Package rdkit :: Package Chem :: Module MCS

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# Source Code for Module rdkit.Chem.MCS

      1  # This work was funded by Roche and generously donated to the free
      2  # and open source cheminformatics community.
      3
      4  ## Copyright (c) 2012 Andrew Dalke Scientific AB
      5  ## Andrew Dalke <dalke@dalkescientific.com>
      6  ##
      7  ## All rights reserved.
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     10  ## modification, are permitted provided that the following conditions are
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     32  from rdkit.Chem import fmcs
     33  from rdkit.Chem.fmcs import Default
     34
     35  """MCS - find a Maximum Common Substructure
     36
     37  This software finds the maximum common substructure of a set of
     38  structures and reports it as a SMARTS string.
     39
     40  The SMARTS string depends on the desired match properties. For
     41  example, if ring atoms are only allowed to match ring atoms then an
     42  aliphatic ring carbon in the query is converted to the SMARTS "[C;R]",
     43  and the double-bond ring bond converted to "=;@" while the respective
     44  chain-only version are "[C;!R]" and "=;!@".
     45
     46  """
     47
     48
     49  # The simplified algorithm description is:
     50  #
     51  #   best_substructure = None
     52  #   pick one structure as the query, and other as the targets
     53  #   for each substructure in the query graph:
     54  #     convert it to a SMARTS string based on the desired match properties
     55  #     if the SMARTS pattern exists in all of the targets:
     56  #        then this is a common substructure
     57  #        keep track of the maximum such common structure,
     58  #
     59  # The algorithm will usually take a long time. There are several
     60  # ways to speed it up.
     61  #
     62  # == Bond elimination ==
     63  #
     64  # As the first step, remove bonds which obviously cannot be part of the
     65  # MCS.
     66  #
     67  # This requires atom and bond type information, which I store as SMARTS
     68  # patterns. A bond can only be in the MCS if its canonical bond type is
     69  # present in all of the structures. A bond type is string made of the
     70  # SMARTS for one atom, the SMARTS for the bond, and the SMARTS for the
     71  # other atom. The canonical bond type is the lexographically smaller of
     72  # the two possible bond types for a bond.
     73  #
     74  # The atom and bond SMARTS depend on the type comparison used.
     75  #
     76  # The "ring-matches-ring-only" option adds an "@" or "!@" to the bond
     77  # SMARTS, so that the canonical bondtype for "C-C" becomes [#6]-@[#6] or
     78  # [#6]-!@[#6] if the bond is in a ring or not in a ring, and if atoms
     79  # are compared by element and bonds are compared by bondtype. (This
     80  # option does not add "R" or "!R" to the atom SMARTS because there
     81  # should be a single bond in the MCS of c1ccccc1O and CO.)
     82  #
     83  # The result of all of this atom and bond typing is a "TypedMolecule"
     84  # for each input structure.
     85  #
     86  # I then find which canonical bondtypes are present in all of the
     87  # structures. I convert each TypedMolecule into a
     88  # FragmentedTypedMolecule which has the same atom information but only
     89  # those bonds whose bondtypes are in all of the structures. This can
     90  # break a structure into multiple, disconnected fragments, hence the
     91  # name.
     92  #
     93  # (BTW, I would like to use the fragmented molecules as the targets
     94  # because I think the SMARTS match would go faster, but the RDKit SMARTS
     95  # matcher doesn't like them. I think it's because the new molecule
     96  # hasn't been sanitized and the underlying data structure the ring
     97  # information doesn't exist. Instead, I use the input structures for the
     98  # SMARTS match.)
     99  #
    100  # == Use the structure with the smallest largest fragment as the query ==
    101  # == and sort the targets by the smallest largest fragment             ==
    102  #
    103  # I pick one of the FragmentedTypedMolecule instances as the source of
    104  # substructure enumeration. Which one?
    105  #
    106  # My heuristic is to use the one with the smallest largest fragment.
    107  # Hopefully it produces the least number of subgraphs, but that's also
    108  # related to the number of rings, so a large linear graph will product
    109  # fewer subgraphs than a small fused ring system. I don't know how to
    110  # quantify that.
    111  #
    112  # For each of the fragmented structures, I find the number of atoms in
    113  # the fragment with the most atoms, and I find the number of bonds in
    114  # the fragment with the most bonds. These might not be the same
    115  # fragment.
