diff --git a/CHANGELOG.md b/CHANGELOG.md index 90568fa..b9176c1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,20 @@ # CHANGELOG +## Version 1.3.0 - 2024-01-09 + +### Features + +- Adding support for Condensed Graph of Reaction +- Adding support for flagging stereocontrolled reactions +- Adding routines for desalting reactions +- Adding routines for identifying reaction centers +- Adding several dataframe modifying actions +- Adding several reaction modifying actions + +### Trival changes + +- Unifying routines to make and concatenate batches + ## Version 1.2.0 - 2022-11-21 ### Features diff --git a/README.md b/README.md index afa5170..146205d 100644 --- a/README.md +++ b/README.md @@ -106,6 +106,8 @@ Please use ``black`` package for formatting, and follow ``pep8`` style guide. * [@ckannas](https://github.com/ckannas) * [@SGenheden](https://www.github.com/SGenheden) +* [@anniewesterlund](https://www.github.com/anniewesterlund) +* [@CBA087](https://www.github.com/CBA087) * [@EBjerrum](https://www.github.com/EBjerrum) * [@A-Thakkar](https://www.github.com/A-Thakkar) diff --git a/docs/conf.py b/docs/conf.py index 7bea246..2d4856a 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -6,7 +6,7 @@ project = "ReactionUtils" copyright = "2022, Molecular AI group" author = "Molecular AI group" -release = "1.2.0" +release = "1.3.0" extensions = [ "sphinx.ext.autodoc", diff --git a/env-dev.yml b/env-dev.yml index d511378..a4a74e4 100644 --- a/env-dev.yml +++ b/env-dev.yml @@ -3,5 +3,5 @@ channels: - https://conda.anaconda.org/conda-forge - defaults dependencies: - - python>=3.8,<3.10 + - python>=3.9,<3.11 - poetry>=1.1.4,<2.0 diff --git a/poetry.lock b/poetry.lock index 4399764..585c72f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,83 +1,117 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + [[package]] name = "alabaster" -version = "0.7.12" -description = "A configurable sidebar-enabled Sphinx theme" -category = "main" 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"pytest-mypy (>=0.9.1)", "pytest-ruff"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.9,<3.11" +content-hash = "13e7052f89483c07392ae9f47cce1fbff758c47cbf1b371f74f7257724bd389c" diff --git a/pyproject.toml b/pyproject.toml index bc711a8..c2260ea 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "reaction_utils" -version = "1.2.0" +version = "1.3.0" description = "Utilities for working with reactions, reaction templates and template extraction" authors = ["Genheden, Samuel ", "Kannas, Christos "] license = "Apache-2.0" @@ -13,18 +13,20 @@ packages = [ ] [tool.poetry.dependencies] -python = "^3.8" +python = ">=3.9,<3.11" +urllib3 = "<2.0" pandas = "^1.0.0" xxhash = "^2.0.0" rdchiral = "^1.1.0" -PyYAML = "^5.4.1" +PyYAML = "^6.0.1" swifter = "^1.0.9" metaflow = "^2.6.3" py7zr = "^0.18.7" Deprecated = "^1.2.13" -Sphinx = "^5.0.1" wrapt-timeout-decorator = "^1.3.12" rdkit = "^2022.3.3" +cgrtools = "^4.1.35" +scipy = "^1.11.4" [tool.poetry.dev-dependencies] pytest = "^6.2.2" @@ -39,6 +41,7 @@ pre-commit = "^2.10.1" ipython = "^7.21.0" pylint = "^2.14.1" invoke = "^1.7.1" +sphinx = "^4.0.0" [build-system] requires = ["poetry-core>=1.0.0"] diff --git a/rxnutils/chem/cgr.py b/rxnutils/chem/cgr.py new file mode 100644 index 0000000..bff91b4 --- /dev/null +++ b/rxnutils/chem/cgr.py @@ -0,0 +1,141 @@ +""" Wrapper class for the CGRTools library +""" +import io +import warnings +from typing import List + +from CGRtools.files.SDFrw import SDFRead +from CGRtools.containers.reaction import ReactionContainer +from CGRtools.containers.molecule import MoleculeContainer +from rdkit import Chem + +from rxnutils.chem.reaction import ChemicalReaction +from rxnutils.chem.utils import atom_mapping_numbers + + +class CondensedGraphReaction: + """ + The Condensed Graph of Reaction (CGR) representation of a reaction + + :ivar reaction_container: the CGRTools container of the reaction + :ivar cgr_container: the CGRTools container of the CGR + :param reaction: the reaction composed of RDKit molecule to start from + :raises ValueError: if it is not possible to create the CGR from the reaction + """ + + def __init__(self, reaction: ChemicalReaction) -> None: + self.reaction = reaction + self._cgr_reactants = [] + self._cgr_products = [] + self._make_cgr_containers() + self.reaction_container = ReactionContainer( + reactants=self._cgr_reactants, products=self._cgr_products + ) + try: + self.cgr_container = self.reaction_container.compose() + except ValueError as err: + if str(err) == "mapping of graphs is not disjoint": + raise ValueError( + "Reaction contains inconsistent atom-mapping, perhaps duplicates" + ) + elif str(err).endswith("} not equal"): + raise ValueError( + "Atom with the same atom-mapping in reactant and product is not equal" + ) + else: + raise ValueError(f"Unknown problem with generating CGR: {err}") + + def __eq__(self, obj: object) -> bool: + if not isinstance(obj, CondensedGraphReaction): + return False + return self.cgr_container == obj.cgr_container + + @property + def bonds_broken(self) -> int: + """Returns the number of broken bonds in the reaction""" + return sum(bond.p_order is None for _, _, bond in self.cgr_container.bonds()) + + @property + def bonds_changed(self) -> int: + """Returns the number of broken or formed bonds in the reaction""" + return sum( + bond.p_order is None or bond.order is None + for _, _, bond in self.cgr_container.bonds() + ) + + @property + def bonds_formed(self) -> int: + """Returns the number of formed bonds in the reaction""" + return sum(bond.order is None for _, _, bond in self.cgr_container.bonds()) + + @property + def total_centers(self) -> int: + """Returns the number of atom and bond centers in the reaction""" + return len(self.cgr_container.center_atoms) + len( + self.cgr_container.center_bonds + ) + + def distance_to(self, other: "CondensedGraphReaction") -> int: + """ + Returns the chemical distance between two reactions, i.e. the absolute difference + between the total number of centers. + + Used for some atom-mapping comparison statistics + + :param other: the reaction to compare to + :returns: the computed distance + """ + return abs(self.total_centers - other.total_centers) + + def _make_cgr_containers(self): + nreactants = len(self.reaction.reactants) + renumbered_mols = self._make_renumbered_mols() + self._cgr_reactants = _convert_rdkit_molecules(renumbered_mols[:nreactants]) + self._cgr_products = _convert_rdkit_molecules(renumbered_mols[nreactants:]) + + if len(self._cgr_reactants) != nreactants: + warnings.warn("Warning not all reactants could be converted to CGRtools") + if len(self._cgr_products) != len(self.reaction.products): + warnings.warn("Warning not all products could be converted to CGRtools") + + def _make_renumbered_mols(self): + # It is necessary that all atoms have atom-mapping + # otherwise CGRTools will start adding bad atom-mappings + # so this adds safe atom-mapping to un-mapped atoms + renumbered_mols = [] + max_atom_map_numb = max( + max(atom_mapping_numbers(smi) or [0]) + for smi in self.reaction.reactants_list + self.reaction.products_list + ) + for mol0 in self.reaction.reactants + self.reaction.products: + if mol0 is None: + raise ValueError( + "Cannot create CGR for this reaction, some molecules are None" + ) + mol = Chem.rdchem.Mol(mol0) + for atom in mol.GetAtoms(): + if not atom.GetAtomMapNum(): + max_atom_map_numb += 1 + atom.SetAtomMapNum(max_atom_map_numb) + renumbered_mols.append(mol) + return renumbered_mols + + +def _convert_rdkit_molecules( + mols: List[Chem.rdchem.Mol], +) -> List[MoleculeContainer]: + # This connects the SDWriter of RDKit with the SDFRead of CGRTools + with io.StringIO() as sd_buffer: + with Chem.SDWriter(sd_buffer) as sd_writer_obj: + for mol in mols: + sd_writer_obj.write(mol) + sd_buffer.seek(0) + with SDFRead(sd_buffer, remap=False) as sd_reader_obj: + cgr_molecules = sd_reader_obj.read() + + # This make the aromatization of the molecules, which were + # lost when writing to SD format. This is essential to keep + # the reactant and product structures as equal as possible + for mol in cgr_molecules: + mol.thiele() + return cgr_molecules diff --git a/rxnutils/chem/utils.py b/rxnutils/chem/utils.py index 8702570..257fb14 100644 --- a/rxnutils/chem/utils.py +++ b/rxnutils/chem/utils.py @@ -9,6 +9,7 @@ from rdkit import Chem from rdkit.Chem import AllChem from rdkit.Chem.MolStandardize import rdMolStandardize +from rdkit.Chem.SaltRemover import SaltRemover def get_special_groups(mol) -> List[Tuple[Tuple[int, ...], Tuple[int, ...]]]: @@ -336,7 +337,7 @@ def has_atom_mapping( def remove_atom_mapping( - smiles: str, is_smarts: bool = False, sanitize: bool = True + smiles: str, is_smarts: bool = False, sanitize: bool = True, canonical: bool = True ) -> str: """ Returns a molecule without atom mapping @@ -344,6 +345,7 @@ def remove_atom_mapping( :param smiles: the SMILES/SMARTS representing the molecule :param is_smarts: if True, will interpret the SMILES as a SMARTS :param sanitize: if True, will sanitize the molecule + :param canonical: if False, will not canonicalize (applies to SMILES) :return: the molecule without atom-mapping """ if not smiles: @@ -358,7 +360,7 @@ def remove_atom_mapping( # is_smarts=True: Chem.MolToSmarts # is_smarts=False: Chem.MolToSmiles to_func = { - False: Chem.MolToSmiles, + False: functools.partial(Chem.MolToSmiles, canonical=canonical), True: Chem.MolToSmarts, } mol = from_func[is_smarts](smiles) @@ -368,6 +370,7 @@ def remove_atom_mapping( for atom in mol.GetAtoms(): atom.SetAtomMapNum(0) + return to_func[is_smarts](mol) @@ -403,6 +406,35 @@ def neutralize_molecules(smiles_list: List[str]) -> List[str]: return [Chem.MolToSmiles(mol) for mol in neutral_mols] +def desalt_molecules(smiles_list: List[str], keep_something: bool = False) -> List[str]: + """ + Remove salts from a set of molecules using RDKit routines + + :param smiles_list: the molecules as SMILES + :param keep_something: if True will keep at least one salt + :return: the desalted molecules + """ + remover = SaltRemover() # use default saltremover + mols = [Chem.MolFromSmiles(smi) for smi in smiles_list] + desalted_mols = [ + remover.StripMol(mol, dontRemoveEverything=keep_something) + for mol in mols + if mol + ] + if len(mols) > len(desalted_mols): + logging.warning( + f"No. of desalted molecules ({len(desalted_mols)}) are less than No. of input molecules ({len(mols)})." + ) + + smiles_list_new = [Chem.MolToSmiles(mol) for mol in desalted_mols] + smiles_list_new = [smi for smi in smiles_list_new if smi] + if len(smiles_list) > len(smiles_list_new): + logging.warning( + f"No. of desalted SMILES ({len(smiles_list_new)}) are less than No. of input SMILES ({len(smiles_list)})." + ) + return smiles_list_new + + def same_molecule(mol1, mol2) -> bool: """Test if two molecules are the same. First number of atoms and bonds are compared to guard the potentially more expensive @@ -501,6 +533,17 @@ def reassign_rsmi_atom_mapping(rsmi: str, as_smiles: bool = False) -> str: return updated_rsmi +def join_smiles_from_reaction(smiles_list: List[str]) -> str: + """ + Join a part of reaction SMILES, e.g. reactants and products into components. + Intra-molecular complexes are bracketed with parenthesis + + :param smiles_list: the SMILES components + :return: the joined list + """ + return ".".join([f"({item})" if "." in item else item for item in smiles_list]) + + def split_smiles_from_reaction(smiles: str) -> List[str]: """ Split a part of reaction SMILES, e.g. reactants or products @@ -540,3 +583,73 @@ def split_smiles_from_reaction(smiles: str) -> List[str]: else: components.append(smiles[block_start:pos]) return components + + +def reaction_centres(rxn: AllChem.ChemicalReaction) -> Tuple[List[int], ...]: + """ + Return reaction centre atoms, provided that the bonding partners + actually change when comparing the environment in the reactant and the product + + inspired by code from Greg Landrum's tutorial + set up array to remove atoms from the reaction centers + by comparing the atom mapping in the reactant vs the products + + Original implementation by Christoph Bauer + + :param rxn: the initialized RDKit reaction + :return: tuple of reaction centre atoms, filtered by connectivity criterion + """ + rxncenters = rxn.GetReactingAtoms(mappedAtomsOnly=True) + + remove_array = [] + nreactants = len(rxncenters) + for ridx, reacting in enumerate(rxncenters): + reactant = rxn.GetReactantTemplate(ridx) + for raidx in reacting: + ratm = reactant.GetAtomWithIdx(raidx) + mapnum = ratm.GetAtomMapNum() + numexplicitH_reactant = ratm.GetNumExplicitHs() + neighbours = ratm.GetNeighbors() + map_neighbours = sorted( + [neighbour_atom.GetAtomMapNum() for neighbour_atom in neighbours] + ) + foundit = False + for product in rxn.GetProducts(): + for patom in product.GetAtoms(): + if patom.GetAtomMapNum() == mapnum: + neighbours_product = patom.GetNeighbors() + numexplicitH_product = patom.GetNumExplicitHs() + map_neighbours_product = sorted( + [ + neighbour_atom.GetAtomMapNum() + for neighbour_atom in neighbours_product + ] + ) + # criterion: check if the map numbers of neighbours are the same in the reactant and the product + if map_neighbours == map_neighbours_product: + if numexplicitH_reactant == numexplicitH_product: + # if yes --> then the environment doesn't change, so set remove to True + remove_array.append(True) + else: + remove_array.append(False) + else: + # False means that the environment does change, so set remove to False + remove_array.append(False) + foundit = True + break + if foundit: + break + + # actual removal of reaction centres by generating new tuple + rxncenters_filtered = [] + counter = 0 + for reactant_idx in range(nreactants): + reactantcenters = [] + for index in rxncenters[reactant_idx]: + if not remove_array[counter]: + reactantcenters.append(index) + counter += 1 + reactantcenters = tuple(reactantcenters) + rxncenters_filtered.append(reactantcenters) + rxncenters_filtered = tuple(rxncenters_filtered) + return rxncenters_filtered diff --git a/rxnutils/data/base_pipeline.py b/rxnutils/data/base_pipeline.py index 1c9b2d7..91df7ae 100644 --- a/rxnutils/data/base_pipeline.py +++ b/rxnutils/data/base_pipeline.py @@ -7,6 +7,10 @@ import pandas as pd from metaflow import FlowSpec, Parameter +from typing import List, Tuple + +from rxnutils.data.batch_utils import create_csv_batches, combine_csv_batches + # This is hack to only import the validation_runner if rxnmapper is not installed try: import rxnmapper # noqa @@ -14,50 +18,23 @@ from rxnutils.pipeline.runner import main as validation_runner -def nlines(filename: str) -> int: - """Count and return the number of lines in a file""" - with open(filename, "rb") as fileobj: - return sum(1 for line in fileobj) - - class DataBaseFlow(FlowSpec): """Base-class for pipelines for processing data""" nbatches = Parameter("nbatches", type=int, required=True) folder = Parameter("folder", default=".") - def _combine_batches(self, filename): - if Path(filename).exists(): - return + def _combine_batches(self, filename: str) -> None: + combine_csv_batches(filename, self.nbatches) - data = None - for idx in range(self.nbatches): - filename2 = f"{filename}.