From 688218b9a074bd0649928252f0474b613587bbc2 Mon Sep 17 00:00:00 2001 From: Matteo Manica Date: Thu, 29 Feb 2024 11:05:17 +0100 Subject: [PATCH] chore: open-sourcing rxn-neb. Signed-off-by: Matteo Manica --- .github/workflows/tests.yaml | 20 + .gitignore | 127 + README.md | 51 + poetry.lock | 3468 ++++++++++++++++++++++++ pyproject.toml | 149 + sample-data/reaction_trees.json | 418 +++ src/rxn/neb/__init__.py | 3 + src/rxn/neb/availability.py | 47 + src/rxn/neb/cli/__init__.py | 1 + src/rxn/neb/cli/core.py | 68 + src/rxn/neb/cli/data_processing.py | 115 + src/rxn/neb/fingerprints.py | 30 + src/rxn/neb/py.typed | 0 src/rxn/neb/retro.py | 1070 ++++++++ src/rxn/neb/single_step_predictions.py | 84 + src/rxn/neb/utils/__init__.py | 1 + src/rxn/neb/utils/core.py | 924 +++++++ src/rxn/neb/utils/general.py | 122 + src/rxn/neb/utils/smiles.py | 106 + 19 files changed, 6804 insertions(+) create mode 100644 .github/workflows/tests.yaml create mode 100644 .gitignore create mode 100644 README.md create mode 100644 poetry.lock create mode 100644 pyproject.toml create mode 100644 sample-data/reaction_trees.json create mode 100644 src/rxn/neb/__init__.py create mode 100644 src/rxn/neb/availability.py create mode 100644 src/rxn/neb/cli/__init__.py create mode 100755 src/rxn/neb/cli/core.py create mode 100644 src/rxn/neb/cli/data_processing.py create mode 100644 src/rxn/neb/fingerprints.py create mode 100644 src/rxn/neb/py.typed create mode 100644 src/rxn/neb/retro.py create mode 100755 src/rxn/neb/single_step_predictions.py create mode 100644 src/rxn/neb/utils/__init__.py create mode 100644 src/rxn/neb/utils/core.py create mode 100644 src/rxn/neb/utils/general.py create mode 100644 src/rxn/neb/utils/smiles.py diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml new file mode 100644 index 0000000..63c7b7b --- /dev/null +++ b/.github/workflows/tests.yaml @@ -0,0 +1,20 @@ +name: "Running tests: ruff stylinh" + +on: [push, pull_request] + +jobs: + tests: + runs-on: ubuntu-latest + name: Style, mypy, pytest + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.8 + uses: actions/setup-python@v3 + with: + python-version: 3.8 + - name: Install poetry + run: pip install poetry==1.7.1 + - name: Install Dependencies + run: poetry install + - name: Check style + run: poetry run ruff check . && poetry run ruff format --check . diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..36ba7ee --- /dev/null +++ b/.gitignore @@ -0,0 +1,127 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +src/rxn/neb/.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +../.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +conda_env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +# PyCharm +.idea/ + +# VSCode +.vscode/ + +# Pre-commit configuration +# .pre-commit-config.yaml + +# Apple macOS +.DS_Store + +# ruff +.ruff_cache + +# custom +check_test.py +retrosynthesis_check_test_ref.json +test_retro.json +sandbox +rxnfp diff --git a/README.md b/README.md new file mode 100644 index 0000000..9329daa --- /dev/null +++ b/README.md @@ -0,0 +1,51 @@ +# rxn-neb + +## Setup + +To install the package run: + +```console +poetry install +``` + +## Pre-process data for running rxn-neb + +Here we assume to start from a JSON file reporting synthesis trees characterized by reactions SMILES represented using a pre-order traversal. A sample is provided [here](./sample-data/reaction_trees.json). + +Additionally we assume a model for reaction fingerprints compatible with [`rxnfp`](https://github.com/rxn4chemistry/rxnfp) is available (see the repo for instructions on how to train your own on public or proprietary data). + +To get the default model used in RXN for Chemistry simply clone the repo: + +```console +git clone https://github.com/rxn4chemistry/rxnfp.git +``` + +You can directly use the default model available at `./rxnfp/rxnfp/models/transformers/bert_ft`. + +Prepare the fingerprints from available synthesis trees: + +```console +generate-fingerprints --reaction_trees_path "./sample-data/reaction_trees.json" --fingerprints_model_path "./rxnfp/rxnfp/models/transformers/bert_ft" --generated_fingerprints_path "./sandbox/generated_fingerprints.csv" +``` + +Prepare the PCA model for fingerprint compression and related indexes: + +```console +generate-pca-compression-and-indices --reaction_trees_path "./sample-data/reaction_trees.json" --fingerprints_path "./sandbox/generated_fingerprints.csv" --pca_model_filename "./sandbox/pca.pkl" --tree_data_dict_pca_filename "./sandbox/tree_data_dict_pca.pkl" +``` + +NOTE: these examples are creating a `sandbox` folder where all outputs are stored. + +## Usage + +We assume you have a pair of single-step forward and backward model trained using [`rxn-onmt-models`](https://github.com/rxn4chemistry/rxn-onmt-models) (see the repo for a detailed guide on how to train them on public or proprietary data). + +```console +run-neb-retrosynthesis --product "NS(=O)(=O)c1nn(-c2ccccn2)cc1Br" \ + --forward_model_path "/path/to/forward_model.pt" \ + --backward_model_path "/path/to/backward_model.pt" \ + --fingerprints_model_path "./rxnfp/rxnfp/models/transformers/bert_ft" \ + --pca_model_filename "./sandbox/pca.pkl" \ + --tree_data_dict_pca_filename "./sandbox/tree_data_dict_pca.pkl" \ + --output_path ./test_retro.json +``` diff --git a/poetry.lock 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+[tool.poetry.scripts] +run-neb-retrosynthesis = "rxn.neb.cli.core:main" +generate-fingerprints = "rxn.neb.cli.data_processing:generate_fingerprints" +generate-pca-compression-and-indices = "rxn.neb.cli.data_processing:generate_pca_compression_and_indices" + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + +[[tool.mypy.overrides]] +# module = [ +# "tqdm.*", +# ] +ignore_missing_imports = true + +[tool.ruff] +# Exclude a variety of commonly ignored directories. +exclude = [ + ".bzr", + ".direnv", + ".eggs", + ".git", + ".git-rewrite", + ".hg", + ".ipynb_checkpoints", + ".mypy_cache", + ".nox", + ".pants.d", + ".pyenv", + ".pytest_cache", + ".pytype", + ".ruff_cache", + ".svn", + ".tox", + ".venv", + ".vscode", + "__pypackages__", + "_build", + "buck-out", + "build", + "dist", + "node_modules", + "site-packages", + "venv", +] + +# Same as Black. +line-length = 150 +indent-width = 4 + +target-version = "py38" + +[tool.ruff.lint] +# Enable Pyflakes (`F`) and a subset of the pycodestyle (`E`) codes by default. +# Unlike Flake8, Ruff doesn't enable pycodestyle warnings (`W`) or +# McCabe complexity (`C901`) by default. +select = [ + "E4", + "E7", + "E9", + "F", + "W291", + "W292", + "W505", + "E721", + "I", + "N801", + "N802", + "N803", + "N804", + "N805", + "N806", + "N816", + # "D100", + # "D101", + "D102", + # "D103", + "D104", + "D105", + "D106", + "D107", + "D300", + "UP010", + "UP011", + "UP019", + "UP032" +] +ignore = [] + +# Allow fix for all enabled rules (when `--fix`) is provided. +fixable = ["ALL"] +unfixable = [] + +# Allow unused variables when underscore-prefixed. +dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$" + +[tool.ruff.format] +# Like Black, use double quotes for strings. +quote-style = "double" + +# Like Black, indent with spaces, rather than tabs. +indent-style = "space" + +# Like Black, respect magic trailing commas. +skip-magic-trailing-comma = false + +# Like Black, automatically detect the appropriate line ending. +line-ending = "auto" + + +# Set the line length limit used when formatting code snippets in +# docstrings. +# +# This only has an effect when the `docstring-code-format` setting is +# enabled. +docstring-code-line-length = "dynamic" diff --git a/sample-data/reaction_trees.json b/sample-data/reaction_trees.json new file mode 100644 index 0000000..5e2cec2 --- /dev/null +++ b/sample-data/reaction_trees.json @@ -0,0 +1,418 @@ +{ + "synthesis-0": { + "rxns": [ + "C=CCc1cc(C2=NNC(=O)CC2)ccc1O.ClCC1CO1.ClCCl.C1CCNCC1.[Na+]~[OH-]>>C=CCc1cc(C2=NNC(=O)CC2)ccc1OCC1CO1", + "C=CCOc1ccc(C2=NNC(=O)CC2)cc1.CN(C)c1ccccc1>>C=CCc1cc(C2=NNC(=O)CC2)ccc1O", + "O=C([O-])[O-]~[K+]~[K+].C=CCBr.CC(C)=O.O=C1CCC(c2ccc(O)cc2)=NN1>>C=CCOc1ccc(C2=NNC(=O)CC2)cc1" + ], + "rxnfp_ids": [ + 0, + 26, + 36 + ], + "depths": [ + 0, + 1, + 2 + ], + "rxn_classes": [ + "Unknown", + "Unknown", + "Unknown" + ] + }, + "synthesis-1": { + "rxns": [ + 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"CCOC(C)=O.CCSc1ccc2c(c1)C(CC(=O)N(C)C)=Cc1ccccc1N2C.C1CCOC1.[Al+3]~[H-]~[H-]~[H-]~[H-]~[Li+].[Na+]~[OH-]>>CCSc1ccc2c(c1)C(CCN(C)C)=Cc1ccccc1N2C", + "ClC(Cl)Cl.CCSc1ccc2c(c1)C(CC(=O)Oc1ccc([N+](=O)[O-])cc1)=Cc1ccccc1N2C.CNC.O>>CCSc1ccc2c(c1)C(CC(=O)N(C)C)=Cc1ccccc1N2C" + ], + "rxnfp_ids": [ + 8, + 22 + ], + "depths": [ + 0, + 1 + ], + "rxn_classes": [ + "Unknown", + "Unknown" + ] + }, + "synthesis-15": { + "rxns": [ + "O=C(O)/C=C\\C(=O)O.O=C(CO)OC(CC1CCCCN1)c1ccc2sc3ccccc3c2c1.O=C(CC1CCCN1)c1ccc2c(c1)Cc1ccccc1-2.O=C(CC1CCCCN1)c1ccc2ccc3ccccc3c2c1>>OC(CC1CCCN1)c1ccc2c(c1)Cc1ccccc1-2", + "CC(=O)c1ccc2c(c1)Cc1ccccc1-2.CC(=O)c1ccc2oc3ccccc3c2c1.C1=NCCC1.C1=NCCCC1>>O=C(CC1CCCN1)c1ccc2c(c1)Cc1ccccc1-2" + ], + "rxnfp_ids": [ + 9, + 46 + ], + "depths": [ + 0, + 1 + ], + "rxn_classes": [ + "Unknown", + "Unknown" + ] + }, + "synthesis-16": { + "rxns": [ + "C=O.C=CC(C(N)=S)c1cccc(C)n1.C1COCCN1>>C=CC(C(=S)NCN1CCOCC1)c1cccc(C)n1", + "C=CC(C#N)c1cccc(C)n1.C=CC(O)c1cccc(C)n1.C=CC(Cl)c1cccc(C)n1.S.O=S(Cl)Cl.