Andreas V. Copan, Kevin B. Moore III, Sarah N. Elliott, and Stephen J. Klippenstein
This repository includes four modules:
- AutoMol: A library for manipulating and interconverting molecular descriptors
- PhyDat: A small, supporting library of physical constants and chemical data
- MolSym: An externally-developed package for handling molecular symmetry, by Stephen M. Goodlett and Nathaniel L. Kitzmiller (see here for details)
The central module is AutoMol, which provides an extensive library of functions for working with various molecular descriptors, including:
- Molecular graphs (molecules and transition states):
automol.graph - Cartesian geometries (molecules and transition states):
automol.geom - Z-matrix geometries (molecules and transition states):
automol.zmat - Various string identifiers:
- SMILES (molecules only):
automol.smiles - InChI (molecules only):
automol.inchi - the "AutoMech Chemical Identifier" (AMChI, molecules and transition states):
automol.amchi
- SMILES (molecules only):
Included are functions for interconverting between these various representations, for extracting information from each, and for visualizing the chemical structures they represent in an IPython notebook.
Other notable functionalities include...
- Reaction mapping for combustion reaction classes:
automol.reac - Stereochemistry handling for molecules and transition states:
automol.graph.expand_stereo() - Geometry embedding for molecules and transition states:
automol.graph.geometry()
Basic example. One can generate Cartesian and Z-matrix geometries from a SMILES string as follows.
>>> smi = "C#C"
>>> geo = automol.smiles.geometry(smi)
>>> zma = automol.geom.zmatrix(geo)
Each of these can be converted a string for printing or writing to a file.
>>> print(automol.geom.string(geo))
C -0.590121 0.289363 0.050632
C 0.590121 0.095717 -0.050631
H -1.638114 0.461311 0.140548
H 1.638114 -0.076229 -0.140549
>>> print(automol.zmat.string(zma))
C
X 0 R1
C 0 R2 1 A2
H 0 R3 1 A3 2 D3
X 2 R4 0 A4 1 D4
H 2 R5 4 A5 0 D5
R1 = 1.000000
R2 = 1.200302
R3 = 1.065804
R4 = 1.000000
R5 = 1.065804
A2 = 89.999959
A3 = 90.000057
A4 = 90.000041
A5 = 90.000104
D3 = 180.000000
D4 = 359.999999
D5 = 179.999965
They can also be visualized in an IPython notebook using the display functions for these datatypes.
>>> automol.zmat.display(zma)
Tracking connectivity. We can track how the atoms are connected with a molecular graph.
>>> gra = automol.smiles.graph(smi)
>>> geo = automol.graph.geometry(gra)
Like the other datatypes, graphs can be printed as strings.
>>> print(automol.graph.string(gra))
atoms:
0: {symbol: C, implicit_hydrogens: 0, stereo_parity: null}
1: {symbol: C, implicit_hydrogens: 0, stereo_parity: null}
2: {symbol: H, implicit_hydrogens: 0, stereo_parity: null}
3: {symbol: H, implicit_hydrogens: 0, stereo_parity: null}
bonds:
0-1: {order: 1, stereo_parity: null}
0-2: {order: 1, stereo_parity: null}
1-3: {order: 1, stereo_parity: null}
They can also be displayed in an IPython notebook, with or without labels and explicit hydrogens (both off by default).
>>> automol.graph.display(gra, exp=True, label=True)
The above graph matches the connectivity of the Cartesian geometry that we printed above. We might also wish to track this connectivity upon conversion to a Z-matrix. We can do so by returning the conversion information upon generating the Z-matrix and applying this to the geometry-aligned graph.
>>> zma, zc_ = automol.geom.zmatrix_with_conversion_info(geo)
>>> zgra = automol.graph.apply_zmatrix_conversion(gra, zc_)
The result is graph that matches the connectivity of the Z-matrix that we printed above.
>>> print(automol.graph.string(zgra))
atoms:
0: {symbol: C, implicit_hydrogens: 0, stereo_parity: null}
1: {symbol: X, implicit_hydrogens: 0, stereo_parity: null}
2: {symbol: C, implicit_hydrogens: 0, stereo_parity: null}
3: {symbol: H, implicit_hydrogens: 0, stereo_parity: null}
4: {symbol: X, implicit_hydrogens: 0, stereo_parity: null}
5: {symbol: H, implicit_hydrogens: 0, stereo_parity: null}
bonds:
0-1: {order: 0, stereo_parity: null}
0-2: {order: 1, stereo_parity: null}
0-3: {order: 1, stereo_parity: null}
2-4: {order: 0, stereo_parity: null}
2-5: {order: 1, stereo_parity: null}
We can see the connectivity more easily by displaying the graph.1
>>> automol.graph.display(zgra, exp=True, label=True)
Stereoexpansion. As a simple example, we can enumerate the stereoisomers of 1,2-difluoroethylene as follows.
