be branched. If false, only linear paths will be considered.
> - `useBondOrder`: if true (the default) bond types will be considered when hashing subgraphs, otherwise this component of the hash will be ignored.

### Pattern Fingerprints

These fingerprints were designed to be used in substructure screening. These are, as far as I know, unique to the RDKit. The algorithm identifies features in the molecule by doing substructure searches using a small number (12 in the `2019.03` release of the RDKit) of very generic SMARTS patterns - like `[*]~[*]~[*](~[*])~[*]` or `[R]~1[R]~[R]~[R]~1`, and then hashing each occurrence of a pattern based on the atom and bond types involved. The fact that particular pattern matched the molecule at all is also stored by hashing the pattern ID and size. If a particular feature contains either a query atom or a query bond (e.g. something generated from SMARTS), the only information that is hashed is the fact that the generic pattern matched.

For the `2019.03` release, the atom types use just the atomic number of the atom and the bond types use the bond type, or `AROMATIC` for aromatic bonds).

NOTE: Because it plays an important role in substructure screenout, the internals of this fingerprint (the generic patterns used and/or the details of the hashing algorithm) may change from one release to the next.

### Atom-Pair and Topological Torsion Fingerprints

These two related fingerprints are implemented based on the original papers: [8] [9]. Atoms are typed based on atomic number, number of pi electrons, and the degree of the atom. Optionally information about atomic chirality can also be integrated into the atom types. Both fingerprint types can be generated in explicit or sparse form and as bit or count vectors. These fingerprint types are different from the others in the RDKit in that bits in the sparse form of the fingerprint can be directly explained (i.e. the “hashing function” used is fully reversible).

These fingerprints were originally “intended” to be used in count-vectors and they seem to work better that way. The default behavior of the explicit bit-vector forms of both fingerprints is to use a “count simulation” procedure where multiple bits are set for a given feature if it occurs more than once. The default behavior is to use 4 fingerprint bits for each feature (so a 2048 bit fingerprint actually stores information about the same number of features as a 512 bit fingerprint that isn’t using count simulation). The bins correspond to counts of 1, 2, 4, and 8. As an example of how this works: if a feature occurs 5 times in a molecule, the bits corresponding to counts 1, 2, and 4 will be set.

### Morgan and Feature Morgan Fingerprints

These are implememented based on the original paper [10]. The algorithm follows the description in the paper as closely as possible with the exception of the chemical feature definitions used for the “Feature Morgan” fingerprint - the RDKit implementation uses the feature types Donor, Acceptor, Aromatic, Halogen, Basic, and Acidic with definitions adapted from those in the paper [11]. It is possible to provide your own atom types. The fingerprints are available as either explicit or sparse count vectors or explicit bit vectors.

### Layered Fingerprints

These are another “RDKit original” and were developed with the intention of using them as a substructure fingerprint. Since the pattern fingerprint is far simpler and has proven to be quite effective as a substructure fingerprint, the layered fingerprint hasn’t received much attention. It may still be interesting for something, so we continue to include it.

The idea of the fingerprint is generate features using the same subgraph (or path) enumeration algorithm used in the RDKit fingerprint. After a subgraph has been generated, it is used to set multiple bits based on different atom and bond type definitions.

Footnotes

[1] | http://www.daylight.com/dayhtml/doc/theory/theory.smirks.html

[2] | (1, 2) http://www.daylight.com/dayhtml/doc/theory/theory.smiles.html

[3] | (1, 2, 3) http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html

[4] | https://docs.chemaxon.com/display/docs/ChemAxon+Extended+SMILES+and+SMARTS+-+CXSMILES+and+CXSMARTS

[5] | Thanks to James Davidson for this example.

[6] | Thanks to JP Ebejer and Paul Finn for this example.

[7] | http://www.daylight.com/dayhtml/doc/theory/theory.finger.html

[8] | http://pubs.acs.org/doi/abs/10.1021/ci00046a002

[9] | http://pubs.acs.org/doi/abs/10.1021/ci00054a008

[10] | http://pubs.acs.org/doi/abs/10.1021/ci100050t

[11] | https://doi.org/10.1002/(SICI)1097-0290(199824)61:1%3C47::AID-BIT9%3E3.0.CO;2-Z

## License

This document is copyright (C) 2007-2019 by Greg Landrum

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/4.0/ or send a letter to Creative Commons, 543 Howard Street, 5th Floor, San Francisco, California, 94105, USA.

The intent of this license is similar to that of the RDKit itself. In simple words: “Do whatever you want with it, but please give us some credit.”