{idx}" - data_temp = pd.read_csv(filename2, sep="\t") - if data is None: - data = data_temp - else: - data = pd.concat([data, data_temp]) - os.remove(filename2) - data.to_csv(filename, index=False, sep="\t") - - def _create_batches(self, input_filename, output_filename): - if Path(output_filename).exists(): - return [(-1, None, None)] - - file_size = ( - nlines(input_filename) - 1 - ) # Header should not be counted for chunk size calculations - chunk_size = math.ceil(file_size / self.nbatches) - partition_limits = [ - ( - idx, - idx * chunk_size + 1, - (idx + 1) * chunk_size + 1, - ) # +1 to account for header - for idx in range(self.nbatches) - ] - return partition_limits + def _create_batches( + self, input_filename: str, output_filename: str + ) -> List[Tuple[int, int, int]]: + return create_csv_batches( + input_filename, + self.nbatches, + output_filename, + ) class DataPreparationBaseFlow(DataBaseFlow): diff --git a/rxnutils/data/batch_utils.py b/rxnutils/data/batch_utils.py new file mode 100644 index 0000000..c62a036 --- /dev/null +++ b/rxnutils/data/batch_utils.py @@ -0,0 +1,136 @@ +import os +from pathlib import Path +from typing import Any, List, Optional, Tuple + +import pandas as pd +from scipy import sparse + + +def nlines(filename: str) -> int: + """Count and return the number of lines in a file""" + with open(filename, "rb") as fileobj: + return sum(1 for line in fileobj) + + +def combine_batches( + filename: str, + nbatches: int, + read_func: Any, + write_func: Any, + combine_func: Any, +) -> None: + if Path(filename).exists(): + return + + data = None + for idx in range(nbatches): + temp_data, filename2 = read_func(filename, idx) + if data is None: + data = temp_data + else: + data = combine_func(data, temp_data) + os.remove(filename2) + write_func(data, filename) + + +def combine_csv_batches(filename: str, nbatches: int) -> None: + """ + Combine CSV batches to one master file + + The batch files are removed from disc + + :param filename: the filename of the master file + :param nbatches: the number of batches + """ + + def _combine_csv(data: pd.DataFrame, temp_data: pd.DataFrame) -> pd.DataFrame: + return pd.concat([data, temp_data]) + + def _read_csv(filename: str, idx: int) -> pd.DataFrame: + filename2 = f"{filename}.{idx}" + return pd.read_csv(filename2, sep="\t"), filename2 + + def _write_csv(data: pd.DataFrame, filename: str) -> None: + data.to_csv(filename, index=False, sep="\t") + + combine_batches(filename, nbatches, _read_csv, _write_csv, _combine_csv) + + +def combine_sparse_matrix_batches(filename: str, nbatches: int) -> None: + """ + Combine sparse matrix batches to one master file + + The batch files are removed from disc + + :param filename: the filename of the master file + :param nbatches: the number of batches + """ + + def _read_matrix(filename: str, idx: int) -> Any: + filename2 = filename.replace(".npz", f".{idx}.npz") + return sparse.load_npz(filename2), filename2 + + def _write_matrix(data: Any, filename: str) -> None: + sparse.save_npz(filename, data, compressed=True) + + def _combine_matrix(data: pd.DataFrame, temp_data: pd.DataFrame) -> pd.DataFrame: + return sparse.vstack([data, temp_data]) + + combine_batches(filename, nbatches, _read_matrix, _write_matrix, _combine_matrix) + + +def create_csv_batches( + filename: str, nbatches: int, output_filename: Optional[str] = None +) -> List[Tuple[int, int, int]]: + """ + Create batches for reading a splitted CSV-file + + The batches will be in the form of a tuple with three indices: + * Batch index + * Start index + * End index + + :param filename: the CSV file to make batches of + :param nbatches: the number of batches + :param output_filename: + :return: the created batches + """ + if output_filename and Path(output_filename).exists(): + return [(-1, None, None)] + + file_size = ( + nlines(filename) - 1 + ) # Header should not be counted for batch size calculations + batch_size, remainder = divmod(file_size, nbatches) + stop = 1 # 1-indexed to account for header in the .csv file + batches = [] + for partition_idx in range(1, nbatches + 1): + start = stop + stop += batch_size + 1 if partition_idx <= remainder else batch_size + batches.append((partition_idx - 1, start, stop)) + return batches + + +def read_csv_batch( + filename: str, batch: Tuple[int, ...] = None, **kwargs: Any +) -> pd.DataFrame: + """ + Read parts of a CSV file as specified by a batch + + :param filename: the path to the CSV file on disc + :param batch: the batch specification as returned by `create_csv_batches` + """ + if batch is None: + return pd.read_csv(filename, **kwargs) + if len(batch) == 3: + _, batch_start, batch_end = batch + elif len(batch) == 2: + batch_start, batch_end = batch + else: + raise ValueError(f"The batch specification can only be 2 or 3 not {len(batch)}") + return pd.read_csv( + filename, + nrows=batch_end - batch_start, + skiprows=range(1, batch_start), + **kwargs, + ) diff --git a/rxnutils/data/mapping.py b/rxnutils/data/mapping.py index 0aa4cb4..9be0d6e 100644 --- a/rxnutils/data/mapping.py +++ b/rxnutils/data/mapping.py @@ -5,6 +5,8 @@ import pandas as pd +from rxnutils.data.batch_utils import read_csv_batch + try: from rxnmapper import RXNMapper # pylint: disable=all except ImportError: @@ -32,6 +34,11 @@ def main(input_args: Optional[Sequence[str]] = None) -> None: nargs=2, help="Line numbers to start and stop reading rows", ) + parser.add_argument( + "--column", + default="ReactionSmilesClean", + help="The column with the reaction SMILES", + ) parser.add_argument( "--mapper_batch_size", type=int, @@ -40,25 +47,12 @@ def main(input_args: Optional[Sequence[str]] = None) -> None: ) args = parser.parse_args(input_args) - if args.batch: - start, end = args.batch - data = pd.read_csv( - args.input, - sep="\t", - nrows=end - start, - skiprows=range(1, start), - ) - else: - data = pd.read_csv(args.input, sep="\t") + data = read_csv_batch(args.input, sep="\t", index_col=False, batch=args.batch) rxn_mapper = RXNMapper() mapped_data = [] for start in range(0, len(data), args.mapper_batch_size): - batch = ( - data["ReactionSmilesClean"] - .iloc[start : start + args.mapper_batch_size] - .tolist() - ) + batch = data[args.column].iloc[start : start + args.mapper_batch_size].tolist() fail_data = [ { "mapped_rxn": None, diff --git a/rxnutils/pipeline/actions/dataframe_mod.py b/rxnutils/pipeline/actions/dataframe_mod.py index aa3f438..06c70c4 100644 --- a/rxnutils/pipeline/actions/dataframe_mod.py +++ b/rxnutils/pipeline/actions/dataframe_mod.py @@ -1,10 +1,12 @@ """Module containing actions that modify the dataframe in some way""" from __future__ import annotations + from dataclasses import dataclass from typing import ClassVar import pandas as pd from rdkit import RDLogger + from rxnutils.pipeline.base import action rd_logger = RDLogger.logger() @@ -49,6 +51,32 @@ def __str__(self) -> str: return f"{self.pretty_name} (remove duplicates based on the provided list of columns for comparison)" +@action +@dataclass +class DropRows: + """Drops rows according to boolean in 'indicator_columns'. + True => Keep, False => Drop. + + yaml example: + + drop_rows: + indicator_columns: + - is_sanitizable + - is_sanitizable2 + """ + + pretty_name: ClassVar[str] = "drop_rows" + indicator_columns: list[str] + + def __call__(self, data: pd.DataFrame) -> pd.DataFrame: + for column in self.indicator_columns: + data = data.iloc[data[column].values] + return data + + def __str__(self) -> str: + return f"{self.pretty_name} (drop rows according to booleans specified in 'indicator_columns')" + + @action @dataclass class KeepColumns: @@ -77,6 +105,34 @@ def __str__(self) -> str: return f"{self.pretty_name} (keeps columns in specified list, drops the rest)" +@action +@dataclass +class RenameColumns: + """Renames columns specified in 'in_columns' to the names specified in 'out_columns' + + yaml example: + + rename_columns: + in_columns: + - column1 + - column2 + out_columns: + - column1_renamed + - column2_renamed + """ + + pretty_name: ClassVar[str] = "rename_columns" + in_columns: list[str] + out_columns: list[str] + + def __call__(self, data: pd.DataFrame) -> pd.DataFrame: + rename_mapper = dict(zip(self.in_columns, self.out_columns)) + return data.rename(columns=rename_mapper) + + def __str__(self) -> str: + return f"{self.pretty_name} (renames the specified columns)" + + @action @dataclass class QueryDataframe: diff --git a/rxnutils/pipeline/actions/reaction_mod.py b/rxnutils/pipeline/actions/reaction_mod.py index 5dbb9f8..4b4eeb9 100644 --- a/rxnutils/pipeline/actions/reaction_mod.py +++ b/rxnutils/pipeline/actions/reaction_mod.py @@ -2,19 +2,25 @@ from __future__ import annotations import os +import re import subprocess import sys import tempfile -from dataclasses import dataclass +from dataclasses import dataclass, field +from pathlib import Path from typing import ClassVar, List, Tuple import pandas as pd from rdkit import Chem, RDLogger from rdkit.Chem import RDConfig + from rxnutils.chem.utils import ( atom_mapping_numbers, neutralize_molecules, remove_atom_mapping, + desalt_molecules, + split_smiles_from_reaction, + join_smiles_from_reaction, ) from rxnutils.pipeline.base import ReactionActionMixIn, action, global_apply @@ -26,13 +32,47 @@ f"{RDConfig.RDContribDir}/RxnRoleAssignment" ) # to make Nadines code import "utils" # fmt: off - from RxnRoleAssignment.identifyReactants import reassignReactionRoles # pylint: disable=all # noqa + from RxnRoleAssignment.identifyReactants import \ + reassignReactionRoles # pylint: disable=all # noqa + # fmt: on rd_logger = RDLogger.logger() rd_logger.setLevel(RDLogger.CRITICAL) +@action +@dataclass +class DesaltMolecules(ReactionActionMixIn): + """Action for desalting molecules""" + + pretty_name: ClassVar[str] = "desalt_molecules" + in_column: str + out_column: str = "RxnDesalted" + keep_something: bool = False + + def __call__(self, data: pd.DataFrame) -> pd.DataFrame: + smiles_col = global_apply(data, self._apply_row, axis=1) + return data.assign(**{self.out_column: smiles_col}) + + def _apply_row(self, row: pd.Series) -> pd.Series: + reactants_list, reagents_list, products_list = self.split_lists(row) + reactants_list = desalt_molecules( + reactants_list, keep_something=self.keep_something + ) + if reagents_list: + reagents_list = desalt_molecules( + reagents_list, keep_something=self.keep_something + ) + products_list = desalt_molecules( + products_list, keep_something=self.keep_something + ) + return self.join_lists(reactants_list, reagents_list, products_list) + + def __str__(self) -> str: + return f"{self.pretty_name} (desalt molecules using RDKit SaltRemover)" + + @action @dataclass class NameRxn: @@ -220,6 +260,32 @@ def _row_action(self, row: pd.Series) -> str: return smiles_temp.replace("$", "@@").replace("£", "@") +@action +@dataclass +class IsotopeInfo: + """Action creating and modifying isotope information""" + + pretty_name: ClassVar[str] = "isotope_info" + in_column: str + isotope_column: str = "Isotope" + out_column: str = "RxnSmilesWithoutIsotopes" + match_regex: str = ( + r"\[(?P[0-9]+)(?P[A-Za-z][a-z]*)(?P[^\]]+)*\]" + ) + sub_regex: str = r"[\g\g]" + + def __call__(self, data: pd.DataFrame) -> pd.DataFrame: + isotop_info = data[self.in_column].str.extract(self.match_regex) + isotop_col = isotop_info.mass + isotop_info.symbol + rsmi_col = data[self.in_column].str.replace(self.match_regex, self.sub_regex) + return data.assign( + **{self.isotope_column: isotop_col, self.out_column: rsmi_col} + ) + + def __str__(self) -> str: + return f"{self.pretty_name} (extract and remove isotope information from reactions)" + + @action @dataclass class RemoveExtraAtomMapping(ReactionActionMixIn): @@ -385,6 +451,58 @@ def __str__(self) -> str: return f"{self.pretty_name} (reaction role assignment using RDKit contrib (Nadine and Greg's))" +# fmt: off +_MAP_SCRIPT = Path(__file__).parent.parent.parent / "data" / "mapping.py" +# fmt: on + + +@action +@dataclass +class RxnMapper: + """Action for mapping reactions with the RXNMapper tool""" + + pretty_name: ClassVar[str] = "rxnmapper" + in_column: str + out_column: str = "RxnmapperRxnSmiles" + rxnmapper_command: str = "conda run -p ${RXNMAPPER_ENV_PATH} python " + str( + _MAP_SCRIPT + ) + + def __call__(self, data: pd.DataFrame) -> pd.DataFrame: + new_column = self._do_action(data[self.in_column]) + return data.assign(**{self.out_column: new_column}) + + def _do_action(self, column: pd.Series) -> pd.Series: + input_handle, input_path = tempfile.mkstemp(suffix=".csv") + output_handle, output_path = tempfile.mkstemp(suffix=".csv") + column.to_csv(input_path, sep="\t", index=False) + + cmd = self.rxnmapper_command + if "CONDA_PATH" in os.environ: + cmd = os.environ["CONDA_PATH"] + os.sep + cmd + arglist = cmd.split() + [ + "--input", + input_path, + "--column", + self.in_column, + "--output", + output_path, + ] + _ = subprocess.check_output(arglist).decode("utf-8") + + mapped_data = pd.read_csv(output_path, sep="\t") + + os.remove(input_path) + os.remove(output_path) + os.close(input_handle) + os.close(output_handle) + + return mapped_data["mapped_rxn"] + + def __str__(self) -> str: + return f"{self.pretty_name} (RFP by rxnfp)" + + @action @dataclass class SplitReaction(ReactionActionMixIn): diff --git a/rxnutils/pipeline/actions/reaction_props.py b/rxnutils/pipeline/actions/reaction_props.py index 162d406..aeb6ce8 100644 --- a/rxnutils/pipeline/actions/reaction_props.py +++ b/rxnutils/pipeline/actions/reaction_props.py @@ -2,22 +2,32 @@ from __future__ import annotations import json +import os from collections import defaultdict -from typing import ClassVar, Tuple, List, Set - from dataclasses import dataclass +from typing import ClassVar, List, Set, Tuple, Optional + import pandas as pd -from rdkit import RDLogger -from rdkit import Chem +from rdkit import Chem, RDLogger +from rdkit.Chem import AllChem, Descriptors +from rdkit.Chem.MolKey.InchiInfo import InchiInfo from rdkit.Chem.rdMolDescriptors import CalcNumRings -from rdkit.Chem import Descriptors +from rdkit.Chem.rdmolops import FindPotentialStereo -from rxnutils.pipeline.base import action, global_apply, ReactionActionMixIn +from rxnutils.pipeline.base import ( + action, + global_apply, + ReactionActionMixIn, +) from rxnutils.chem.utils import ( + atom_mapping_numbers, has_atom_mapping, + join_smiles_from_reaction, + reaction_centres, split_smiles_from_reaction, - atom_mapping_numbers, ) +from rxnutils.chem.reaction import ChemicalReaction +from rxnutils.chem.cgr import CondensedGraphReaction rd_logger = RDLogger.logger() rd_logger.setLevel(RDLogger.CRITICAL) @@ -213,6 +223,56 @@ def _process_bad_column(smiles_list: str) -> Set[int]: return unsanitize_atom_map_num +@action +@dataclass +class CgrCreated: + """Action for determining if a CGR can be created from the reaction