    116  #
    117  # I sort the input structures by the number of bonds in the largest
    118  # fragment, with ties broken first on the number of atoms, and then on
    119  # the input order. The smallest such structure is the query structure,
    120  # and the remaining are the targets.
    121  #
    122  # == Use a breadth-first search and a priority queue to    ==
    123  # == enumerate the fragment subgraphs                      ==
    124  #
    125  # I extract each of the fragments from the FragmentedTypedMolecule into
    126  # a TypedFragment, which I use to make an EnumerationMolecule. An
    127  # enumeration molecule contains a pair of directed edges for each atom,
    128  # which simplifies the enumeration algorithm.
    129  #
    130  # The enumeration algorithm is based around growing a seed. A seed
    131  # contains the current subgraph atoms and bonds as well as an exclusion
    132  # set of bonds which cannot be used for future grown. The initial seed
    133  # is the first bond in the fragment, which may potentially grow to use
    134  # the entire fragment. The second seed is the second bond in the
    135  # fragment, which is excluded from using the first bond in future
    136  # growth. The third seed starts from the third bond, which may not use
    137  # the first or second bonds during growth, and so on.
    138  #
    139  #
    140  # A seed can grow along bonds connected to an atom in the seed but which
    141  # aren't already in the seed and aren't in the set of excluded bonds for
    142  # the seed. If there are no such bonds then subgraph enumeration ends
    143  # for this fragment. Given N bonds there are 2**N-1 possible ways to
    144  # grow, which is just the powerset of the available bonds, excluding the
    145  # no-growth case.
    146  #
    147  # This breadth-first growth takes into account all possibilties of using
    148  # the available N bonds so all of those bonds are added to the exclusion
    149  # set of the newly expanded subgraphs.
    150  #
    151  # For performance reasons, the bonds used for growth are separated into
    152  # 'internal' bonds, which connect two atoms already in the subgraph, and
    153  # 'external' bonds, which lead outwards to an atom not already in the
    154  # subgraph.
    155  #
    156  # Each seed growth can add from 0 to N new atoms and bonds. The goal is
    157  # to maximize the subgraph size so the seeds are stored in a priority
    158  # queue, ranked so the seed with the most bonds is processed first. This
    159  # turns the enumeration into something more like a depth-first search.
    160  #
    161  #
    162  # == Prune seeds which aren't found in all of the structures ==
    163  #
    164  # At each stage of seed growth I check that the new seed exists in all
    165  # of the original structures. (Well, all except the one which I
    166  # enumerate over in the first place; by definition that one will match.)
    167  # If it doesn't match then there's no reason to include this seed or any
    168  # larger seeds made from it.
    169  #
    170  # The check is easy; I turn the subgraph into its corresponding SMARTS
    171  # string and use RDKit's normal SMARTS matcher to test for a match.
    172  #
    173  # There are three ways to generate a SMARTS string: 1) arbitrary, 2)
    174  # canonical, 3) hybrid.
    175  #
    176  # I have not tested #1. During most of the development I assumed that
    177  # SMARTS matches across a few hundred structures would be slow, so that
    178  # the best solution is to generate a *canonical* SMARTS and cache the
    179  # match information.
    180  #
    181  # Well, it turns out that my canonical SMARTS match code takes up most
    182  # of the MCS run-time. If I drop the canonicalization step then the
    183  # code averages about 5-10% faster. This isn't the same as #1 - I still
    184  # do the initial atom assignment based on its neighborhood, which is
    185  # like a circular fingerprint of size 2 and *usually* gives a consistent
    186  # SMARTS pattern, which I can then cache.
    187  #
    188  # However, there are times when the non-canonical SMARTS code is slower.
    189  # Obviously one is if there are a lot of structures, and another if is
    190  # there is a lot of symmetry. I'm still working on characterizing this.
    191  #
    192  #
    193  # == Maximize atoms? or bonds? ==
    194  #
    195  # The above algorithm enumerates all subgraphs of the query and
    196  # identifies those subgraphs which are common to all input structures.
    197  #
    198  # It's trivial then to keep track of the current "best" subgraph, which
    199  # can defined as having the subgraph with the most atoms, or the most
    200  # bonds. Both of those options are implemented.
    201  #
    202  # It would not be hard to keep track of all other subgraphs which are
    203  # the same size.
    204  #
    205  # == complete_ring_only implementation ==
    206  #
    207  # The "complete ring only" option is implemented by first enabling the
    208  # "ring-matches-ring-only" option, as otherwise it doesn't make sense.