[C-]#N~[Na+]>>C=CC(C(N)=S)c1cccc(C)n1" + ], + "rxnfp_ids": [ + 15, + 3 + ], + "depths": [ + 0, + 1 + ], + "rxn_classes": [ + "Unknown", + "Unknown" + ] + }, + "synthesis-17": { + "rxns": [ + "Cc1cc2c(cc1Cl)c(-c1ccccc1)o[n+]2C(C)(C)C~F[B-](F)(F)F.C1COCCN1>>Cc1cc2c(cc1Cl)C(c1ccccc1)(N1CCOCC1)ON2C(C)(C)C", + "CC(C)(C)O.CCOCC.Cc1cc2noc(-c3ccccc3)c2cc1Cl.F[B-](F)(F)F~[H+].C[N+](=O)[O-]>>Cc1cc2c(cc1Cl)c(-c1ccccc1)o[n+]2C(C)(C)C~F[B-](F)(F)F" + ], + "rxnfp_ids": [ + 42, + 29 + ], + "depths": [ + 0, + 1 + ], + "rxn_classes": [ + "Unknown", + "Unknown" + ] + }, + "synthesis-18": { + "rxns": [ + "Cc1ccc2c(c1)Cc1cc(C)ccc1OC(C(=O)O)O2.CO.O=S(=O)(O)O>>COC(=O)C1Oc2ccc(C)cc2Cc2cc(C)ccc2O1", + "O=C([O-])[O-]~[K+]~[K+].Cc1ccc(O)c(Cc2cc(C)ccc2O)c1.CC(C)O.O=C(O)C(Cl)Cl>>Cc1ccc2c(c1)Cc1cc(C)ccc1OC(C(=O)O)O2" + ], + "rxnfp_ids": [ + 32, + 33 + ], + "depths": [ + 0, + 1 + ], + "rxn_classes": [ + "Unknown", + "Unknown" + ] + }, + "synthesis-19": { + "rxns": [ + "CC(C)N.CO.COC(=O)CCC(=O)c1ccccc1OCC1CO1>>COC(=O)CCC(=O)c1ccccc1OCC(O)CNC(C)C", + "O=C([O-])[O-]~[K+]~[K+].BrCC1CO1.CCC(C)=O.COC(=O)CCC(=O)c1ccccc1O>>COC(=O)CCC(=O)c1ccccc1OCC1CO1", + "CO.Cl.O=C(O)CCC(=O)c1ccccc1O>>COC(=O)CCC(=O)c1ccccc1O" + ], + "rxnfp_ids": [ + 24, + 45, + 18 + ], + "depths": [ + 0, + 1, + 2 + ], + "rxn_classes": [ + "Unknown", + "Unknown", + "Unknown" + ] + } +} \ No newline at end of file diff --git a/src/rxn/neb/__init__.py b/src/rxn/neb/__init__.py new file mode 100644 index 0000000..3696084 --- /dev/null +++ b/src/rxn/neb/__init__.py @@ -0,0 +1,3 @@ +"""Package initialization.""" + +__version__ = "0.0.1" # managed by poetry_bumpversion diff --git a/src/rxn/neb/availability.py b/src/rxn/neb/availability.py new file mode 100644 index 0000000..94c923b --- /dev/null +++ b/src/rxn/neb/availability.py @@ -0,0 +1,47 @@ +"""Compound availabilities.""" +from rxn.availability import IsAvailable +from rxn.chemutils.smiles_standardization import standardize_smiles + + +def standardize(smiles: str) -> str: + """Standardize a SMILES string. + + Args: + smiles: SMILES string. + + Returns: + standardized SMILES. + """ + return standardize_smiles( + smiles, + canonicalize=True, + sanitize=True, + inchify=False, + ) + + +is_available_object = IsAvailable( + pricing_threshold=0.0, + always_available=None, + model_available=None, + excluded=None, + avoid_substructure=None, + are_materials_exclusive=False, + standardization_function=standardize, + # NOTE: here you can include additional compounds + # that are considered available passing a file containing + # one smiles per line + additional_compounds_filepath=None, +) + + +def is_available(smiles: str) -> bool: + """Check whether compound is available. + + Args: + smiles: SMILES string. + + Returns: + whether the compound is available. + """ + return is_available_object(smiles) diff --git a/src/rxn/neb/cli/__init__.py b/src/rxn/neb/cli/__init__.py new file mode 100644 index 0000000..7e7fe4f --- /dev/null +++ b/src/rxn/neb/cli/__init__.py @@ -0,0 +1 @@ +"""CLI entry-points.""" diff --git a/src/rxn/neb/cli/core.py b/src/rxn/neb/cli/core.py new file mode 100755 index 0000000..b619505 --- /dev/null +++ b/src/rxn/neb/cli/core.py @@ -0,0 +1,68 @@ +"""Core CLI for NEB retro.""" +import json +from pathlib import Path +from typing import Optional + +import click +from loguru import logger + +from ..retro import retro_synthesis + + +@click.command() +@click.option("--product", required=True, type=str, default="NS(=O)(=O)c1nn(-c2ccccn2)cc1Br") +@click.option("--forward_model_path", required=True, type=click.Path(path_type=Path, exists=True)) +@click.option("--backward_model_path", required=True, type=click.Path(path_type=Path, exists=True)) +@click.option("--fingerprints_model_path", required=True, type=click.Path(path_type=Path, exists=True)) +@click.option("--pca_model_filename", required=True, type=click.Path(path_type=Path, exists=True)) +@click.option("--tree_data_dict_pca_filename", required=True, type=click.Path(path_type=Path, exists=True)) +@click.option("--max_steps", required=False, type=int, default=15) +@click.option("--pruning_steps", required=False, type=int, default=3) +@click.option("--output_path", required=False, type=click.Path(path_type=Path), default=None) +def main( + product: str, + forward_model_path: Path, + backward_model_path: Path, + fingerprints_model_path: Path, + pca_model_filename: Path, + tree_data_dict_pca_filename: Path, + max_steps: int, + pruning_steps: int, + output_path: Optional[Path], +) -> None: + """Execute NEB retro. + + Args: + product: product SMILES. + forward_model_path: path to forward model. + backward_model_path: path to backward model. + fingerprints_model_path: path to fingerprints model. + pca_model_filename: PCA model file. + tree_data_dict_pca_filename: PCA model file fro tree data. + max_steps: maximum number of steps for the retro. + pruning_steps: number of steps in-between pruning. + output_path: file where to store the retro. Defaults to None, a.k.a., + simply print the retro without saving it. + + Returns: + a retrosynthesis object. + """ + data = { + "product": product, + "parameters": {"max_steps": max_steps, "pruning_steps": pruning_steps}, + "forward_model_path": str(forward_model_path), + "backward_model_path": str(backward_model_path), + "fingerprints_model_path": str(fingerprints_model_path), + "pca_model_filename": str(pca_model_filename), + "tree_data_dict_pca_filename": str(tree_data_dict_pca_filename), + } + try: + retrosynthesis = retro_synthesis(data) + except Exception: + logger.exception("NEB-retro failed") + retrosynthesis = {"status": "DONE", "routes": []} + + if output_path: + with open(output_path, "w") as fp: + json.dump(retrosynthesis, fp, indent=4) + logger.info(f"retrosynthesis={retrosynthesis}") diff --git a/src/rxn/neb/cli/data_processing.py b/src/rxn/neb/cli/data_processing.py new file mode 100644 index 0000000..b1e540b --- /dev/null +++ b/src/rxn/neb/cli/data_processing.py @@ -0,0 +1,115 @@ +"""Data preparation CLI entry-points.""" +import json +import os +import pickle +from pathlib import Path +from typing import Any, Dict + +import click +import numpy as np +import pandas as pd +from rxn.neb.fingerprints import load_fingerprints_model +from rxn.neb.utils.general import batcher +from sklearn.decomposition import PCA +from sklearn.neighbors import KDTree + + +@click.command() +@click.option("--reaction_trees_path", required=True, type=click.Path(path_type=Path, exists=True)) +@click.option("--fingerprints_model_path", required=True, type=click.Path(path_type=Path, exists=True)) +@click.option("--generated_fingerprints_path", required=True, type=click.Path(path_type=Path)) +@click.option("--batch_size", required=False, type=int, default=1024) +def generate_fingerprints( + reaction_trees_path: Path, + fingerprints_model_path: Path, + generated_fingerprints_path: Path, + batch_size: int, +) -> None: + """Generate fingerprints starting from reaction trees in .JSON format. + + Args: + reaction_trees_path: path to .JSON for the reaction trees. + fingerprints_model_path: path to fingerprints model. + generated_fingerprints_path: path to generated fingerprints. + batch_size: size of the batch used for dumping fingerprints to file. Defaults to 1024. + """ + with open(reaction_trees_path) as fp: + trees: Dict[str, Dict[str, Any]] = json.load(fp) + + fingerprint_model = load_fingerprints_model(fingerprints_model_path) + + # NOTE: sorted reactions according to rxnfp_ids from reaction trees + rxns = [ + rxn + for _, rxn in sorted( + ({index: rxn for tree in trees.values() for index, rxn in zip(tree["rxnfp_ids"], tree["rxns"])}).items(), + key=lambda index_to_rxn: index_to_rxn[0], + ) + ] + + if generated_fingerprints_path.exists(): + generated_fingerprints_path.unlink() + os.makedirs(generated_fingerprints_path.parent, exist_ok=True) + for fingerprints_batch in batcher(fingerprint_model.predict(rxns), batch_size=batch_size): + pd.DataFrame(fingerprints_batch).to_csv(generated_fingerprints_path, mode="ab", header=False, index=None) + + +@click.command() +@click.option("--reaction_trees_path", required=True, type=click.Path(path_type=Path, exists=True)) +@click.option("--fingerprints_path", required=True, type=click.Path(path_type=Path, exists=True)) +@click.option("--pca_model_filename", required=True, type=click.Path(path_type=Path)) +@click.option("--tree_data_dict_pca_filename", required=True, type=click.Path(path_type=Path)) +@click.option("--number_of_components", required=False, type=int, default=16) +@click.option("--max_depth", required=False, type=int, default=16) +@click.option("--leaf_size", required=False, type=int, default=8) +def generate_pca_compression_and_indices( + reaction_trees_path: Path, + fingerprints_path: Path, + pca_model_filename: Path, + tree_data_dict_pca_filename: Path, + number_of_components: int, + max_depth: int, + leaf_size: int, +) -> None: + """Generate a PCA compressed representation for fingerprints a set of KDTree indices. + + Args: + reaction_trees_path: path to .JSON for the reaction trees. + fingerprints_path: path to fingerprints. + pca_model_filename: path to which the PCA object will be serialized to. + tree_data_dict_pca_filename: path to which the depth to KDTree mapping object will be serialized to. + number_of_components: number of principal components. + max_depth: maximum depth considered. + leaf_size: size of the KDTree leaves. + """ + with open(reaction_trees_path) as fp: + trees: Dict[str, Dict[str, Any]] = json.load(fp) + + fingerprints = pd.read_csv(fingerprints_path, header=None, index_col=None) + + pca = PCA(n_components=number_of_components, svd_solver="full") + pca.fit(fingerprints.values) + + with open(pca_model_filename, "wb") as fp: + pickle.dump(pca, fp, protocol=4) + + fingerprints_principal_components = pca.transform(fingerprints.values) + depth_to_kdtree: Dict[str, KDTree] = {} + for depth in range(1, max_depth): + fingerprints_principal_components_for_depth = [] + for tree in trees.values(): + reactions = tree["rxnfp_ids"] + for offset in range(len(reactions) - depth + 1): + fingerprints_principal_components_for_depth.append( + np.array([values for index in reactions[offset : offset + depth] for values in fingerprints_principal_components[index].tolist()]) + ) + if fingerprints_principal_components_for_depth: + kdtree = KDTree( + np.array(fingerprints_principal_components_for_depth).reshape( + len(fingerprints_principal_components_for_depth), number_of_components * depth + ), + leaf_size=leaf_size, + ) + depth_to_kdtree[depth] = kdtree + with open(tree_data_dict_pca_filename, "wb") as fp: + pickle.dump(depth_to_kdtree, fp, protocol=4) diff --git a/src/rxn/neb/fingerprints.py b/src/rxn/neb/fingerprints.py new file mode 100644 index 0000000..c948281 --- /dev/null +++ b/src/rxn/neb/fingerprints.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python + +from functools import lru_cache +from pathlib import Path + +from rxn.utilities.modeling.core import RXNFPModel +from rxn.utilities.modeling.tokenization import BasicSmilesTokenizer, SmilesTokenizer + + +@lru_cache(maxsize=5) +def load_fingerprints_model(model_path: str) -> RXNFPModel: + """Load fingerprints model. + + Args: + model_path: path to the model. + + Returns: + loaded model. + """ + return RXNFPModel( + model_name_or_path=str(model_path), + tokenizer=SmilesTokenizer( + vocab_file=str(Path(model_path) / "vocab.txt"), + basic_tokenizer=BasicSmilesTokenizer(), + ), + ) + + +def compute_rxnfp(smiles: str, fingerprints_model: RXNFPModel): + return list(fingerprints_model.predict([smiles]))[0] diff --git a/src/rxn/neb/py.typed b/src/rxn/neb/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/src/rxn/neb/retro.py b/src/rxn/neb/retro.py new file mode 100644 index 0000000..a8ab6f7 --- /dev/null +++ b/src/rxn/neb/retro.py @@ -0,0 +1,1070 @@ +#!