smiles""" + + in_column: str + pretty_name: ClassVar[str] = "cgr_created" + out_column: str = "CGRCreated" + + def __call__(self, data: pd.DataFrame) -> pd.DataFrame: + out_col = global_apply(data, self._create_cgr_column, axis=1) + return data.assign(**{self.out_column: out_col}) + + def __str__(self) -> str: + return f"{self.pretty_name} (flag if a CGR can be created for the reaction)" + + def _create_cgr_column(self, row: pd.Series) -> bool: + rxn = ChemicalReaction(row[self.in_column], clean_smiles=False) + try: + _ = CondensedGraphReaction(rxn) + except Exception: + return False + return True + + +@action +@dataclass +class CgrNumberOfDynamicBonds: + """Action for calculating the number of dynamic bonds""" + + in_column: str + pretty_name: ClassVar[str] = "cgr_dynamic_bonds" + out_column: str = "NDynamicBonds" + + def __call__(self, data: pd.DataFrame) -> pd.DataFrame: + out_col = global_apply(data, self._process_row, axis=1) + return data.assign(**{self.out_column: out_col}) + + def __str__(self) -> str: + return f"{self.pretty_name} (number of dynamic bonds in the CGR)" + + def _process_row(self, row: pd.Series) -> Optional[int]: + rxn = ChemicalReaction(row[self.in_column], clean_smiles=False) + try: + cgr = CondensedGraphReaction(rxn) + except Exception: + return None + return cgr.bonds_changed + + @action @dataclass class ProductAtomMappingStats: @@ -400,6 +460,40 @@ def _row_action(self, row: pd.Series) -> str: return reactant_atom_count +@action +@dataclass +class MaxRingNumber: + """ + Action for calculating the maximum number of rings in either the product or reactant + For a reaction without reactants or products, it will return 0 to enable easy arithmetic comparison + """ + + pretty_name: ClassVar[str] = "maxrings" + in_column: str + out_column: str = "MaxRings" + + def __call__(self, df: pd.DataFrame) -> pd.DataFrame: + smiles_col = global_apply(df, self._row_action, axis=1) + return df.assign(**{self.out_column: smiles_col}) + + def __str__(self) -> str: + return f"{self.pretty_name} (maximum number of rings)" + + def _row_action(self, row: pd.Series) -> str: + reactants, _, products = row[self.in_column].split(">") + + reactants_mols = [Chem.MolFromSmiles(smi) for smi in reactants.split(".")] + nrings_reactants = [CalcNumRings(mol) for mol in reactants_mols if mol] + max_rings_reactants = 0 if not nrings_reactants else max(nrings_reactants) + + prod_mol = Chem.MolFromSmiles(products) + if not prod_mol: + return max_rings_reactants + + nrings_product = CalcNumRings(prod_mol) + return max([nrings_product, max_rings_reactants]) + + @action @dataclass class RingNumberChange: @@ -579,6 +673,9 @@ def __str__(self) -> str: def _row_action(self, row: pd.Series) -> str: smiles = row[self.in_column] + if not smiles: + return False + mol = Chem.MolFromSmiles(smiles) try: # Extra check that the molecule can generate SMILES that are again parsable mol = Chem.MolFromSmiles(Chem.MolToSmiles(mol)) @@ -616,3 +713,283 @@ def _row_action(self, row: pd.Series) -> bool: reactants, _, products = row[self.in_column].split(">") return self._has_stereo(products) and not self._has_stereo(reactants) + + +@action +@dataclass +class StereoCentreChanges: + """ + Action for checking if stereogenic centre in reaction center is changing + during the reaction + + Will create two columns: + 1. A boolean column indicating True or False if it has stereochanges + 2. A description of the stereo information before and after the reaction + """ + + pretty_name: ClassVar[str] = "stereo_centre_changes" + in_column: str + out_column: str = "HasStereoChanges" + stereo_changes_column: str = "StereoChanges" + + def __call__(self, data: pd.DataFrame) -> pd.DataFrame: + new_df = global_apply(data, self._row_action, axis=1) + return data.assign(**{col: new_df[col] for col in new_df.columns}) + + def __str__(self) -> str: + return f"{self.pretty_name} (check if stereochemistry changes in reaction)" + + def _row_action(self, row: pd.Series) -> str: + smiles = row[self.in_column] + rdkit_rxn = AllChem.ReactionFromSmarts(smiles, useSmiles=True) + rdkit_rxn.Initialize() + rxncenters = reaction_centres(rdkit_rxn) + + reactants = rdkit_rxn.GetReactants() + reactants_chiral_flags = {} + for centers, reactant in zip(rxncenters, reactants): + for index in centers: + atom = reactant.GetAtomWithIdx(index) + reactants_chiral_flags[atom.GetAtomMapNum()] = str(atom.GetChiralTag()) + + product_chiral_flags = {} + for atom in rdkit_rxn.GetProducts()[0].GetAtoms(): + if atom.GetAtomMapNum() in reactants_chiral_flags: + product_chiral_flags[atom.GetAtomMapNum()] = str(atom.GetChiralTag()) + + if reactants_chiral_flags == product_chiral_flags: + return pd.Series({self.out_column: False, self.stereo_changes_column: None}) + + output = {} + for atom_map, reactant_chirality in reactants_chiral_flags.items(): + product_chirality = product_chiral_flags.get(atom_map, None) + if product_chirality != reactant_chirality: + output[atom_map] = (reactant_chirality, product_chirality) + return pd.Series( + {self.out_column: True, self.stereo_changes_column: json.dumps(output)} + ) + + +@action +@dataclass +class StereoHasChiralReagent: + """ + Action for checking if reagent has stereo centres + """ + + pretty_name: ClassVar[str] = "stereo_chiral_reagent" + in_column: str + out_column: str = "HasChiralReagent" + + def __call__(self, data: pd.DataFrame) -> pd.DataFrame: + if len(data) == 0: + return data + out_col = global_apply(data, self._row_action, axis=1) + return data.assign(**{self.out_column: out_col}) + + def __str__(self) -> str: + return f"{self.pretty_name} (check if reagent is chiral)" + + def _row_action(self, row: pd.Series) -> bool: + smiles = row[self.in_column] + return "@" in smiles.split(">")[1] + + +@action +@dataclass +class StereoCenterIsCreated: + """ + Action for checking if stereo centre is created during reaction + """ + + pretty_name: ClassVar[str] = "stereo_centre_created" + in_column: str = "StereoChanges" + out_column: str = "StereoCentreCreated" + + def __call__(self, data: pd.DataFrame) -> pd.DataFrame: + if len(data) == 0: + return data + out_col = global_apply(data, self._row_action, axis=1) + return data.assign(**{self.out_column: out_col}) + + def __str__(self) -> str: + return f"{self.pretty_name} (check if stereo centre is created)" + + def _row_action(self, row: pd.Series) -> str: + try: + chirality_changes = json.loads(row[self.in_column]) + except TypeError: + return False + for reactant_chirality, _ in chirality_changes.values(): + if reactant_chirality == "CHI_UNSPECIFIED": + return True + return False + + +@action +@dataclass +class StereoCenterIsRemoved: + """ + Action for checking if stereo centre is removed during reaction + """ + + pretty_name: ClassVar[str] = "stereo_centre_removed" + in_column: str = "StereoChanges" + out_column: str = "StereoCentreRemoved" + + def __call__(self, data: pd.DataFrame) -> pd.DataFrame: + if len(data) == 0: + return data + out_col = global_apply(data, self._row_action, axis=1) + return data.assign(**{self.out_column: out_col}) + + def __str__(self) -> str: + return f"{self.pretty_name} (check if stereo centre is removed)" + + def _row_action(self, row: pd.Series) -> str: + try: + chirality_changes = json.loads(row[self.in_column]) + except TypeError: + return False + for _, product_chirality in chirality_changes.values(): + if product_chirality == "CHI_UNSPECIFIED": + return True + return False + + +@action +@dataclass +class StereoCenterInReactantPotential: + """ + Action for