    209  #
    210  # Second, in order to be a "best" subgraph, all bonds in the subgraph
    211  # which are ring bonds in the original molecule must also be in a ring
    212  # in the subgraph. This is handled as a post-processing step.
    213  #
    214  # (Note: some possible optimizations, like removing ring bonds from
    215  # structure fragments which are not in a ring, are not yet implemented.)
    216  #
    217  #
    218  # == Prune seeds which have no potential for growing large enough  ==
    219  #
    220  # Given a seed, its set of edges available for growth, and the set of
    221  # excluded bonds, figure out the maximum possible growth for the seed.
    222  # If this maximum possible is less than the current best subgraph then
    223  # prune.
    224  #
    225  # This requires a graph search, currently done in Python, which is a bit
    226  # expensive. To speed things up, I precompute some edge information.
    227  # That is, if I know that a given bond is a chain bond (not in a ring)
    228  # then I can calculate the maximum number of atoms and bonds for seed
    229  # growth along that bond, in either direction. However, precomputation
    230  # doesn't take into account the excluded bonds, so after a while the
    231  # predicted value is too high.
    232  #
    233  # Again, I'm still working on characterizing this, and an implementation
    234  # in C++ would have different tradeoffs.
    235
    236  __all__ = ["FindMCS"]
    237
    238
    239  ########## Main driver for the MCS code
    240
    241 -class MCSResult(object):
    242 -   def __init__(self, obj):
    243          self.numAtoms = obj.num_atoms
    244          self.numBonds = obj.num_bonds
    245          self.smarts = obj.smarts
    246          self.completed = obj.completed
    247 -   def __nonzero__(self):
    248          return self.smarts is not None
    249 -   def __repr__(self):
    250         return "MCSResult(numAtoms=%d, numBonds=%d, smarts=%r, completed=%d)" % (
    251             self.numAtoms, self.numBonds, self.smarts, self.completed)
    252 -   def __str__(self):
    253         msg = "MCS %r has %d atoms and %d bonds" % (self.smarts, self.numAtoms, self.numBonds)
    254         if not self.completed:
    255             msg += " (timed out)"
    256         return msg
    257
    258
    259
    260 -def FindMCS(mols, minNumAtoms=2,
    261              maximize = Default.maximize,
    262              atomCompare = Default.atomCompare,
    263              bondCompare = Default.bondCompare,
    264              matchValences = Default.matchValences,
    265              ringMatchesRingOnly = False,
    266              completeRingsOnly = False,
    267              timeout=Default.timeout,
    268              threshold=None,
    269              ):
    270      """Find the maximum common substructure of a set of molecules
    271
    272      In the simplest case, pass in a list of molecules and get back
    273      an MCSResult object which describes the MCS:
    274
    275      >>> from rdkit import Chem
    276      >>> mols = [Chem.MolFromSmiles("C#CCP"), Chem.MolFromSmiles("C=CCO")]
    277      >>> from rdkit.Chem import MCS
    278      >>> MCS.FindMCS(mols)
    279      MCSResult(numAtoms=2, numBonds=1, smarts='[#6]-[#6]', completed=1)
    280
    281      The SMARTS '[#6]-[#6]' matches the largest common substructure of
    282      the input structures. It has 2 atoms and 1 bond. If there is no
    283      MCS which is at least `minNumAtoms` in size then the result will set
    284      numAtoms and numBonds to -1 and set smarts to None.
    285
    286      By default, two atoms match if they are the same element and two
    287      bonds match if they have the same bond type. Specify `atomCompare`
    288      and `bondCompare` to use different comparison functions, as in:
    289
    290      >>> MCS.FindMCS(mols, atomCompare="any")
    291      MCSResult(numAtoms=3, numBonds=2, smarts='[*]-[*]-[*]', completed=1)
    292      >>> MCS.FindMCS(mols, bondCompare="any")
    293      MCSResult(numAtoms=3, numBonds=2, smarts='[#6]~[#6]~[#6]', completed=1)
    294
    295      An atomCompare of "any" says that any atom matches any other atom,
    296      "elements" compares by element type, and "isotopes" matches based on
    297      the isotope label. Isotope labels can be used to implement user-defined
    298      atom types. A bondCompare of "any" says that any bond matches any
    299      other bond, and "bondtypes" says bonds are equivalent if and only if
    300      they have the same bond type.
    301
    302      A substructure has both atoms and bonds. The default `maximize`
    303      setting of "atoms" finds a common substructure with the most number
    304      of atoms. Use maximize="bonds" to maximize the number of bonds.