/usr/bin/env python + +import pickle +import time +from copy import deepcopy +from typing import Any, Dict, List, Set + +import numpy as np +from loguru import logger + +from .fingerprints import load_fingerprints_model +from .single_step_predictions import load_single_step_prediction_model, run_rxn_prediction +from .utils.core import ( + Node, + add_to_saved_reactions, + check_reaction, + check_sanity_tree, + complete_reactions, + compute_reaction_smiles_txt2fp_dict, + fetch_score, + filter_similar_node_id, + filter_similar_node_id_with_tree, + get_all_the_finished_reactions_up_to, + get_full_text_sets_of_reactions, + get_level_max, + get_retrosynthesis, + get_score_for_a_single_path, + get_score_for_many_single_path, + good_coverage, + hash_str, + initialize_tree, + list_is_included, + propagate_leaf, + select_l_nb_filter_node_id, + select_l_nb_node_id, + smiles2fp, + split_precursors_with_wait, + update_reaction_smiles_from_tree, +) +from .utils.general import ( + check_alternatives_among, + get_path_to_node, + select_from_saved_here, + select_prefix_to_keep, +) +from .utils.smiles import multistep_standardize + +max_dist_along_cutoff = int(0.4 * 100) # with PCA 16 dim is about 0.4 times the original + + +def retro_synthesis( + input_data, +): + logger.debug("retro_synthesis: neb-retro STARTING_retro_synthesis") + logger.info("retro_synthesis: neb-retro") + + # input params from `input_data`: + logger.debug("retro_synthesis: input_data[parameters] " + str(input_data["parameters"])) + # those not available are set using defaults + if "max_steps" in input_data["parameters"]: + max_steps = input_data["parameters"]["max_steps"] + else: + max_steps = 12 + + # Pruning the tree every N pruning_steps + if "pruning_steps" in input_data["parameters"]: + pruning_steps = input_data["parameters"]["pruning_steps"] + else: + pruning_steps = 2 + + # `input_data` should contain also the path to the files needed by rxn-neb + # Path to the pca_model_filename + if "pca_model_filename" not in input_data: + input_data["pca_model_filename"] = "./neb_pca_object_16.pkl" + + # Path to the tree_data_dict_pca_filename + if "tree_data_dict_pca_filename" not in input_data: + input_data["tree_data_dict_pca_filename"] = "./neb_tree_data_pca_rxnfps_16.pkl.bz2" + + product = input_data["product"] + prefix = "rxn_neb_retro" + # max_steps cannot be lower than 1 + if max_steps < 1: + max_steps = 1 + logger.info(f"retro_synthesis: setting: max_steps: {max_steps}") + clean = True + # take_all = False + # restart_from_knowledge = False # this is now deprecated, on the UI not usable + # product_can_exist = False # Allow retro for an available product + max_levels = 2 * max_steps + min_levels = 4 + max_predictions = 40 + min_predictions = 6 + max_num_leaves_to_be_propagated_init = 4 + max_steps + n_max_leaves_speed = 25 + n_max_leaves_speed_heavy_cut = 100 + ioniq = 1 + max_total_time = 2.0 * 60.0 * float(max_steps) + 240.0 + if max_steps > 12: + max_total_time = 3.0 * 60.0 * float(max_steps) + 900.0 + max_step_time = 2.0 * 60.0 * float(max_steps) + + # Timing + time0 = time.time() + + max_num_leaves_to_be_propagated = max_num_leaves_to_be_propagated_init + + if max_total_time < max_step_time: + max_step_time = max_total_time + logger.info("retro_synthesis: setting: max_step_time = max_total_time (seconds) " + str(max_total_time)) + + logger.info(f"retro_synthesis: setting: max_total_time = {max_total_time} (seconds)") + logger.info(f"retro_synthesis: setting: max_step_time = {max_step_time} (seconds)") + logger.info(f"retro_synthesis: setting: max_steps = {max_steps} (from UI)") + logger.info(f"retro_synthesis: setting: n_max_leaves_speed = {n_max_leaves_speed}") + logger.info(f"retro_synthesis: setting: n_max_leaves_speed_heavy_cut = {n_max_leaves_speed_heavy_cut}") + + run_is_finished = False + run_is_finished_with_status = "RUNNING" # Options are WAITING, RUNNING, DONE, ERROR + + retrosynthesis: Dict[str, Any] = {} + retrosynthesis["status"] = run_is_finished_with_status + retrosynthesis["routes"] = [] + + # data_rxnfps = np.load('pca_rxnfps_16_transform.npy') + # data_rxnfps = np.load('simpler_pca_rxnfps_16_transform.npy') + # PCA object to embed new predicted steps + with open(input_data["pca_model_filename"], "rb") as f_pca: + # e.g. "neb_pca_object_16.pkl" + pca_model = pickle.load(f_pca) + logger.info("retro_synthesis: Loading neb_pca_object_16.pkl") + + # Reaction fingerprints of dataset in PCA space + # with bz2.open( + with open( + # e.g. "neb_tree_data_pca_rxnfps_16_leaf_size_8_linear_plus_tree.pkl.bz2" + input_data["tree_data_dict_pca_filename"], + "rb", + ) as f: + tree_data_dict_pca = pickle.load(f) + logger.info("retro_synthesis: Loading tree_data_dict_pca_filename") + + logger.info("retro_synthesis: starting...") + logger.info("retro_synthesis: target product: " + str(product)) + # product = multistep_standardize(product, model_settings=model_settings) + product = multistep_standardize(product) + logger.info("retro_synthesis: target product: " + str(product) + " (after canonical)") + + my_exclude = set() + # add the product + my_exclude.add(product) + + # Loading models + fw_model_path = input_data["forward_model_path"] + re_model_path = input_data["backward_model_path"] + fw_model = load_single_step_prediction_model(model_path=fw_model_path, n_best=1, beam_size=10, max_length=300) + re_model = load_single_step_prediction_model(model_path=re_model_path, topn=10, n_best=10, beam_size=15, max_length=300) + + fingerprints_model_path = input_data["fingerprints_model_path"] + fingerprints_model = load_fingerprints_model(model_path=fingerprints_model_path) + + # This is the dictionary to save the confidences of every reaction smile + saved_classes: Dict[str, str] = {} + saved_confidences: Dict[str, float] = {} + saved_reactions: Dict[str, Set[str]] = {} + + logger.info("retro_synthesis: product_via_run_rxn_prediction " + str(product)) + retro_predictions_good = run_rxn_prediction(product, fw_model=fw_model, re_model=re_model) + + # This may include very similar reactions ... like `duplicates` or + # trivial modifications. + saved_reactions = add_to_saved_reactions(product, retro_predictions_good, saved_reactions) + logger.info("len(retro_predictions_good) before cleaning: " + str(len(retro_predictions_good))) + if clean and len(retro_predictions_good) > 0: + # Sort the list of strings by string len + sorted_retro_predictions_good = sorted(retro_predictions_good, key=lambda el: len(el)) + + # Filter the "trivial" duplicates + r_smiles = [r + ">>" + product for r in sorted_retro_predictions_good] + fp = smiles2fp(r_smiles, pca_model, fingerprints_model=fingerprints_model) + keep = [] + keep.append(r_smiles[0]) + n = len(r_smiles) + for i in range(1, n): + r = r_smiles[i] + if check_reaction(keep, r, r_smiles, fp): + # print('keep',i) + keep.append(r) + + retro_predictions_good = [r.split(">>")[0] for r in keep] + + logger.info("len(retro_predictions_good) after cleaning: " + str(len(retro_predictions_good))) + + tree_dict = initialize_tree(product, retro_predictions_good) + + # First initialization + full_node_list_from_tree = [tree_dict[node].name for node in tree_dict] + + # Finds lever after level, all the reaction nodes and + # create a list of reaction_smiles stored in `reaction_smiles_txt` + # this is appended. More over it creates a mapping node to reaction_smile + # 2 outputs, 3 inputs + + # Initialization + reaction_smiles_txt = [] + map_node2react_smile: Dict[str, str] = {} + node_reaction_list: List[str] = [] + reaction_smiles_txt2fp_dict: Dict[str, Any] = {} + already_finished: List[List[int]] = [] + saved_scores: Dict[str, Any] = { + "global": {}, + "local": {}, + "part": {}, + "orig_part": {}, + "part_inds": {}, + "orig_part_inds": {}, + "part_len_steps": {}, + "part_steps": {}, + "orig_part_dists": {}, + } + results_v_l_score = [] + results_v_l_score_dict = {} + n_finished = 0 + node_id_terminated: Set[str] = set() + sources: List[Dict[str, Any]] = [] + + # Extract for the first time the reaction smiles from tree + map_node2react_smile, node_reaction_list = update_reaction_smiles_from_tree(tree_dict, map_node2react_smile, node_reaction_list) + + # save also in the txt list file + reaction_smiles_txt = [map_node2react_smile[s] for s in node_reaction_list] + + reaction_smiles_txt2fp_dict = compute_reaction_smiles_txt2fp_dict( + reaction_smiles_txt2fp_dict, + reaction_smiles_txt, + pca_model, + fingerprints_model=fingerprints_model, + ) + + reaction_smiles_fp = np.array([reaction_smiles_txt2fp_dict[item] for item in reaction_smiles_txt]) + + logger.info("retro_synthesis: l_select: 0 before") + logger.info("retro_synthesis: len(map_node2react_smile): " + str(len(node_reaction_list))) + logger.info("retro_synthesis: len(reaction_smiles_txt2fp_dict): " + str(len(reaction_smiles_txt2fp_dict))) + logger.info("retro_synthesis: len(reaction_smiles_txt): " + str(len(reaction_smiles_txt))) + logger.info("retro_synthesis: len(tree_dict): " + str(len(tree_dict))) + logger.info("retro_synthesis: reaction_smiles_fp.shape: " + str(reaction_smiles_fp.shape)) + + # list_uniq_all_level = {} + + l_select = 1 + + list_of_list_of_1_inds = select_l_nb_node_id(tree_dict, node_reaction_list, l_select) + # Correction to remove empty lists [[],[],[]...] + list_of_list_of_1_inds = [l1 for l1 in list_of_list_of_1_inds if len(l1) > 0] + list_of_list_of_1_inds_ = [[0, l1[0]] for l1 in list_of_list_of_1_inds if len(l1) > 0] + list_uniq_arti = filter_similar_node_id( + list_of_list_of_1_inds_, + node_reaction_list, + map_node2react_smile, + reaction_smiles_txt2fp_dict, + ) + list_uniq = [[l1[1]] for l1 in list_uniq_arti] + # list_uniq_all_level[1] = [l1 for l1 in list_uniq] + + logger.info(f"retro_synthesis: list_uniq at level L=1: {list_uniq}") + n_opena_at_l1 = len(list_uniq) # this is used in the coverage + + # Check until L=2 + level = 2 + finished_dict, _ = get_all_the_finished_reactions_up_to(tree_dict, node_reaction_list, level) + logger.debug(f"retro_synthesis: finished_dict_DEBUG {len(finished_dict)} {finished_dict}") + logger.debug(f"retro_synthesis: node_reaction_list_DEBUG {len(node_reaction_list)}") + logger.debug(f"retro_synthesis: tree_dict_DEBUG {len(tree_dict)}") + + # Print finished reactions (if present) + logger.info("retro_synthesis: List of finished reactions:") + for _, v in finished_dict.items(): + if len(v) > 0: + for v_ in v: + if v_ in already_finished: + logger.info("retro_synthesis: already_finished " + str(v_)) + else: + logger.info("retro_synthesis: just_finished " + str(v_) + " including level: " + str(level)) + already_finished.append(v_) + saved_scores = get_score_for_a_single_path( + v_, + tree_data_dict_pca, + node_reaction_list, + map_node2react_smile, + reaction_smiles_txt2fp_dict, + saved_scores, + max_dist_along_cutoff, + ) + + single_result_v_l_score = fetch_score( + v_, + saved_scores, + node_reaction_list, + tree_dict, + full_node_list_from_tree, + map_node2react_smile, + True, + ) + results_v_l_score.append(single_result_v_l_score) + + l_str = single_result_v_l_score["l_str"] + results_v_l_score_dict[l_str] = single_result_v_l_score + n_finished = n_finished + 1 + logger.info("retro_synthesis: Check_n_finished_vs_len(results_v_l_score_dict)_posA:") + logger.info("retro_synthesis: " + str(n_finished) + " " + str(len(results_v_l_score_dict))) + + logger.info("retro_synthesis: Aggregate: save final_score, single step synthesis") + length = len(single_result_v_l_score["v"]) # len() = 1 + score = results_v_l_score_dict[l_str]["score"] + score_initial = score * length + results_v_l_score_dict[l_str]["final_score"] = [ + score, + ] + + # list of list + results_v_l_score_dict[l_str]["complete_reactions"] = [[]] + logger.info("retro_synthesis: Aggregated_final_score l_str/final_score/complete_reactions") + logger.info("retro_synthesis: " + str(results_v_l_score_dict[l_str]["final_score"])) + logger.info("retro_synthesis: " + str(results_v_l_score_dict[l_str]["v"])) + logger.info("retro_synthesis: " + str(results_v_l_score_dict[l_str]["l"])) + logger.info("retro_synthesis: " + str(results_v_l_score_dict[l_str]["complete_reactions"])) + logger.info("retro_synthesis ---") + + logger.info("retro_synthesis: Assemble full smiles reactions n_finished = " + str(n_finished)) + for k_, v_ in results_v_l_score_dict.items(): + # Here the if is not necessary, we passed only once + # later it may be useful, but a priori longer path + # can finish and open new combinations ... + # if not 'full_smiles' in results_v_l_score_dict: + results_v_l_score_dict[k_]["full_smiles"] = get_full_text_sets_of_reactions(v_, results_v_l_score_dict) + + results_v_l_score_dict[k_]["source"] = [hash_str(str(item)) for item in results_v_l_score_dict[k_]["full_smiles"]] + + l_str_present = set([item["l_str"] for item in sources]) + for k_, v_ in results_v_l_score_dict.items(): + for i in range(len(results_v_l_score_dict[k_]["source"])): + if results_v_l_score_dict[k_]["l_str"] in l_str_present: + continue + dict_ = {} + dict_["source"] = results_v_l_score_dict[k_]["source"][i] + dict_["final_score"] = results_v_l_score_dict[k_]["final_score"][i] + dict_["l_str"] = results_v_l_score_dict[k_]["l_str"] + message_ = "retro_synthesis: SOURCE: " + str(dict_["source"]) + " " + str(dict_["final_score"]) + " " + str(dict_["l_str"]) + logger.info(message_) + sources.append(dict_) + + logger.info("retro_synthesis: Check_n_finished_vs_len(results_v_l_score_dict)_posB: " + str(n_finished) + " " + str(len(results_v_l_score_dict))) + + logger.info( + "retro_synthesis: results_v_l_score len(): " + str(len(results_v_l_score)) + " " + "results_v_l_score.pkl at level: " + str(level) + " " + "0" + ) + + logger.info("retro_synthesis: saved_scores: " + str(len(saved_scores)) + " saved_scores.pkl at level: " + str(level) + " 0") + + if level >= max_levels: + run_is_finished = True + + time2 = time.time() + message_ = "retro_synthesis: TIMING_LOOP_STEP " + str(time2 - time0) + " level: " + str(level) + " " + "n_finished: " + str(n_finished) + logger.info(message_) + + skip_set: Set[str] = set() + leaves_purged_set: Set[str] = set() + + # NOTE: LOOP step + for step in range(100): + time1 = time.time() + logger.info(f"retro_synthesis: step: {step}") + + if level >= max_levels: + run_is_finished = True + + if run_is_finished: + logger.info("retro_synthesis: run_is_finished " + str(run_is_finished) + " current level " + str(level)) + break + + # Compounds l_select = 2,4,6,.. even numbers + l_select = l_select + 1 + + sanity_status = check_sanity_tree(tree_dict, leaves_purged_set) + + if sanity_status["check"]: + # skip_set = set() + for name_ in sanity_status["list_of_node_more_to_stop"]: + skip_set.add(name_) + parent_ = tree_dict[name_].parent + name_c = name_.replace("_MORE", "_STOP") + tree_dict[name_c] = Node(name_c, parent=parent_) + + logger.info("retro_synthesis: INFO_check_sanity_tree_len(skip_set): " + str(len(skip_set))) + + # Since the tree_dict may have been updated + # do update also the node_list + if not len(full_node_list_from_tree) == len(tree_dict): + for node in tree_dict: + if node not in full_node_list_from_tree: + full_node_list_from_tree.append(node) + + leaves_to_be_propagated = [] + for node in tree_dict: + if "MORE___" in node: + if not tree_dict[node].depth == l_select: + continue + # if node in skip_set: + # print( + # "list_of_node_more_to_stop_leaves_to_be_propagated_SKIP", node + # ) + # continue + if node in leaves_purged_set: + logger.info(f"retro_synthesis: LEAF_IN_leaves_purged_set_SKIP {node}") + continue + if node not in sanity_status["more_status"]: + continue + if sanity_status["more_status"][node] == "BAD": + continue + message_ = "retro_synthesis: LEAF_TO_BE_PROPAGATED l_select:" + message_ = message_ + " " + str(l_select) + message_ = message_ + " " + str(tree_dict[node].depth) + message_ = message_ + " NODE: " + str(node) + logger.info(message_) + leaves_to_be_propagated.append(node) + + logger.info( + f"retro_synthesis: LEAVES_TO_BE_PROPAGATED_ORIG: l_select = {l_select}, len(leaves_to_be_propagated) = {len(leaves_to_be_propagated)}" + ) + + if len(leaves_to_be_propagated) < 1: + # There is nothing to be propagated, the run is finished, exit the loop + logger.info(f"retro_synthesis: finished: no leaves_to_be_propagated {len(leaves_to_be_propagated)}") + run_is_finished = True + logger.info("retro_synthesis: run_is_finished {run_is_finished} current level {level}") + logger.debug("retro_synthesis: run_is_finished {run_is_finished} current level {level}") + break + + # Here we filter the tree... + if step % pruning_steps == 0 or len(leaves_to_be_propagated) > n_max_leaves_speed: + max_num_leaves_to_be_propagated = max_num_leaves_to_be_propagated_init + hard_reset = False + + # Backup leaves_to_be_propagated + leaves_to_be_propagated_bak = deepcopy(leaves_to_be_propagated) + logger.info(f"retro_synthesis: pruning: here we filter the tree: step = {step}, l_select = {l_select}") + + heavy_pruning = False + try: + n_pgr = len(leaves_to_be_propagated) + if n_pgr > n_max_leaves_speed_heavy_cut: + heavy_pruning = True + + if not pruning_steps == 1: + logger.info(f"retro_synthesis: setting pruning_steps = 1 from {pruning_steps}") + pruning_steps = 1 + + l_list_of_leaves = [] + l_list_of_leaves_bak = [] + for leaf_ in leaves_to_be_propagated: + li = [node for node in get_path_to_node(tree_dict[leaf_])] + l_list_of_leaves.append([full_node_list_from_tree.index(i) for i in li]) + l_list_of_leaves_bak.append([full_node_list_from_tree.index(i) for i in li]) + + keep_index: List[int] = [] + logger.info(f"retro_synthesis: HEAVY PRUNING: level = {level}, len(leaves_to_be_propagated) = {len(leaves_to_be_propagated)}") + + l_list_single = l_list_of_leaves[0] + for imytry in range(0, 10): + ipos_limit = len(l_list_single) - 2 * imytry + if ipos_limit < 1: + break + logger.info(f"retro_synthesis: HEAVY PRUNING: ipos_limit = {ipos_limit}, attempt = {imytry}, len() = {len(l_list_single)}") + + l_list_keep = [] + keep_index = [] + n_rejecting = 0 + l_list_keep.append(l_list_of_leaves[0]) + keep_index.append(0) + for il1_ in range(1, len(l_list_of_leaves)): + l_list_single = l_list_of_leaves[il1_] + # logger.info( + # f"retro_synthesis: HEAVY PRUNING: trying {l_list_single}" + # ) + take_, _ = check_alternatives_among(ipos_limit, l_list_single, l_list_keep) + if take_: + l_list_keep.append(l_list_single) + keep_index.append(il1_) + # logger.info( + # f"retro_synthesis: HEAVY PRUNING: adding {l_list_single}" + # ) + else: + n_rejecting = n_rejecting + 1 + pass + # logger.info( + # f"retro_synthesis: HEAVY PRUNING: n_sim = {n_sim} rejecting {l_list_single}" + # ) + logger.info(f"retro_synthesis: HEAVY PRUNING: attempt {imytry} tot num. {n_pgr} keeping {len(l_list_keep)}") + logger.info(f"retro_synthesis: HEAVY PRUNING: attempt {imytry} n_rejecting = {n_rejecting}") + if len(l_list_keep) < n_max_leaves_speed_heavy_cut: + logger.info( + f"retro_synthesis: HEAVY PRUNING: attempt {imytry} len(l_list_keep) {len(l_list_keep)} < {n_max_leaves_speed_heavy_cut}" + ) + break + l_list_of_leaves = [item for item in l_list_keep] + logger.info(f"retro_synthesis: HEAVY PRUNING: attempt {imytry} not enough {len(l_list_keep)}") + if heavy_pruning: + keep_index = [l_list_of_leaves_bak.index(item) for item in l_list_keep] + leaves_to_be_propagated = [leaves_to_be_propagated_bak[i] for i in keep_index] + # update backup + leaves_to_be_propagated_bak = deepcopy(leaves_to_be_propagated) + except Exception: + leaves_to_be_propagated = deepcopy(leaves_to_be_propagated_bak) + logger.debug("retro_synthesis: HEAVY PRUNING FAILED WITH Exception") + logger.info("retro_synthesis: HEAVY PRUNING FAILED WITH Exception") + + # compute scores ... + logger.info( + f"retro_synthesis: before PRUNING len(leaves_to_be_propagated) {len(leaves_to_be_propagated)} heavy_pruning = {heavy_pruning}" + ) + v_list_of_leaves = [] + for leaf_ in leaves_to_be_propagated: + vi = [node for node in get_path_to_node(tree_dict[leaf_])][1::2] + v_list_of_leaves.append([node_reaction_list.index(i) for i in vi]) + + saved_scores, saved_here = get_score_for_many_single_path( + v_list_of_leaves, + tree_data_dict_pca, + node_reaction_list, + map_node2react_smile, + reaction_smiles_txt2fp_dict, + saved_scores, + max_dist_along_cutoff, + ) + + try: + itry = 0 + while True: + # if heavy_pruning: + # logger.info( + # f"retro_synthesis: skip PRUNING because of heavy_pruning = {heavy_pruning}" + # ) + # break + logger.info( + f"retro_synthesis: entering PRUNING len(leaves_to_be_propagated) {len(leaves_to_be_propagated)} heavy_pruning = {heavy_pruning}" + ) + v_list_of_leaves = [] + for leaf_ in leaves_to_be_propagated: + vi = [node for node in get_path_to_node(tree_dict[leaf_])][1::2] + v_list_of_leaves.append([node_reaction_list.index(i) for i in vi]) + + saved_scores, saved_here = get_score_for_many_single_path( + v_list_of_leaves, + tree_data_dict_pca, + node_reaction_list, + map_node2react_smile, + reaction_smiles_txt2fp_dict, + saved_scores, + max_dist_along_cutoff, + ) + + if step == 0: + ioniq = 4 # at the beginning we filter less + elif step == 1: + ioniq = 2 + else: + ioniq = 1 + + list_best = select_from_saved_here(saved_here, max_num_leaves_to_be_propagated * ioniq) + logger.info(f"retro_synthesis: max_num_leaves_to_be_propagated: {max_num_leaves_to_be_propagated}, multi: {ioniq}") + new_leaves_to_be_propagated = [] + for i__ in range(len(list_best)): + item = list_best[i__] + ipos = [i for i in range(len(v_list_of_leaves)) if v_list_of_leaves[i] == item] + for i in ipos: + leaf_ = leaves_to_be_propagated[i] + message_ = "retro_synthesis: LEAF_TO_BE_PROPAGATED_AFTER: " + message_ = message_ + "level " + message_ = message_ + str(level) + " " + message_ = message_ + str(i) + " " + message_ = message_ + str(tree_dict[leaf_].depth) + " " + message_ = message_ + "NODE: " + str(leaf_) + logger.info(message_) + if leaf_ not in new_leaves_to_be_propagated: + new_leaves_to_be_propagated.append(leaf_) + if leaf_ in sanity_status["to_complete"]: + leaves_to_complete = sanity_status["to_complete"][leaf_] + for le in leaves_to_complete: + logger.info("retro_synthesis: LEAF_TO_COMPLETE: " + str(tree_dict[le].depth) + " NODE: " + str(le)) + if le not in new_leaves_to_be_propagated: + new_leaves_to_be_propagated.append(le) + + leaves_purged = [i for i in leaves_to_be_propagated if i not in new_leaves_to_be_propagated] + + # Update the accumulated set of nodes that should not be used + leaves_purged_set = leaves_purged_set.union(set(leaves_purged)) + + leaves_to_be_propagated = [i for i in new_leaves_to_be_propagated] + + logger.info( + f"""retro_synthesis: LOOP_LEAVES_TO_BE_PROPAGATED_AFTER_PURGED: loop: {itry}, level = {level}, l_select = {l_select}, len(leaves_to_be_propagated) = {len(leaves_to_be_propagated)}, len(leaves_purged_set) = {len(leaves_purged_set)}, max_num_leaves_to_be_propagated: {max_num_leaves_to_be_propagated}, multi: {ioniq}""" + ) + + if len(leaves_to_be_propagated) <= n_max_leaves_speed: + logger.info( + f"retro_synthesis: len(leaves_to_be_propagated) {len(leaves_to_be_propagated)} vs. n_max_leaves_speed: {n_max_leaves_speed}" + ) + break + + ioniq = 1 + if hard_reset: + pass + elif max_num_leaves_to_be_propagated == 3: + max_num_leaves_to_be_propagated = 2 + logger.info(f"retro_synthesis: max_num_leaves_to_be_propagated: {max_num_leaves_to_be_propagated}") + continue + elif max_num_leaves_to_be_propagated == 2: + max_num_leaves_to_be_propagated = 1 + logger.info(f"retro_synthesis: max_num_leaves_to_be_propagated: {max_num_leaves_to_be_propagated}") + hard_reset = True + continue + else: + max_num_leaves_to_be_propagated = max_num_leaves_to_be_propagated - 3 + if max_num_leaves_to_be_propagated <= 1: + max_num_leaves_to_be_propagated = 1 + hard_reset = True + logger.info( + f"retro_synthesis: purge: hard_reset: {hard_reset}" + f"retro_synthesis: max_num_leaves_to_be_propagated: {max_num_leaves_to_be_propagated}" + ) + continue + + itry = itry + 1 + if itry > 5: + break + + logger.info( + f"""retro_synthesis: LEAVES_TO_BE_PROPAGATED_AFTER_PURGED: level = {level}, l_select = {l_select}, len(leaves_to_be_propagated) = {len(leaves_to_be_propagated)}, len(leaves_purged_set) = {len(leaves_purged_set)}, max_num_leaves_to_be_propagated: {max_num_leaves_to_be_propagated}, multi: {ioniq}, hard_reset: {hard_reset}""" + ) + + # Tree is purged, set original value `max_num_leaves_to_be_propagated` + max_num_leaves_to_be_propagated = max_num_leaves_to_be_propagated_init + + except Exception: + leaves_to_be_propagated = deepcopy(leaves_to_be_propagated_bak) + logger.debug( + f"retro_synthesis: PRUNING Exception using leaves_to_be_propagated_bak len(leaves_to_be_propagated_bak) {len(leaves_to_be_propagated_bak)}" + ) + logger.info( + f"retro_synthesis: PRUNING Exception using leaves_to_be_propagated_bak len(leaves_to_be_propagated_bak) {len(leaves_to_be_propagated_bak)}" + ) + + # 2nd pass of heavy pruning: `2ND_HEAVY PRUNING` + leaves_to_be_propagated_bak = deepcopy(leaves_to_be_propagated) + heavy_pruning = False + try: + n_pgr = len(leaves_to_be_propagated) + if n_pgr > n_max_leaves_speed_heavy_cut: + heavy_pruning = True + logger.info(f"retro_synthesis: entering 2ND_HEAVY PRUNING n_pgr = {n_pgr}") + + l_list_of_leaves = [] + l_list_of_leaves_bak = [] + for leaf_ in leaves_to_be_propagated: + li = [node for node in get_path_to_node(tree_dict[leaf_])] + l_list_of_leaves.append([full_node_list_from_tree.index(i) for i in li]) + l_list_of_leaves_bak.append([full_node_list_from_tree.index(i) for i in li]) + + keep_index = [] + logger.info(f"retro_synthesis: 2ND_HEAVY PRUNING: level = {level}, len(leaves_to_be_propagated) = {len(leaves_to_be_propagated)}") + + l_list_single = l_list_of_leaves[0] + for imytry in range(0, 10): + ipos_limit = len(l_list_single) - 2 * imytry + if ipos_limit < 1: + break + logger.info(f"retro_synthesis: HEAVY PRUNING: ipos_limit = {ipos_limit}, attempt = {imytry}, len() = {len(l_list_single)}") + + l_list_keep = [] + keep_index = [] + n_rejecting = 0 + l_list_keep.append(l_list_of_leaves[0]) + keep_index.append(0) + for il1_ in range(1, len(l_list_of_leaves)): + l_list_single = l_list_of_leaves[il1_] + take_, _ = check_alternatives_among(ipos_limit, l_list_single, l_list_keep) + if take_: + l_list_keep.append(l_list_single) + keep_index.append(il1_) + else: + n_rejecting = n_rejecting + 1 + pass + logger.info(f"retro_synthesis: 2ND_HEAVY PRUNING: attempt {imytry} tot num. {n_pgr} keeping {len(l_list_keep)}") + logger.info(f"retro_synthesis: 2ND_HEAVY PRUNING: attempt {imytry} n_rejecting = {n_rejecting}") + if len(l_list_keep) < n_max_leaves_speed_heavy_cut: + logger.info( + f"retro_synthesis: 2ND_HEAVY PRUNING: attempt {imytry} len(l_list_keep) {len(l_list_keep)} < {n_max_leaves_speed_heavy_cut}" + ) + break + l_list_of_leaves = [item for item in l_list_keep] + logger.info(f"retro_synthesis: 2ND_HEAVY PRUNING: attempt {imytry} not enough {len(l_list_keep)}") + if heavy_pruning: + keep_index = [l_list_of_leaves_bak.index(item) for item in l_list_keep] + leaves_to_be_propagated = [leaves_to_be_propagated_bak[i] for i in keep_index] + # update backup + leaves_to_be_propagated_bak = deepcopy(leaves_to_be_propagated) + logger.info( + f"""retro_synthesis: LEAVES_TO_BE_PROPAGATED_AFTER_PURGED_and_2ND_HEAVY PRUNING: level = {level}, len(leaves_to_be_propagated) = {len(leaves_to_be_propagated)}""" + ) + + # Tree is purged, set original value `max_num_leaves_to_be_propagated` + except Exception: + leaves_to_be_propagated = deepcopy(leaves_to_be_propagated_bak) + logger.debug("retro_synthesis: 2ND_HEAVY PRUNING FAILED WITH Exception") + logger.info("retro_synthesis: 2ND_HEAVY PRUNING FAILED WITH Exception") + # end of 2nd pass of heavy pruning + + # This option in the last resort, with the 2nd_heavy pruning, + # it should seldom pass in the following if statement + if len(leaves_to_be_propagated) > n_max_leaves_speed_heavy_cut: + leaves_to_be_propagated = deepcopy(leaves_to_be_propagated_bak) + logger.info("retro_synthesis: call select_prefix_to_keep: " + str(len(leaves_to_be_propagated))) + prefix = select_prefix_to_keep(node_reaction_list, saved_here) + logger.info(f"retro_synthesis: call select_prefix_to_keep: prefix: {prefix}") + new_list = [s for s in leaves_to_be_propagated if s.startswith(prefix)] + logger.info(f"retro_synthesis: call select_prefix_to_keep: len(new_list): {len(new_list)}") + if len(new_list) > 0: + leaves_to_be_propagated = [item for item in new_list] + logger.info("retro_synthesis: call select_prefix_to_keep: " + str(len(leaves_to_be_propagated))) + else: + leaves_to_be_propagated = deepcopy(leaves_to_be_propagated_bak) + logger.info("retro_synthesis: call select_prefix_to_keep: KEEP ALL") + # end of the select prefix section + + for leaf in leaves_to_be_propagated: + ( + tree_dict, + saved_reactions, + saved_confidences, + saved_classes, + ) = propagate_leaf( + leaf, + tree_dict, + saved_reactions, + clean, + pca_model, + saved_confidences, + saved_classes, + fw_model=fw_model, + re_model=re_model, + fingerprints_model=fingerprints_model, + ) + + time2 = time.time() + if n_finished > 0 and (time2 - time0) > max_step_time: + run_is_finished = True + logger.info( + f"""retro_synthesis: TIMING_TMP_in_leaves_to_be_propagated_step_time_END {time2 - time0} max_step_time: {max_step_time}, n_finished: {n_finished}""" + ) + logger.info(f"retro_synthesis: WALLTIME_run_is_finished: {run_is_finished}, n_finished: {n_finished}, level: {level}, time2: {time2}") + break + + if not len(full_node_list_from_tree) == len(tree_dict): + for node in tree_dict: + if node not in full_node_list_from_tree: + full_node_list_from_tree.append(node) + + level = l_select + 1 # `level` here is an odd number 3,5,7,... + + # operations after updating `tree_dict`: extract the reaction smiles from tree + map_node2react_smile, node_reaction_list = update_reaction_smiles_from_tree(tree_dict, map_node2react_smile, node_reaction_list) + + # save also in the txt list file + reaction_smiles_txt = [map_node2react_smile[s] for s in node_reaction_list] + reaction_smiles_txt2fp_dict = compute_reaction_smiles_txt2fp_dict( + reaction_smiles_txt2fp_dict, + reaction_smiles_txt, + pca_model, + fingerprints_model=fingerprints_model, + ) + reaction_smiles_fp = np.array([reaction_smiles_txt2fp_dict[item] for item in reaction_smiles_txt]) + + # We have level L=3,5,7,... + list_of_list_of_l_inds = select_l_nb_filter_node_id(tree_dict, node_reaction_list, level, list_uniq) + list_of_list_of_l_inds = [l1 for l1 in list_of_list_of_l_inds if len(l1) > 0] + + # list_uniq = [l1 for l1 in list_of_list_of_l_inds if len(l1) > 0] + + list_uniq = filter_similar_node_id_with_tree( + tree_dict, + list_of_list_of_l_inds, + node_reaction_list, + map_node2react_smile, + reaction_smiles_txt2fp_dict, + ) + + if run_is_finished: + logger.info(f"retro_synthesis: run_is_finished {run_is_finished} current level {level}") + break + + # init `node_to_continue` + node_to_continue = set([item[-1] for item in list_uniq]) + node_id_to_continue = set([node_reaction_list[i] for i in node_to_continue]) + leaves_to_be_propagated = [node for node in node_id_to_continue] + l_select = l_select + 1 + logger.info(f"retro_synthesis: l_select {l_select} {len(leaves_to_be_propagated)}") + + tree_dict = split_precursors_with_wait(tree_dict, leaves_to_be_propagated, node_id_terminated, my_exclude) + + if not len(full_node_list_from_tree) == len(tree_dict): + for node in tree_dict: + if node not in full_node_list_from_tree: + full_node_list_from_tree.append(node) + level = l_select + 1 + finished_dict, _ = get_all_the_finished_reactions_up_to(tree_dict, node_reaction_list, level) + + # Append - just in case... to make sure order of existing is not changed + # full_node_list_from_tree=[tree_dict[node].name for node in tree_dict] + for node in tree_dict: + if node not in full_node_list_from_tree: + full_node_list_from_tree.append(node) + + # Print finished reactions (if present) + logger.info("retro_synthesis: List of finished reactions:") + just_finished_are = [] + for k, v in finished_dict.items(): + if len(v) > 0: + for v_ in v: + if v_ in already_finished: + logger.info("retro_synthesis: already_finished " + str(v_)) + else: + logger.info("retro_synthesis: just_finished " + str(v_) + " including level: " + str(level)) + already_finished.append(v_) + saved_scores = get_score_for_a_single_path( + v_, + tree_data_dict_pca, + node_reaction_list, + map_node2react_smile, + reaction_smiles_txt2fp_dict, + saved_scores, + max_dist_along_cutoff, + ) + + # print the score for the `just_finished` path + single_result_v_l_score = fetch_score( + v_, + saved_scores, + node_reaction_list, + tree_dict, + full_node_list_from_tree, + map_node2react_smile, + True, + ) + results_v_l_score.append(single_result_v_l_score) + + l_str = single_result_v_l_score["l_str"] + results_v_l_score_dict[l_str] = single_result_v_l_score + n_finished = n_finished + 1 + just_finished_are.append(single_result_v_l_score["l_str"]) + + logger.info( + "retro_synthesis: results_v_l_score len(): " + str(len(results_v_l_score)) + " results_v_l_score.pkl at level: " + str(level) + " B" + ) + + logger.info("retro_synthesis: saved_scores: " + str(len(saved_scores)) + " saved_scores.pkl at level: " + str(level) + " B") + + logger.info("retro_synthesis: ------------------------------") + + # Aggregate the branches giving complete RetroSynthesis + # score them, at this level this is done for the `just_finished` + + logger.info("retro_synthesis: Aggregate the branches giving complete RetroSynthesis") + logger.info("retro_synthesis: Aggregate:") + list_of_lists = [res_["l"] for k, res_ in results_v_l_score_dict.items()] + + # This should not be necessary ... we leave it for the moment as check + logger.info("retro_synthesis: Aggregate_original_list_of_lists " + str(len(list_of_lists)) + " " + str(list_of_lists)) + list_of_lists = [l1 for l1 in list_of_lists if not list_is_included(l1, list_of_lists)] + logger.info("retro_synthesis: Aggregate_cleaned_list_of_lists " + str(len(list_of_lists)) + " " + str(list_of_lists)) + + for l_str in just_finished_are: + jfin = results_v_l_score_dict[l_str]["l"][-1] + res = complete_reactions(list_of_lists, jfin) + logger.info("retro_synthesis: RESULT jfin/l_str/complete_reactions" + str(jfin) + " " + str(l_str) + " " + str(res)) + length = len(results_v_l_score_dict[l_str]["v"]) + score = results_v_l_score_dict[l_str]["score"] + score_initial = score * length + results_v_l_score_dict[l_str]["final_score"] = [] + results_v_l_score_dict[l_str]["complete_reactions"] = [] + # res = [[[],[]]] + # or res is [ + # [[1, 2, 3, 6, 7, 10], [1, 2, 3, 6, 9, 11]], + # [[1, 2, 3, 6, 7, 10], [1, 2, 3, 6, 9, 12]] + # ] + for r in res: + # for each alternative the score is initialized + # reset to the one of the selected `jfin` -> `l_str` + score_tmp = score_initial + # loop on the lists of one of the completed path + for l1 in r: + if len(l1) == 0: + continue + l_str_local = str(l1) + score_tmp = score_tmp + (results_v_l_score_dict[l_str_local]["score"] * len(results_v_l_score_dict[l_str_local]["v"])) + # save Aggregated results + results_v_l_score_dict[l_str]["final_score"].append(score_tmp) + # `r` is a list of lists + results_v_l_score_dict[l_str]["complete_reactions"].append(r) + logger.info( + "retro_synthesis: aggregated_final_score " + + "l_str/final_score/complete_reactions " + + str(results_v_l_score_dict[l_str]["final_score"]) + + " " + + str(results_v_l_score_dict[l_str]["v"]) + + " " + + str(results_v_l_score_dict[l_str]["l"]) + + " " + + str(results_v_l_score_dict[l_str]["complete_reactions"]) + ) + + logger.info("retro_synthesis: Assemble full smiles reactions n_finished = " + str(n_finished)) + + for k_, v_ in results_v_l_score_dict.items(): + # Here the if is not necessary, we passed only once + # later it may be useful, but a priori longer path + # can finish and open new combinations ... + # if not 'full_smiles' in results_v_l_score_dict: + results_v_l_score_dict[k_]["full_smiles"] = get_full_text_sets_of_reactions(v_, results_v_l_score_dict) + + results_v_l_score_dict[k_]["source"] = [hash_str(str(item)) for item in results_v_l_score_dict[k_]["full_smiles"]] + + l_str_present = set([item["l_str"] for item in sources]) + for k_, v_ in results_v_l_score_dict.items(): + for i in range(len(results_v_l_score_dict[k_]["source"])): + if results_v_l_score_dict[k_]["l_str"] in l_str_present: + continue + dict_ = {} + dict_["source"] = results_v_l_score_dict[k_]["source"][i] + dict_["final_score"] = results_v_l_score_dict[k_]["final_score"][i] + dict_["l_str"] = results_v_l_score_dict[k_]["l_str"] + sources.append(dict_) + + just_finished_are = [] + logger.info(f"retro_synthesis: End_of_Aggregate at level: {level}") + + # print a little report ... + logger.info( + "retro_synthesis: report:" + + " run_is_finished: " + + str(run_is_finished) + + " level: " + + str(level) + + " max_levels: " + + str(max_levels) + + " n_finished: " + + str(n_finished) + + " min_predictions: " + + str(min_predictions) + ) + + time2 = time.time() + logger.info("retro_synthesis: TIMING_LOOP_STEP " + str(time2 - time1) + " level: " + str(level) + " n_finished: " + str(n_finished)) + + if n_finished > 0 and good_coverage(results_v_l_score_dict, n_opena_at_l1): + if level < min_levels: + logger.info(f"retro_synthesis: good_coverage True, but current level is {level} < {min_levels}") + else: + logger.info(f"retro_synthesis: good_coverage True, current level is {level}") + run_is_finished = True + + if level >= max_levels and n_finished >= min_predictions: + logger.info( + f"""retro_synthesis: run_is_finished {run_is_finished} level >= max_levels AND n_finished >= min_predictions level: {level} max_levels: {max_levels} n_finished: {n_finished} min_predictions: {min_predictions}""" + ) + run_is_finished = True + break + + if n_finished >= max_predictions: + logger.info(f"""retro_synthesis: run_is_finished {run_is_finished} n_finished >= max_predictions {n_finished} {max_predictions}""") + run_is_finished = True + break + + if (time2 - time0) >= 180.0 and n_finished >= min_predictions: + logger.info( + f"""retro_synthesis: run_is_finished {run_is_finished} time {time2 - time0} >= 180. seconds AND n_finished >= min_predictions level: {level}, max_levels: {max_levels}, n_finished: {n_finished}, min_predictions: {min_predictions}""" + ) + run_is_finished = True + break + + if run_is_finished: + logger.info(f"retro_synthesis: run_is_finished {run_is_finished} current level {level}") + break + + if len(leaves_to_be_propagated) == 0: + logger.info(f"retro_synthesis: len(leaves_to_be_propagated)==0 {len(leaves_to_be_propagated)}") + break + + if (time2 - time1) > max_step_time: + run_is_finished = True + logger.info(f"""retro_synthesis: TIMING_LOOP_max_step_time_END {time2 - time1} max_step_time: {max_step_time}""") + logger.info(f"retro_synthesis: WALLTIME_run_is_finished: {run_is_finished} level: {level} time2: {time2}") + + if (time2 - time0) > max_total_time: + run_is_finished = True + logger.info(f"retro_synthesis: WALLTIME_TIMING_LOOP_max_total_time_END {time2 - time0} max_total_time: {max_total_time}") + logger.info(f"retro_synthesis: WALLTIME_run_is_finished: {run_is_finished} level: {level} time2: {time2}") + + if run_is_finished: + break + + if pruning_steps > 1 and (time2 - time0) >= 120.0: + logger.info(f"retro_synthesis: TIME: {time2-time0} level: {level} reducing pruning_steps: {pruning_steps} to 1") + pruning_steps = 1 + + logger.info(f"retro_synthesis: TIMING_LOOP_STEPS_TOT {(time2 - time0):0.3f} level: {level} n_finished: {n_finished}") + + # END ... SUMMARY + + if run_is_finished or len(leaves_to_be_propagated) == 0: + logger.info(f"retro_synthesis: END_SUMMARY_run_is_finished {run_is_finished}") + logger.info(f"retro_synthesis: n_finished = {n_finished}") + logger.info(f"retro_synthesis: finished level = {level}") + logger.info(f"retro_synthesis: len(leaves_to_be_propagated): {len(leaves_to_be_propagated)}") + logger.info("retro_synthesis: get_all_the_finished_reactions ...") + level_max = get_level_max(tree_dict) + finished_dict, _ = get_all_the_finished_reactions_up_to(tree_dict, node_reaction_list, level_max) + + # Print all finished reactions (if present) + logger.info("retro_synthesis: List of all finished reactions") + # + # Append - just in case... to make sure order of existing is not changed + for node in tree_dict: + if node not in full_node_list_from_tree: + full_node_list_from_tree.append(node) + # + for k, v in finished_dict.items(): + if len(v) > 0: + for v_ in v: + single_result_v_l_score = fetch_score( + v_, + saved_scores, + node_reaction_list, + tree_dict, + full_node_list_from_tree, + map_node2react_smile, + True, + ) + score_ = single_result_v_l_score["score"] + logger.info(f"retro_synthesis: finished {v_} with score {score_}") + + # Populate `retrosynthesis` is an object of the class Retrosynthesis + # `saved_confidences` is used to populate the object from `retrosynthesis` Class + retrosynthesis["routes"] = get_retrosynthesis(results_v_l_score_dict, sources, saved_confidences, saved_classes) + logger.info("retro_synthesis: retrosynthesis len( _routes_ ): " + str(len(retrosynthesis["routes"]))) + run_is_finished_with_status = "DONE" + retrosynthesis["status"] = run_is_finished_with_status + + if n_finished == 0: + # empty // nothing finished + retrosynthesis["status"] = "ERROR" + retrosynthesis["routes"] = [] + + logger.info("retro_synthesis: retrosynthesis status: " + str(retrosynthesis["status"])) + + time2 = time.time() + logger.info(f"retro_synthesis: TIME ini/end of retrosynthesis: {time2-time0}") + + return retrosynthesis diff --git a/src/rxn/neb/single_step_predictions.py b/src/rxn/neb/single_step_predictions.py new file mode 100755 index 0000000..e9b5d73 --- /dev/null +++ b/src/rxn/neb/single_step_predictions.