checking if there is a potential stereo centre in the reaction + + Do not consider changes to bond stereochemistry + """ + + pretty_name: ClassVar[str] = "potential_stereo_center" + in_column: str + out_column: str = "PotentialStereoCentre" + + def __call__(self, data: pd.DataFrame) -> pd.DataFrame: + if len(data) == 0: + return data + out_col = global_apply(data, self._row_action, axis=1) + return data.assign(**{self.out_column: out_col}) + + def __str__(self) -> str: + return f"{self.pretty_name} (check for existence of potential stereo centre)" + + def _row_action(self, row: pd.Series) -> str: + smiles = row[self.in_column] + rdkit_rxn = AllChem.ReactionFromSmarts(smiles, useSmiles=True) + rdkit_rxn.Initialize() + rxncenters = reaction_centres(rdkit_rxn) + reactants = rdkit_rxn.GetReactants() + + atom_maps = set() + for centers, reactant0 in zip(rxncenters, reactants): + reactant = self._get_clean_mol_copy(reactant0) + stereo_info = FindPotentialStereo(reactant) + if any( + not str(info.type).startswith("Bond") + and info.centeredOn in centers + and str(reactant.GetAtomWithIdx(info.centeredOn).GetChiralTag()) + == "CHI_UNSPECIFIED" + for info in stereo_info + ): + return True + for index in centers: + atom = reactant0.GetAtomWithIdx(index) + atom_maps.add(atom.GetAtomMapNum()) + + product0 = rdkit_rxn.GetProducts()[0] + product = self._get_clean_mol_copy(product0) + stereo_info = FindPotentialStereo(product) + if any( + not str(info.type).startswith("Bond") + and product0.GetAtomWithIdx(info.centeredOn).GetAtomMapNum() in atom_maps + and str(product.GetAtomWithIdx(info.centeredOn).GetChiralTag()) + == "CHI_UNSPECIFIED" + for info in stereo_info + ): + return True + + return False + + @staticmethod + def _get_clean_mol_copy(mol0): + """ + Return a copy of the molecule + that is sanitized and without atom-map numbers + """ + mol = AllChem.rdchem.Mol(mol0) + for atom in mol.GetAtoms(): + atom.SetAtomMapNum(0) + AllChem.SanitizeMol(mol) + return mol + + +@action +@dataclass +class StereoCenterOutsideReaction: + """ + Action for checking if there is a stereo centre outside the reaction centre + """ + + pretty_name: ClassVar[str] = "stereo_centre_outside" + in_column: str + out_column: str = "StereoOutside" + + def __call__(self, data: pd.DataFrame) -> pd.DataFrame: + if len(data) == 0: + return data + out_col = global_apply(data, self._row_action, axis=1) + return data.assign(**{self.out_column: out_col}) + + def __str__(self) -> str: + return f"{self.pretty_name} (check for existence of chirality outside reaction centre)" + + def _row_action(self, row: pd.Series) -> str: + smiles = row[self.in_column] + rdkit_rxn = AllChem.ReactionFromSmarts(smiles, useSmiles=True) + rdkit_rxn.Initialize() + rxncenters = reaction_centres(rdkit_rxn) + + reactants = rdkit_rxn.GetReactants() + for centers, reactant in zip(rxncenters, reactants): + for index, atom in enumerate(reactant.GetAtoms()): + if index in centers: + continue + if str(atom.GetChiralTag()) != "CHI_UNSPECIFIED": + return True + return False + + +@action +@dataclass +class StereoMesoProduct: + """ + Action for checking if the product is a meso compound + """ + + pretty_name: ClassVar[str] = "meso_product" + in_column: str + out_column: str = "MesoProduct" + + def __call__(self, data: pd.DataFrame) -> pd.DataFrame: + if len(data) == 0: + return data + out_col = global_apply(data, self._row_action, axis=1) + return data.assign(**{self.out_column: out_col}) + + def __str__(self) -> str: + return f"{self.pretty_name} (check for existence of meso compound)" + + def _row_action(self, row: pd.Series) -> str: + smiles = row[self.in_column] + rdkit_rxn = AllChem.ReactionFromSmarts(smiles, useSmiles=True) + rdkit_rxn.Initialize() + product_mol = rdkit_rxn.GetProducts()[0] + AllChem.SanitizeMol(product_mol) + info = InchiInfo(AllChem.MolToInchi(product_mol)) + return info.get_sp3_stereo()["main"]["non-isotopic"][2] diff --git a/rxnutils/pipeline/base.py b/rxnutils/pipeline/base.py index 537d16d..92e5b27 100644 --- a/rxnutils/pipeline/base.py +++ b/rxnutils/pipeline/base.py @@ -10,8 +10,7 @@ import swifter # noqa #pylint: disable=unused-import from tqdm import tqdm -from rxnutils.chem.utils import split_smiles_from_reaction - +from rxnutils.chem.utils import split_smiles_from_reaction, join_smiles_from_reaction ActionType = Callable[[pd.DataFrame], pd.DataFrame] @@ -111,9 +110,9 @@ def join_lists( :return: the concatenated reaction SMILES """ return self.join_smiles( - self._join_list(reactants_list), - self._join_list(reagents_list), - self._join_list(products_list), + join_smiles_from_reaction(reactants_list), + join_smiles_from_reaction(reagents_list), + join_smiles_from_reaction(products_list), ) def join_smiles(self, reactants: str, reagents: str, products: str) -> str: @@ -149,7 +148,3 @@ def split_smiles(self, row: pd.Series) -> Tuple[str, str, str]: :return: the SMILES of the components """ return row[self.in_column].split(">") - - @staticmethod - def _join_list(list_: List[str]) -> str: - return ".".join([f"({item})" if "." in item else item for item in list_]) diff --git a/rxnutils/pipeline/runner.py b/rxnutils/pipeline/runner.py index 0bf2f6c..18143f2 100644 --- a/rxnutils/pipeline/runner.py +++ b/rxnutils/pipeline/runner.py @@ -12,9 +12,10 @@ import rxnutils.pipeline.actions.templates # noqa import rxnutils.pipeline.actions.dataframe_mod # noqa from rxnutils.pipeline.base import create_action, global_apply, list_actions +from rxnutils.data.batch_utils import read_csv_batch -def run_pipline( +def run_pipeline( data: pd.DataFrame, pipeline: Dict[str, Any], filename: str, @@ -58,7 +59,10 @@ def main(args: Optional[Sequence[str]] = None) -> None: ) parser.add_argument("--max-workers", type=int, help="the maximum number of works") parser.add_argument( - "--batch", type=int, nargs=2, help="Line numbers to start and stop reading rows" + "--batch", + type=int, + nargs=2, + help="Line numbers to start and stop reading rows (1-indexed to always include the header, start:end).", ) parser.add_argument("--no-intermediates", action="store_true", default=False) parser.add_argument("--list", action="store_true", default=False) @@ -74,18 +78,9 @@ def main(args: Optional[Sequence[str]] = None) -> None: with open(args.pipeline, "r") as fileobj: pipeline = yaml.load(fileobj, Loader=yaml.SafeLoader) - if args.batch: - start, end = args.batch - data = pd.read_csv( - args.data, - sep="\t", - index_col=False, - nrows=end - start, - skiprows=range(1, start), - ) - else: - data = pd.read_csv(args.data, sep="\t") - data = run_pipline(data, pipeline, args.output, not args.no_intermediates) + data = read_csv_batch(args.data, sep="\t", index_col=False, batch=args.batch) + + data = run_pipeline(data, pipeline, args.output, not args.no_intermediates) data.to_csv(args.output, index=False, sep="\t") diff --git a/tests/data/example_reactions.csv b/tests/data/example_reactions.csv index e3d05c1..c12a111 100644 --- a/tests/data/example_reactions.csv +++ b/tests/data/example_reactions.csv @@ -1,3 +1,3 @@ rsmi -[CH2:1]([O:8][C:9]1[CH:10]=[C:11]([CH:15]=[C:16]([O:18][CH2:19][C:20]2[CH:25]=[CH:24][CH:23]=[CH:22][CH:21]=2)[CH:17]=1)[C:12](Cl)=[O:13])[C:2]1[CH:7]=[CH:6][CH:5]=[CH:4][CH:3]=1.C(C(CC)(O[Mg+2])C)C.S(=O)(=O)(O)O>[C:26]([O-])(=O)CC([O-])=O.C1C=CC=CC=1>[CH3:26][C:12]([C:11]1[CH:10]=[C:9]([O:8][CH2:1][C:2]2[CH:7]=[CH:6][CH:5]=[CH:4][CH:3]=2)[CH:17]=[C:16]([O:18][CH2:19][C:20]2[CH:25]=[CH:24][CH:23]=[CH:22][CH:21]=2)[CH:15]=1)=[O:13] +[CH2:1]([O:8][C:9]1[CH:10]=[C:11]([CH:15]=[C:16]([O:18][CH2:19][C:20]2[CH:25]=[CH:24][CH:23]=[CH:22][CH:21]=2)[CH:17]=1)[C:12](Cl)=[O:13])[C:2]1[CH:7]=[CH:6][CH:5]=[CH:4][CH:3]=1.C(C(CC)(O[Mg+2])C)CN(C)(C)(C).S(=O)(=O)(O)O>[C:26]([O-])(=O)CC([O-])=O.C1C=CC=CC=1>[CH3:26][C:12]([C:11]1[CH:10]=[C:9]([O:8][CH2:1][C:2]2[CH:7]=[CH:6][CH:5]=[CH:4][CH:3]=2)[CH:17]=[C:16]([O:18][CH2:19][C:20]2[CH:25]=[CH:24][CH:23]=[CH:22][CH:21]=2)[CH:15]=1)=[O:13] [CH2:5]([S:7](Cl)(=[O:9])=[O:8])[CH3:6].