smi = 'FC=CF'
gra = automol.smiles.graph(smi)
for sgra in automol.graph.expand_stereo(gra):
ssmi = automol.graph.smiles(sgra) # stereoisomer SMILES
sgeo = automol.graph.geometry(sgra) # stereoisomer geometry
print(ssmi)
automol.geom.display(sgeo)
The above code will print the SMILES string and display the geometry of each stereoisomer (cis and trans).
Basic example. One can map a reaction from SMILES as follows.
>>> rxn, *_ = automol.reac.from_smiles(["C", "[OH]"], ["[CH3]", "O"])
This function returns a list of Reaction objects, one for each possible transition
state connecting these reactants and products.
While the Reaction object allows us to track additional reaction information, for now
let us simply focus on the transition state itself, starting with its graph.
>>> ts_gra = automol.reac.ts_graph(rxn)
>>> print(automol.graph.string(ts_gra))
atoms:
0: {symbol: C, implicit_hydrogens: 0, stereo_parity: null}
1: {symbol: H, implicit_hydrogens: 0, stereo_parity: null}
2: {symbol: H, implicit_hydrogens: 0, stereo_parity: null}
3: {symbol: H, implicit_hydrogens: 0, stereo_parity: null}
4: {symbol: H, implicit_hydrogens: 0, stereo_parity: null}
5: {symbol: O, implicit_hydrogens: 0, stereo_parity: null}
6: {symbol: H, implicit_hydrogens: 0, stereo_parity: null}
bonds:
0-1: {order: 0.9, stereo_parity: null}
0-2: {order: 1, stereo_parity: null}
0-3: {order: 1, stereo_parity: null}
0-4: {order: 1, stereo_parity: null}
1-5: {order: 0.1, stereo_parity: null}
5-6: {order: 1, stereo_parity: null}
Here, forming bonds are encoded with a bond order of 0.1 and breaking bonds are
encoded with a bond order of 0.9.
We can generate a geometry for this transition state graph the same way we would for a
molecular graph.
>>> ts_geo = automol.graph.geometry(ts_gra)
>>> automol.geom.display(ts_geo, gra=ts_gra)
Similarly, we can generate a Z-matrix for the transition state.
>>> ts_zma, zc_ = automol.geom.zmatrix_with_conversion_info(ts_geo, gra=ts_gra)
>>> ts_zgra = automol.graph.apply_zmatrix_conversion(ts_gra, zc_)
>>> automol.zmat.display(ts_zma, gra=ts_zgra)
Note the appropriate insertion of a dummy atom over the transferring hydrogen atom.
Stereoexpansion. Similar to the example above, we can also enumerate the stereoisomers of a transition state.
>>> rxn, *_ = automol.reac.from_smiles(["CCO[C@H](O[O])C"], ["C[CH]O[C@H](OO)C"], stereo=False)
>>> ts_gra = automol.reac.ts_graph(rxn)
>>>
>>> for ts_sgra in automol.graph.expand_stereo(ts_gra):
>>> ts_schi = automol.graph.amchi(ts_sgra)
>>> ts_sgeo = automol.graph.geometry(ts_sgra)
>>>
>>> print(ts_schi)
>>> automol.geom.display(ts_sgeo, gra=ts_sgra)
AMChI=1/C4H9O3/c1-3-6-4(2)7-5-8-3/h3-4H,1-2H3/t3-,4-/m0/s1/k8-5/f8-3/r1
<Display shown below>
AMChI=1/C4H9O3/c1-3-6-4(2)7-5-8-3/h3-4H,1-2H3/t3-,4+/m0/s1/k8-5/f8-3/r1
<Display omitted>
AMChI=1/C4H9O3/c1-3-6-4(2)7-5-8-3/h3-4H,1-2H3/t3-,4+/m1/s1/k8-5/f8-3/r1
<Display omitted>
AMChI=1/C4H9O3/c1-3-6-4(2)7-5-8-3/h3-4H,1-2H3/t3-,4-/m1/s1/k8-5/f8-3/r1
<Display omitted>
Standard chemical identifiers like SMILES and InChI cannot describe individual transition states. AutoMol comes with its own string identifier for this purpose, the AMChI ("AutoMech Chemical Identifier").2