    305      Maximizing the number of bonds tends to maximize the number of rings,
    306      although two small rings may have fewer bonds than one large ring.
    307
    308      You might not want a 3-valent nitrogen to match one which is 5-valent.
    309      The default `matchValences` value of False ignores valence information.
    310      When True, the atomCompare setting is modified to also require that
    311      the two atoms have the same valency.
    312
    313      >>> MCS.FindMCS(mols, matchValences=True)
    314      MCSResult(numAtoms=2, numBonds=1, smarts='[#6v4]-[#6v4]', completed=1)
    315
    316      It can be strange to see a linear carbon chain match a carbon ring,
    317      which is what the `ringMatchesRingOnly` default of False does. If
    318      you set it to True then ring bonds will only match ring bonds.
    319
    320      >>> mols = [Chem.MolFromSmiles("C1CCC1CCC"), Chem.MolFromSmiles("C1CCCCCC1")]
    321      >>> MCS.FindMCS(mols)
    322      MCSResult(numAtoms=7, numBonds=6, smarts='[#6]-[#6]-[#6]-[#6]-[#6]-[#6]-[#6]', completed=1)
    323      >>> MCS.FindMCS(mols, ringMatchesRingOnly=True)
    324      MCSResult(numAtoms=4, numBonds=3, smarts='[#6](-@[#6])-@[#6]-@[#6]', completed=1)
    325
    326      You can further restrict things and require that partial rings
    327      (as in this case) are not allowed. That is, if an atom is part of
    328      the MCS and the atom is in a ring of the entire molecule then
    329      that atom is also in a ring of the MCS. Set `completeRingsOnly`
    330      to True to toggle this requirement and also sets ringMatchesRingOnly
    331      to True.
    332
    333      >>> mols = [Chem.MolFromSmiles("CCC1CC2C1CN2"), Chem.MolFromSmiles("C1CC2C1CC2")]
    334      >>> MCS.FindMCS(mols)
    335      MCSResult(numAtoms=6, numBonds=6, smarts='[#6]-1-[#6]-[#6](-[#6])-[#6]-1-[#6]', completed=1)
    336      >>> MCS.FindMCS(mols, ringMatchesRingOnly=True)
    337      MCSResult(numAtoms=5, numBonds=5, smarts='[#6]-@1-@[#6]-@[#6](-@[#6])-@[#6]-@1', completed=1)
    338      >>> MCS.FindMCS(mols, completeRingsOnly=True)
    339      MCSResult(numAtoms=4, numBonds=4, smarts='[#6]-@1-@[#6]-@[#6]-@[#6]-@1', completed=1)
    340
    341      The MCS algorithm will exhaustively search for a maximum common substructure.
    342      Typically this takes a fraction of a second, but for some comparisons this
    343      can take minutes or longer. Use the `timeout` parameter to stop the search
    344      after the given number of seconds (wall-clock seconds, not CPU seconds) and
    345      return the best match found in that time. If timeout is reached then the
    346      `completed` property of the MCSResult will be 0 instead of 1.
    347
    348      >>> mols = [Chem.MolFromSmiles("Nc1ccccc1"*100), Chem.MolFromSmiles("Nc1ccccccccc1"*100)]
    349      >>> MCS.FindMCS(mols, timeout=0.1)
    350      MCSResult(..., completed=0)
    351
    352      (The MCS after 50 seconds contained 511 atoms.)
    353      """
    354      ores= fmcs.fmcs(mols,
    355                 minNumAtoms = minNumAtoms,
    356                 maximize = maximize,
    357                 atomCompare = atomCompare,
    358                 bondCompare = bondCompare,
    359                 threshold = threshold,
    360                 matchValences = matchValences,
    361                 ringMatchesRingOnly = ringMatchesRingOnly,
    362                 completeRingsOnly = completeRingsOnly,
    363                 timeout = timeout,
    364          )
    365      return MCSResult(ores)
    366
    367  #------------------------------------
    368  #
    369  #  doctest boilerplate
    370  #
    371 -def _test():
    372    import doctest,sys
    373    return doctest.testmod(sys.modules["__main__"],
    374                           optionflags=doctest.ELLIPSIS+doctest.NORMALIZE_WHITESPACE)
    375
    376
    377  if __name__ == '__main__':
    378    import sys
    379    failed,tried = _test()
    380    sys.exit(failed)
    381