[CH3:90][CH2:91][O:92][CH2:93][CH3:94]>C(N(CC)CC)C>[CH2:5]([S:7](OCCBr)(=[O:9])=[O:8])[CH3:6] diff --git a/tests/data/example_stereo_reactions.csv b/tests/data/example_stereo_reactions.csv new file mode 100644 index 0000000..e03b76d --- /dev/null +++ b/tests/data/example_stereo_reactions.csv @@ -0,0 +1,6 @@ +rsmi +[F:3][c:32]1[cH:31][c:38]2[c:37]([n:30][cH:33]1)[n:36](-[c:20]1[cH:19][c:18](-[c:24]3[cH:25][cH:26][c:27]([CH2:17][N:11]4[CH2:12][CH2:13][NH:14][CH2:15][CH2:16]4)[cH:28][cH:29]3)[cH:23][cH:22][cH:21]1)[c:35](=[O:40])[n:34]([CH:5]1[CH2:6][CH2:7][C:8](=[O:4])[CH2:9][CH2:10]1)[c:39]2=[O:41]>CO.B.[Na+]>[F:3][c:32]1[cH:31][c:38]2[c:37]([n:30][cH:33]1)[n:36](-[c:20]1[cH:19][c:18](-[c:24]3[cH:25][cH:26][c:27]([CH2:17][N:11]4[CH2:12][CH2:13][NH:14][CH2:15][CH2:16]4)[cH:28][cH:29]3)[cH:23][cH:22][cH:21]1)[c:35](=[O:40])[n:34]([C@H:5]1[CH2:6][CH2:7][C@H:8]([OH:4])[CH2:9][CH2:10]1)[c:39]2=[O:41] +O=[Os](=O)(=[O:10])=[O:12].[CH3:1][C@:2]12[CH:3]=[CH:4][C:5](=[O:6])[CH:7]=[C:8]1[CH:9]=[CH:11][C@@H:13]1[C@@H:14]2[CH2:15][CH2:16][C@:17]2([CH3:18])[C:19](=[O:20])[CH2:21][CH2:22][C@@H:23]12>C1COCCO1.c1ccncc1>[CH3:1][C@:2]12[CH:3]=[CH:4][C:5](=[O:6])[CH:7]=[C:8]1[C@H:9]([OH:10])[C@H:11]([OH:12])[C@@H:13]1[C@@H:14]2[CH2:15][CH2:16][C@:17]2([CH3:18])[C:19](=[O:20])[CH2:21][CH2:22][C@@H:23]12 +[CH3:1][C@:2]12[CH2:3][CH2:4][C@H:5]([OH:6])[CH2:7][C:8]1=[CH:9][CH2:10][C@@H:11]1[C@@H:12]2[CH2:13][CH2:14][C@:15]2([CH3:16])[C:17](=[O:18])[CH2:19][CH2:20][C@@:21]12[F:22]>CCO.[H][H].[Pd]>[CH3:1][C@:2]12[CH2:3][CH2:4][C:5](=[O:6])[CH2:7][C@@H:8]1[CH2:9][CH2:10][C@@H:11]1[C@@H:12]2[CH2:13][CH2:14][C@:15]2([CH3:16])[C:17](=[O:18])[CH2:19][CH2:20][C@@:21]12[F:22] +N1[CH:2]([OH:1])[C@H:3]([OH:4])[C@@H:5]([OH:6])[C@H:7]([OH:8])[C@H:9]1[CH2:11][OH:12].[OH2:10]>>[O:1]=[CH:2][C@H:3]([OH:4])[C@@H:5]([OH:6])[C@H:7]([OH:8])[C@H:9]([OH:10])[CH2:11][OH:12] +CC(C)(C)OC(=O)[NH:7][C@H:6](C(=O)O)[CH2:8][c:9]1[cH:10][nH:11][c:12]2[cH:13][cH:14][cH:15][cH:16][c:17]12.CC(C)(C)O[C:4]([NH:3][C@H:2]([CH3:1])[C:18](=[O:19])[OH:20])=[O:5]>C(=NC1CCCCC1)=NC1CCCCC1.CC(C)(C)OC(=O)N[C@@H](COCc1ccccc1)C(=O)O.CC(C)(C)OC(=O)N[C@@H](Cc1ccc(OCc2ccccc2)cc1)C(=O)O.CN(C)C=O.NC(=O)CC[C@H](NC(=O)OCc1ccccc1)C(=O)Oc1ccc([N+](=O)[O-])cc1.O=C(O)[C@H](Cc1c[nH]cn1)NCc1ccccc1>[CH3:1][C@@H:2]([NH:3][C:4](=[O:5])[C@@H:6]([NH2:7])[CH2:8][c:9]1[cH:10][nH:11][c:12]2[cH:13][cH:14][cH:15][cH:16][c:17]12)[C:18](=[O:19])[OH:20] \ No newline at end of file diff --git a/tests/test_batch_utils.py b/tests/test_batch_utils.py new file mode 100644 index 0000000..11e0509 --- /dev/null +++ b/tests/test_batch_utils.py @@ -0,0 +1,112 @@ +import os + +import pandas as pd +import pytest +from scipy import sparse + +from rxnutils.data.batch_utils import ( + combine_csv_batches, + combine_sparse_matrix_batches, + create_csv_batches, + nlines, + read_csv_batch, +) + + +@pytest.fixture +def create_dummy_file(tmpdir): + def wrapper(line_count): + filename = str(tmpdir / "temp.csv") + pd.DataFrame({"any": list(range(line_count))}).to_csv(filename, index=False) + return filename + + return wrapper + + +@pytest.mark.parametrize( + ("line_count"), + [ + (0), + (1), + (5), + (10), + (100), + ], +) +def test_nlines(line_count, create_dummy_file): + filename = create_dummy_file(line_count) + + assert nlines(filename) == line_count + 1 + + +@pytest.mark.parametrize( + ("nbatches", "expected"), + [ + (2, [(0, 1, 6), (1, 6, 11)]), + (3, [(0, 1, 5), (1, 5, 8), (2, 8, 11)]), + ], +) +def test_csv_chunks(nbatches, expected, create_dummy_file): + filename = create_dummy_file(10) + + assert create_csv_batches(filename, nbatches) == expected + + +@pytest.mark.parametrize( + ("nbatches", "expected"), + [ + (2, [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]), + (3, [[0, 1, 2, 3], [4, 5, 6], [7, 8, 9]]), + ], +) +def test_csv_chunks_end2end(nbatches, expected, create_dummy_file): + filename = create_dummy_file(10) + + chunks = create_csv_batches(filename, nbatches) + for batch, this_expected in zip(chunks, expected): + csv_data = read_csv_batch(filename, batch) + assert csv_data["any"].to_list() == this_expected + + +def test_combine_csv_batches(tmpdir): + filename1 = str(tmpdir / "temp.csv.0") + pd.DataFrame({"any": [1, 2, 3]}).to_csv(filename1, sep="\t", index=False) + filename2 = str(tmpdir / "temp.csv.1") + pd.DataFrame({"any": [4, 5, 6]}).to_csv(filename2, sep="\t", index=False) + filename = str(tmpdir / "temp.csv") + + assert os.path.exists(filename1) + assert os.path.exists(filename2) + + combine_csv_batches(filename, 2) + + assert not os.path.exists(filename1) + assert not os.path.exists(filename2) + assert pd.read_csv(filename)["any"].to_list() == [1, 2, 3, 4, 5, 6] + + +def test_combine_sparse_matrix_batches(tmpdir): + filename1 = str(tmpdir / "temp.0.npz") + sparse.save_npz(filename1, sparse.csr_array([0, 0, 0, 1]), compressed=True) + filename2 = str(tmpdir / "temp.1.npz") + sparse.save_npz(filename2, sparse.csr_array([1, 1, 1, 0]), compressed=True) + filename = str(tmpdir / "temp.npz") + + assert os.path.exists(filename1) + assert os.path.exists(filename2) + + combine_sparse_matrix_batches(filename, 2) + + assert not os.path.exists(filename1) + assert not os.path.exists(filename2) + expected = [ + 0, + 0, + 0, + 1, + 1, + 1, + 1, + 0, + ] + assert sparse.load_npz(filename).toarray().flatten().tolist() == expected diff --git a/tests/test_cgr.py b/tests/test_cgr.py new file mode 100644 index 0000000..ba91297 --- /dev/null +++ b/tests/test_cgr.py @@ -0,0 +1,48 @@ +import pytest + +from rxnutils.chem.reaction import ChemicalReaction +from rxnutils.chem.cgr import CondensedGraphReaction + + +@pytest.fixture +def rsmi2cgr(): + def wrapper(rsmi): + rxn = ChemicalReaction(rsmi, clean_smiles=False) + return CondensedGraphReaction(rxn) + + return wrapper + + +def test_create_cgr(rsmi2cgr): + rsmi = ( + "[O:2]=[C:3]([OH:4])[CH2:5][CH2:6][CH2:7][O:8][c:9]1[cH:10][cH:11][cH:12][cH:23][cH:24]1.O=C1CCC(=O)N1[Cl:1]>" + ">[Cl:1][c:10]1[c:9]([O:8][CH2:7][CH2:6][CH2:5][C:3](=[O:2])[OH:4])[cH:24][cH:23][cH:12][cH:11]1" + ) + + cgr = rsmi2cgr(rsmi) + + assert cgr.bonds_broken == 1 + assert cgr.bonds_formed == 1 + assert cgr.bonds_changed == 2 + assert cgr.total_centers == 5 + + +def test_compare_cgrs(rsmi2cgr): + rsmi1 = ( + "[O:2]=[C:3]([OH:4])[CH2:5][CH2:6][CH2:7][O:8][c:9]1[cH:10][cH:11][cH:12][cH:23][cH:24]1.O=C1CCC(=O)N1[Cl:1]" + ">>[Cl:1][c:10]1[c:9]([O:8][CH2:7][CH2:6][CH2:5][C:3](=[O:2])[OH:4])[cH:24][cH:23][cH:12][cH:11]1" + ) + rsmi2 = ( + "[O:2]=[C:3]([OH:4])[CH2:5][CH2:6][CH2:7][O:8][c:9]1[cH:10][cH:11][cH:12][cH:23][cH:24]1" + ">>[Cl:1][c:10]1[c:9]([O:8][CH2:7][CH2:6][CH2:5][C:3](=[O:2])[OH:4])[cH:24][cH:23][cH:12][cH:11]1" + ) + + cgr1 = rsmi2cgr(rsmi1) + cgr2 = rsmi2cgr(rsmi2) + + assert cgr1 == cgr1 + assert cgr1.distance_to(cgr1) == 0 + + assert cgr1 != cgr2 + assert cgr1.distance_to(cgr2) == 2 + assert cgr2.distance_to(cgr1) == 2 diff --git a/tests/test_chem_utils.py b/tests/test_chem_utils.py index 53d1ea2..ce3a7c0 100644 --- a/tests/test_chem_utils.py +++ b/tests/test_chem_utils.py @@ -1,5 +1,6 @@ import pytest from rdkit import Chem +from rdkit.Chem import AllChem from rxnutils.chem.utils import ( has_atom_mapping, @@ -11,6 +12,7 @@ split_smiles_from_reaction, reassign_rsmi_atom_mapping, get_special_groups, + reaction_centres, ) @@ -118,3 +120,23 @@ def test_special_groups(): # if this should make sense mol = Chem.MolFromSmiles("c1ccccc1B(O)O") assert get_special_groups(mol)[0][1] == (6, 7, 8) + + +@pytest.mark.parametrize( + ("smiles", "expected"), + [ + ( + "[ClH:1].