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python + +from functools import lru_cache +from typing import List, Set + +from loguru import logger +from rxn.chemutils.tokenization import detokenize_smiles, tokenize_smiles +from rxn.onmt_utils import Translator + +from .utils.smiles import ( + from_complex_smile_to_fragments, + multistep_standardize, + multistep_standardize_fw, +) + + +@lru_cache(maxsize=5) +def load_single_step_prediction_model(model_path: str, **model_parameters) -> Translator: + """Load single step prediction model. + + Args: + model_path: path to the model. + + Returns: + loaded model. + """ + return Translator.from_model_path(model_path=model_path, **model_parameters) + + +def run_rxn_prediction(product: str, fw_model: Translator, re_model: Translator, verbose: bool = False) -> List[str]: + """Run RXN roundtrip predictions. + + Args: + product: product SMILES. + fw_model: forward model. + re_model: backward model. + verbose: verbose mode. Defaults to False. + + Returns: + list of predictions passing the roundtrip check. + """ + prod_std = multistep_standardize(product) + output_tokenize_smile = tokenize_smiles(prod_std) + retro_list = [ + [detokenize_smiles(result.text) for result in result_list] for result_list in re_model.translate_multiple_with_scores([output_tokenize_smile]) + ] + if verbose: + for retro_smiles in retro_list[0]: + print("retro_prediction:", retro_smiles) + if len(retro_list) == 0: + return [] + + # round-trip + back_pred_std = [] + for smile in retro_list[0]: + smile = multistep_standardize_fw(smile) + input_tokenize_smile = tokenize_smiles(smile) + output_tokenize_smile = fw_model.translate_single(input_tokenize_smile) + out_smile = detokenize_smiles(output_tokenize_smile) + if out_smile in [product, prod_std]: + back_pred_std.append(smile) + if verbose: + print("OK", smile, out_smile) + else: + if verbose: + print("FAIL", smile, out_smile) + pass + if len(back_pred_std) == 0: + return [] + + # remove retro prediction if one of the precursor is the target product + retro_predictions_: Set[str] = set() + for _, r in enumerate(back_pred_std): + can_fragments_, s_ = from_complex_smile_to_fragments(r) + if verbose: + print("CHECK:", r, "<>", s_, can_fragments_) + if prod_std not in can_fragments_: + retro_predictions_.add(s_) + else: + string_log = str(product) + " " + str(can_fragments_) + logger.info("run_rxn_prediction: RETRO_PREDICTION_DISCARDED_product_in_fragments " + string_log) + good_retro_predictions = list(retro_predictions_) + + return good_retro_predictions diff --git a/src/rxn/neb/utils/__init__.py b/src/rxn/neb/utils/__init__.py new file mode 100644 index 0000000..e2042e8 --- /dev/null +++ b/src/rxn/neb/utils/__init__.py @@ -0,0 +1 @@ +"""Utils module.""" diff --git a/src/rxn/neb/utils/core.py b/src/rxn/neb/utils/core.py new file mode 100644 index 0000000..4069c19 --- /dev/null +++ b/src/rxn/neb/utils/core.py @@ -0,0 +1,924 @@ +"""Core utilities.""" +import hashlib +import itertools +from copy import deepcopy +from typing import Any, Dict, List, Set + +import numpy as np +import regex as re +from anytree import Node as NodeBase +from loguru import logger + +from ..availability import is_available +from ..fingerprints import compute_rxnfp as rxnfp +from ..single_step_predictions import run_rxn_prediction +from .general import check_alternatives, get_l_from_tree, get_path_to_node +from .smiles import from_complex_smile_to_fragments, try_further + + +class Node(NodeBase): + separator = "," + + +def one_smile2fp(smile, pca, fingerprints_model): + fp = rxnfp(smile, fingerprints_model=fingerprints_model) + array = np.array([fp], dtype="float32") + array = pca.transform(array) + array = array.reshape(-1) + fp = array.tolist() + # print('one_smile2fp len(fp):', len(fp)) + return fp + + +def smiles2fp(smiles, pca, fingerprints_model): + fps = [rxnfp(smile, fingerprints_model=fingerprints_model) for smile in smiles] + array = np.array(fps, dtype="float32") + # PCA reduction + array = pca.transform(array) + return array + + +def check_reaction(keep, r, reaction_smiles_txt, reaction_smiles_fp): + # + # Check reactions runs over a small number of reactions, + # we keep the distances in the original space. It is + # also more accurate. + # + logic = True + i = reaction_smiles_txt.index(r) + a = reaction_smiles_fp[i] + for r_ in keep: + j = reaction_smiles_txt.index(r_) + b = reaction_smiles_fp[j] + d = np.sum((a - b) ** 2, axis=0) + if d < 2.0: + return False + return logic + + +def update_reaction_smiles_from_tree(tree_dict, node2rs, node_list): + my_depths = set([tree_dict[node].depth for node in tree_dict]) + my_depths_max = max(my_depths) + 1 + my_select = [i for i in range(1, my_depths_max, 2)] + + for node in tree_dict: + if tree_dict[node].depth in my_select: + tmp = get_path_to_node(tree_dict[node].ancestors[-1]) + if len(tmp) >= 2: + # ancestor is a product (or an interm. product, not a reaction) + # tmp[-1] should point to an even element of the list `tmp` + ancestor = re.sub(r"^.*?___", "", tmp[-1]) + else: + ancestor = re.sub(r"^.*?___", "", tmp[0]) + precursors = re.sub(r"^.*?___", "", node) + if "NONE" in precursors: + continue # here OK either continue or pass + else: + s_ = precursors + ">>" + ancestor + if node not in node2rs: + node2rs[node] = s_ + node_list.append(node) + return node2rs, node_list + + +def get_compounds_in_list(list_): + comp_list = [] + for name_ in list_: + if "_MORE___" in name_ or "ROOT___" in name_ or "_END___" in name_: + smile = re.sub(r"^.*?___", "", name_) + # pref_ = re.sub(r"___.*", "", name_) + comp_list.append(smile) + return comp_list + + +def add_to_saved_reactions(prod, retro_predictions: List[str], saved_reactions: Dict[str, Set[str]]) -> Dict[str, Set[str]]: + if prod not in saved_reactions: + saved_reactions[prod] = set() + for retro in retro_predictions: + saved_reactions[prod].add(retro) + return saved_reactions + + +def initialize_tree(product, retro_predictions_good): + tree_dict = {} + root_ = "ROOT" + "___" + str(product) + tree_dict[root_] = Node(root_) + for i in range(len(retro_predictions_good)): + child = retro_predictions_good[i] + name_ = "R" + str(i) + "___" + str(child) + tree_dict[name_] = Node(name_, parent=tree_dict[root_]) + smile_split, s_ = from_complex_smile_to_fragments(child) + j = 0 + for s in smile_split: + if is_available(s) or try_further(s): + name_c = "R" + str(i) + "_C" + str(j) + "__END" + "___" + str(s) + j = j + 1 + else: + name_c = "R" + str(i) + "_C" + str(j) + "_MORE" + "___" + str(s) + j = j + 1 + tree_dict[name_c] = Node(name_c, parent=tree_dict[name_]) + return tree_dict + + +def propagate_leaf( + leaf, + tree_dict, + saved_reactions, + clean, + pca_model, + saved_confidences, + saved_classes, + fw_model, + re_model, + fingerprints_model, +): + product = re.sub(r"\w+___", "", leaf) + + if product in saved_reactions: + retro_predictions_good = [item for item in saved_reactions[product]] + else: + retro_predictions_good = run_rxn_prediction(product, fw_model=fw_model, re_model=re_model) + + saved_reactions = add_to_saved_reactions(product, retro_predictions_good, saved_reactions) + + if clean and len(retro_predictions_good) > 0: + # Sort the list of strings by string len + sorted_retro_predictions_good = sorted(retro_predictions_good, key=lambda el: len(el)) + + # Filter the duplicates + r_smiles = [r + ">>" + product for r in sorted_retro_predictions_good] + fp = smiles2fp(r_smiles, pca_model, fingerprints_model=fingerprints_model) + keep = [] + keep.append(r_smiles[0]) + n = len(r_smiles) + for i in range(1, n): + r = r_smiles[i] + if check_reaction(keep, r, r_smiles, fp): + keep.append(r) + + retro_predictions_good = [r.split(">>")[0] for r in keep] + + if len(retro_predictions_good) == 0: + child = "NONE" + leaf_n = re.sub(r"_MORE.*", "", leaf) + name_ = leaf_n + "_R0___NONE" + tree_dict[name_] = Node(name_, parent=tree_dict[leaf]) + else: + for j in range(len(retro_predictions_good)): + child = retro_predictions_good[j] + leaf_n = re.sub(r"_MORE.*", "", leaf) + name_ = leaf_n + "_R" + str(j) + "___" + str(child) + if name_ in tree_dict: + logger.debug("propagate_leaf: skip") + else: + tree_dict[name_] = Node(name_, parent=tree_dict[leaf]) + return tree_dict, saved_reactions, saved_confidences, saved_classes + + +def split_precursors_with_wait(tree_dict, my_list, node_wait, my_exclude): + for ime in range(len(my_list)): + name_ = my_list[ime] + if "__NONE" in name_: + continue + node = tree_dict[name_] + list_ = get_path_to_node(node) + ancestors_compounds = get_compounds_in_list(list_) + smile = re.sub(r"^.*?___", "", name_) + pref_ = re.sub(r"___.*", "", name_) + smile_split, s_ = from_complex_smile_to_fragments(smile) + j = 0 + # This loop is to check if there is one repeated fragment + # close loops (if one is stop, all are tagged as stop) + stop_ = False + for s in smile_split: + if s in ancestors_compounds: + if s in my_exclude: + stop_ = True + elif is_available(s) or try_further(s): + pass + elif len(s) <= 7: # [Na+] + pass + else: + stop_ = True + for s in smile_split: + if is_available(s) or try_further(s): + name_c = pref_ + "_C" + str(j) + "__END" + "___" + str(s) + j = j + 1 + else: + name_c = pref_ + "_C" + str(j) + "_MORE" + "___" + str(s) + j = j + 1 + if name_c in tree_dict: + raise Exception("split_precursors_with_wait: error: dist_pair_lists_max_far len(list_a) != len(list_b)") + elif stop_: + name_c = name_c.replace("__END", "_STOP") + name_c = name_c.replace("_MORE", "_STOP") + tree_dict[name_c] = Node(name_c, parent=tree_dict[name_]) + else: + tree_dict[name_c] = Node(name_c, parent=tree_dict[name_]) + + # node to wait + for name_ in node_wait: + if "__NONE" in name_: + continue + node = tree_dict[name_] + list_ = get_path_to_node(node) + ancestors_compounds = get_compounds_in_list(list_) + smile = re.sub(r"^.*?___", "", name_) + pref_ = re.sub(r"___.*", "", name_) + smile_split, s_ = from_complex_smile_to_fragments(smile) + j = 0 + # This loop is to check if there is one repeated fragment + # close loops (if one is stop, all are tagged as stop) + stop_ = False + for s in smile_split: + if s in ancestors_compounds: + if s in my_exclude: + stop_ = True + elif is_available(s) or try_further(s): + pass + else: + stop_ = True + + for s in smile_split: + if is_available(s) or try_further(s): + name_c = pref_ + "_C" + str(j) + "__END" + "___" + str(s) + j = j + 1 + else: + name_c = pref_ + "_C" + str(j) + "_WAIT" + "___" + str(s) + j = j + 1 + if name_c in tree_dict: + pass + elif stop_: + name_c = name_c.replace("__END", "_STOP") + name_c = name_c.replace("_MORE", "_STOP") + tree_dict[name_c] = Node(name_c, parent=tree_dict[name_]) + else: + tree_dict[name_c] = Node(name_c, parent=tree_dict[name_]) + return tree_dict + + +def compute_reaction_smiles_txt2fp_dict(reaction_smiles_txt2fp_dict, reaction_smiles_txt, pca, fingerprints_model): + for s_ in reaction_smiles_txt: + if s_ not in reaction_smiles_txt2fp_dict: + reaction_smiles_txt2fp_dict[s_] = one_smile2fp(s_, pca, fingerprints_model) + return reaction_smiles_txt2fp_dict + + +def check_reaction_node_id(keep, r, map_node2react_smile, reaction_smiles_txt2fp_dict): + logic = True + a = np.array(reaction_smiles_txt2fp_dict[map_node2react_smile[r]]) + for r_ in keep: + b = np.array(reaction_smiles_txt2fp_dict[map_node2react_smile[r_]]) + d = np.sum((a - b) ** 2, axis=0) + if d < 2.0: + return False + return logic + + +def filter_similar_node_id(list_of_list, node_reaction_list, map_node2react_smile, reaction_smiles_txt2fp_dict): + list_uniq = [] + first: Dict[int, List[str]] = {} + for l1 in list_of_list: + r = node_reaction_list[l1[-1]] + if l1[0] not in first: + first[l1[0]] = [] + first[l1[0]].append(r) + list_uniq.append(l1) + continue + if check_reaction_node_id(first[l1[0]], r, map_node2react_smile, reaction_smiles_txt2fp_dict): + first[l1[0]].append(r) + list_uniq.append(l1) + else: + pass + logger.debug("filter_similar_node_id: len(list_uniq) " + str(len(list_uniq))) + return