[cH:2]1[cH:3][cH:4][cH:5][cH:6][cH:7]1>>[Cl:1][c:2]1[cH:3][cH:4][cH:5][cH:6][cH:7]1", + ((0,), (0,)), + ), + ( + "Br[CH2:9][CH2:10][CH2:11]Br.[CH3:1][CH2:2][O:3][C:4](=[O:5])[CH2:6][C:7]#[N:8]>>[CH3:1][CH2:2][O:3][C:4](=[O:5])[C:6]1([C:7]#[N:8])[CH2:9][CH2:10][CH2:11]1", + ((1, 3), (5,)), + ), + ], +) +def test_reaction_centers(smiles, expected): + rxn = AllChem.ReactionFromSmarts(smiles, useSmiles=True) + rxn.Initialize() + + assert reaction_centres(rxn) == expected diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index 29e0956..fba3077 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -35,7 +35,7 @@ def test_pipeline(shared_datadir, tmpdir): assert data["NMappedReactants"].to_list() == [2, 2] assert data["NReagents"].to_list() == [0, 0] assert data["NMappedReagents"].to_list() == [0, 0] - assert data["BadMolecules"].to_list()[0] == "C(C(CC)(O[Mg+2])C)C" + assert data["BadMolecules"].to_list()[0] == "C(C(CC)(O[Mg+2])C)CN(C)(C)(C)" assert data["BadMolecules"].isna()[1] @@ -62,4 +62,4 @@ def test_pipeline_batched(shared_datadir, tmpdir): assert data["NMappedReactants"].to_list() == [2] assert data["NReagents"].to_list() == [0] assert data["NMappedReagents"].to_list() == [0] - assert data["BadMolecules"].to_list() == ["C(C(CC)(O[Mg+2])C)C"] + assert data["BadMolecules"].to_list() == ["C(C(CC)(O[Mg+2])C)CN(C)(C)(C)"] diff --git a/tests/test_reaction.py b/tests/test_reaction.py index 26a1591..dd676ab 100644 --- a/tests/test_reaction.py +++ b/tests/test_reaction.py @@ -100,7 +100,7 @@ def test_nochangeness(rsmi, expected): True, ), ( - "[CH3:1]I.[CH3:6][N:5]1[CH2:4][CH2:3][NH:2][CH2:31][CH2:16]1>C(C(CC)(O[Mg+2])C)C>" + "[CH3:1]I.[CH3:6][N:5]1[CH2:4][CH2:3][NH:2][CH2:31][CH2:16]1>C(C(CC)(O[Mg+2])CN(C)(C)(C))C>" "[CH3:1]I.[CH3:6][N:5]1[CH2:4][CH2:3][NH:2][CH2:31][CH2:16]1", False, ), diff --git a/tests/test_reaction_mods_actions.py b/tests/test_reaction_mods_actions.py index ab04eaa..1d7c052 100644 --- a/tests/test_reaction_mods_actions.py +++ b/tests/test_reaction_mods_actions.py @@ -11,6 +11,8 @@ RDKitRxnRoles, SplitReaction, RemoveUnchangedProducts, + IsotopeInfo, + DesaltMolecules, CONTRIB_INSTALLED, ) from rxnutils.pipeline.base import global_apply @@ -140,7 +142,7 @@ def test_remove_unsanitizable(make_reaction_dataframe): df2 = action(df) - assert df2["BadMolecules"].to_list() == ["C(C(CC)(O[Mg+2])C)C", ""] + assert df2["BadMolecules"].to_list() == ["C(C(CC)(O[Mg+2])C)CN(C)(C)(C)", ""] @pytest.mark.xfail( @@ -179,3 +181,63 @@ def test_split_reaction(make_reaction_dataframe): assert [smi.count(".") + 1 for smi in df2["Reactants"]] == [3, 2] assert [smi.count(".") + 1 for smi in df2["Reagents"]] == [2, 1] assert [smi.count(".") + 1 for smi in df2["Products"]] == [1, 1] + + +def test_isotope_info(): + # Don't have to be sensible SMILES, the action works on strings + df = pd.DataFrame( + { + "rsmi": [ + "c1ccccc1", + "[13C]c1ccccc1", + "[13CH2]c1ccccc1", + "[2H:3]", + "[2H-:3][13CH2]", + ] + } + ) + action = IsotopeInfo(in_column="rsmi") + + df2 = action(df) + + assert pd.isna(df2["Isotope"]).to_list() == [ + True, + False, + False, + False, + False, + ] + assert df2["Isotope"].to_list()[1:] == ["13C", "13C", "2H", "2H"] + assert df2["RxnSmilesWithoutIsotopes"].to_list() == [ + "c1ccccc1", + "[C]c1ccccc1", + "[CH2]c1ccccc1", + "[H:3]", + "[H-:3][CH2]", + ] + + +def test_desalting(): + smi1 = "OCC.(C.[Na+].[Cl-])>>OC(=O)CC" + smi2 = "OCC>>OC(=O)CC" + smi3 = "OCC.O>>OC(=O)CC" + df = pd.DataFrame({"rsmi": [smi1, smi2, smi3]}) + action = DesaltMolecules(in_column="rsmi") + + df2 = action(df) + + assert df2["RxnDesalted"].to_list() == [ + "CCO.C>>CCC(=O)O", + "CCO>>CCC(=O)O", + "CCO>>CCC(=O)O", + ] + + action.keep_something = True + + df2 = action(df) + + assert df2["RxnDesalted"].to_list() == [ + "CCO.C>>CCC(=O)O", + "CCO>>CCC(=O)O", + "CCO.O>>CCC(=O)O", + ] diff --git a/tests/test_reaction_props_actions.py b/tests/test_reaction_props_actions.py index facecdb..2a60579 100644 --- a/tests/test_reaction_props_actions.py +++ b/tests/test_reaction_props_actions.py @@ -16,6 +16,16 @@ RingBondMade, RingMadeSize, RingNumberChange, + StereoMesoProduct, + StereoCentreChanges, + StereoCenterIsCreated, + StereoCenterIsRemoved, + StereoHasChiralReagent, + StereoCenterInReactantPotential, + StereoCenterOutsideReaction, + CgrCreated, + CgrNumberOfDynamicBonds, + MaxRingNumber, ) from rxnutils.pipeline.actions.reaction_mod import RemoveUnsanitizable from rxnutils.pipeline.base import global_apply @@ -108,7 +118,7 @@ def test_smiles_length(make_reaction_dataframe): df2 = action(df) - assert df2["SmilesLength"].to_list() == [434, 133] + assert df2["SmilesLength"].to_list() == [444, 133] def test_smiles_sanitizable(make_reaction_dataframe): @@ -158,3 +168,76 @@ def test_ring_actions_no_rings(make_reaction_dataframe): assert df2["NRingChange"].to_list() == [0, 0] assert df2["RingBondMade"].to_list() == [False, False] assert df2["RingMadeSize"].to_list() == [0, 0] + + +def test_meso_products(shared_datadir): + df = pd.read_csv(shared_datadir / "example_stereo_reactions.csv") + action = StereoMesoProduct(in_column="rsmi") + + df2 = action(df) + + assert df2["MesoProduct"].to_list() == [True, False, False, False, False] + + +def test_stereo_changes(shared_datadir): + df = pd.read_csv(shared_datadir / "example_stereo_reactions.csv") + action1 = StereoCentreChanges(in_column="rsmi") + action2 = StereoCenterIsRemoved() + action3 = StereoCenterIsCreated() + + df2 = action3(action2(action1(df))) + + assert df2["HasStereoChanges"].to_list() == [True, True, True, True, True] + assert df2["StereoCentreCreated"].to_list() == [True, True, True, False, False] + assert df2["StereoCentreRemoved"].to_list() == [False, False, True, False, False] + + +def test_potential_stereo(shared_datadir): + df = pd.read_csv(shared_datadir / "example_stereo_reactions.csv") + action = StereoCenterInReactantPotential(in_column="rsmi") + + df2 = action(df) + + assert df2["PotentialStereoCentre"].to_list() == [False, False, False, True, False] + + +def test_chiral_reagent(shared_datadir): + df = pd.read_csv(shared_datadir / "example_stereo_reactions.csv") + action = StereoHasChiralReagent(in_column="rsmi") + + df2 = action(df) + + assert df2["HasChiralReagent"].to_list() == [False, False, False, False, True] + + +def test_cgr_created(make_reaction_dataframe): + action1 = RemoveUnsanitizable(in_column="rsmi", out_column="rsmi") + action2 = CgrCreated(in_column="rsmi") + df = make_reaction_dataframe + + df2 = action2(df) + + assert df2["CGRCreated"].to_list() == [False, True] + + df2 = action2(action1(df)) + + assert df2["CGRCreated"].to_list() == [True, True] + + +def test_cgr_dynamic_bonds(make_reaction_dataframe): + action = CgrNumberOfDynamicBonds(in_column="rsmi") + df = make_reaction_dataframe + + df2 = action(df) + + assert pd.isna(df2["NDynamicBonds"].iloc[0]) + assert df2["NDynamicBonds"].iloc[1] == 2 + + +def test_max_rings(make_reaction_dataframe): + action = MaxRingNumber(in_column="rsmi") + df = make_reaction_dataframe + + df2 = action(df) + + assert df2["MaxRings"].to_list() == [3, 0]