list_uniq + + +def filter_similar_node_id_with_tree( + tree_dict, + list_of_list, + node_reaction_list, + map_node2react_smile, + reaction_smiles_txt2fp_dict, +): + full_node_list_from_tree_local = [tree_dict[node].name for node in tree_dict] + list_uniq = [] + first: Dict[int, List[str]] = {} + for l1 in list_of_list: + l2 = [full_node_list_from_tree_local.index(i) for i in get_path_to_node(tree_dict[node_reaction_list[l1[-1]]])] + if len(l2) > 1: + l0 = l2[-2] + else: + l0 = 0 + r = node_reaction_list[l1[-1]] + if l0 not in first: + first[l0] = [] + first[l0].append(r) + list_uniq.append(l1) + continue + if check_reaction_node_id(first[l0], r, map_node2react_smile, reaction_smiles_txt2fp_dict): + first[l0].append(r) + list_uniq.append(l1) + else: + pass + return list_uniq + + +def select_l_nb_node_id(tree_dict, node_reaction_list, l_select): + # Compute the list of id of the reactions at given level + # l_select=3 + if l_select not in [2 * i + 1 for i in range(100)]: + return [] + list_of_list_of_2_inds = [] + for node in tree_dict: + if "NONE" in node: + continue + if tree_dict[node].depth == l_select: + tmp = get_path_to_node(tree_dict[node]) + l_ = [node_reaction_list.index(n_) for n_ in tmp if n_ in node_reaction_list] + # correction to avoid empty lists appended ... [[],[],[],...] + if len(l_) > 0: + list_of_list_of_2_inds.append(l_) + return list_of_list_of_2_inds + + +def select_l_nb_filter_node_id(tree_dict, node_reaction_list, l_select, list_filter): + # Compute the list of id of the reactions at given level + if l_select not in [2 * i + 1 for i in range(100)]: + return [] + if len(list_filter) > 0: + nlen = len(list_filter[0]) + else: + nlen = -1 + list_of_list = [] + for node in tree_dict: + if "NONE" in node: + continue + if tree_dict[node].depth == l_select: + tmp = get_path_to_node(tree_dict[node]) + list_of_inds = [node_reaction_list.index(n_) for n_ in tmp if n_ in node_reaction_list] + if nlen > 0: + if list_of_inds[0:nlen] in list_filter: + # correction to avoid empty lists appended ... [[],[],[],...] + if len(list_of_inds) > 0: + list_of_list.append(list_of_inds) + else: + pass + else: + # correction to avoid empty lists appended ... [[],[],[],...] + if len(list_of_inds) > 0: + list_of_list.append(list_of_inds) + return list_of_list + + +def get_score_for_a_single_path( + list_of_steps, + tree_data, + node_reaction_list, + map_node2react_smile, + reaction_smiles_txt2fp_dict, + saved_scores, + max_dist_along_cutoff, + n_neighbors: int = 5, + max_length_path_neb: int = 15, +): + lmax = max_length_path_neb + nmax = len(list_of_steps) + + # Important: `list_str`: this is changed in the loop + # only at the end of the loop is set equal to the full list + # to be saved in the 'global' variable. + + nl_count = 0 + sum_min_dist = 0.0 + for ib1 in range(nmax): + for j in range(1, lmax): + ib2 = ib1 + j + # this `ib2 > nmax` should be without equal to include the last step + if ib2 > nmax: + break + list_ = [list_of_steps[i] for i in range(ib1, ib2)] + list_str = str(list_) # this is to make an id of `list_` + + if list_str in saved_scores["part"]: + min_dist = saved_scores["part"][list_str] + else: + # `my_fps_series` is a list of fps ... + my_fps_series = [reaction_smiles_txt2fp_dict[map_node2react_smile[node_reaction_list[i]]] for i in list_] + my_data = np.array(my_fps_series) + my_data = my_data.reshape(1, j * 16) + dist, ind = tree_data[j].query(my_data, k=n_neighbors) + # + # DISTANCE KDTree + # min_dist = np.mean(dist) + min_dist = np.mean(np.mean(dist * dist, axis=1)) + saved_scores["orig_part_dists"][list_str] = [dist[0][i] * dist[0][i] for i in range(dist.shape[1])] + saved_scores["orig_part"][list_str] = min_dist + saved_scores["orig_part_inds"][list_str] = ind + # CUT_OFF_min_dist, below min_dist not further used, + # not check one by one vs. max_dist_along_cutoff + if min_dist > max_dist_along_cutoff * len(list_): + min_dist = max_dist_along_cutoff * len(list_) + saved_scores["part"][list_str] = min_dist + saved_scores["part_inds"][list_str] = ind + saved_scores["part_len_steps"][list_str] = len(list_) + saved_scores["part_steps"][list_str] = list_ + + saved_scores["local"][list_str] = min_dist / len(list_) + nl_count = nl_count + 1 + sum_min_dist = sum_min_dist + min_dist / len(list_) + + # Important reset list_str=str(list_of_steps) + # Now `list_str` is the string of the full `list_of_steps` + list_str = str(list_of_steps) + if nl_count > 0: + sum_min_dist = sum_min_dist / nl_count + saved_scores["global"][list_str] = sum_min_dist + else: + pass + return saved_scores + + +def get_score_for_many_single_path( + list_of_list_of_steps, + tree_data, + node_reaction_list, + map_node2react_smile, + reaction_smiles_txt2fp_dict, + saved_scores, + max_dist_along_cutoff, +): + saved_here = {} + for i_ in range(len(list_of_list_of_steps)): + list_of_steps = list_of_list_of_steps[i_] + # print('get_score_for_many_single_path', i_, list_of_steps) + # list_of_steps = [it for it in list_of_list_of_steps[i_]] + if len(list_of_steps) < 1: + continue + saved_scores = get_score_for_a_single_path( + list_of_steps, + tree_data, + node_reaction_list, + map_node2react_smile, + reaction_smiles_txt2fp_dict, + saved_scores, + max_dist_along_cutoff, + ) + saved_here[str(list_of_steps)] = saved_scores["global"][str(list_of_steps)] + return saved_scores, saved_here + + +def get_level_max(tree_dict): + depth_set = set([tree_dict[node].depth for node in tree_dict]) + level_max = max(depth_set) + return level_max + + +def remove_nodes(new_tree_dict): + # list of nodes to be removed with or without parent + node_del = [] + node_del_with_parent = [] + for node in new_tree_dict: + if new_tree_dict[node].depth == 0: + if "ROOT" not in node: + continue + number = len([c for c in [i_.name for i_ in new_tree_dict[node].children] if c in new_tree_dict]) + if number > 0: + continue + if "STOP" in node or "MORE" in node or "WAIT" in node: + node_del_with_parent.append(node) + elif "END" in node: + pass # this is good + else: + # this is a reaction not continued + node_del.append(node) + + # nullify `node_del` + for node in node_del: + new_tree_dict[node] = Node(node, parent=None) + for node in node_del: + del new_tree_dict[node] + + # nullify `node_del_with_parent` + more_to_del = [] + for node in node_del_with_parent: + n = new_tree_dict[node].parent + new_tree_dict[node] = Node(node, parent=None) + new_tree_dict[n.name] = Node(n.name, parent=None) + more_to_del.append(n.name) + more_and_more_to_del = [re.sub(r"(^.*?)___.*$", r"\1", n_) for n_ in more_to_del] + + for node in node_del_with_parent: + del new_tree_dict[node] + + for node in more_to_del: + if node in new_tree_dict: + del new_tree_dict[node] + + node_del = [] + for prefix_without_underscore in more_and_more_to_del: + prefix_ = prefix_without_underscore + "_" + for node in new_tree_dict: + if "ROOT" in node: + continue + if node[: len(prefix_)] == prefix_: + node_del.append(node) + + # nullify `node_del` + for node in node_del: + if node in new_tree_dict: + new_tree_dict[node] = Node(node, parent=None) + for node in node_del: + if node in new_tree_dict: + del new_tree_dict[node] + + return new_tree_dict + + +def get_all_the_finished_reactions_up_to(tree_dict, node_reaction_list, level_max): + if level_max in [2 * i + 1 for i in range(100)]: + level_max = level_max + 1 + logger.debug(f"get_all_the_finished_reactions_up_to: level_max: {level_max}") + + # Make a copy of the tree_dict + new_tree_dict = deepcopy(tree_dict) + + # Remove nodes above level_max (L>=level_max+1) + delete = [] + for node in new_tree_dict: + if new_tree_dict[node].depth >= level_max + 1: + delete.append(node) + + for node in delete: + new_tree_dict[node] = Node(node, parent=None) + # May consider to delete the key + for node in delete: + del new_tree_dict[node] + + nf = -1 + ni = len(new_tree_dict) + while not ni == nf: + ni = len(new_tree_dict) + new_tree_dict = remove_nodes(new_tree_dict) + nf = len(new_tree_dict) + + # The exclude list is to remove the shortest pathways from the long ones + dict_of_list_of_x_inds: Dict[int, List[int]] = {} + if level_max in [2 * i + 1 for i in range(100)]: + my_l = level_max + else: + my_l = level_max - 1 + + for i_level in range(1, my_l + 1, 2): + dict_of_list_of_x_inds[i_level] = [] + list_of_list_of_x_inds_ = select_l_nb_node_id(new_tree_dict, node_reaction_list, i_level) + # We need to check for the last are good + for l1 in list_of_list_of_x_inds_: + bad = False + n = l1[-1] + for item in new_tree_dict[node_reaction_list[n]].children: + if "MORE" in item.name or "STOP" in item.name: + bad = True + break + if not bad: + dict_of_list_of_x_inds[i_level].append(l1) + + return dict_of_list_of_x_inds, new_tree_dict + + +def check_sanity_tree(tree_dict, leaves_purged_set, verbose: bool = True): + sanity_status: Dict[str, Any] = { + "check": False, + "list_of_l_more_to_stop": [], + "list_of_node_more_to_stop": [], + "more_status": {}, + "to_complete": {}, + } + + leaves = [node for node in tree_dict if (tree_dict[node].is_leaf and node not in leaves_purged_set)] + full_node_list_from_tree = [tree_dict[node].name for node in tree_dict] + list_of_l = get_l_from_tree(tree_dict) + + more_ = set([node for node in leaves if "MORE" in node]) + end_ = set([node for node in leaves if "END" in node]) + wait_ = set([node for node in leaves if "WAIT" in node]) + stop_ = set([node for node in leaves if "STOP" in node]) + none_ = set([node for node in leaves if "NONE" in node]) + + stop_ = stop_.union(leaves_purged_set) + + others_ = set() + for node_ in leaves: + if node_ in more_: + continue + if node_ in end_: + continue + if node_ in wait_: + continue + if node_ in stop_: + continue + if node_ in none_: + continue + others_.add(node_) + + bad_node = none_.union(stop_) + if verbose: + logger.info("check_sanity_tree info") + logger.info(f"check_sanity_tree: {len(others_)} len_others_ {others_}") + logger.info(f"check_sanity_tree: {len(more_)} more_") + logger.info(f"check_sanity_tree: {len(end_)} end_") + logger.info(f"check_sanity_tree: {len(wait_)} wait_") + logger.info(f"check_sanity_tree: {len(stop_)} stop_") + logger.info(f"check_sanity_tree: {len(none_)} none_") + logger.info(f"check_sanity_tree: {len(bad_node)} bad_node") + + my_more = [node for node in full_node_list_from_tree if node in more_] + for node_more in my_more: + sanity_status["to_complete"][node_more] = set() + ime = full_node_list_from_tree.index(node_more) + r = complete_reactions(list_of_l, ime) + status_ = [] + check_of_what_could_be = set() + for i in range(len(r)): + ok = True + a_ = [ll[-1] for ll in r[i] if len(ll) > 0] + for e in a_: + check_of_what_could_be.add(e) + if full_node_list_from_tree[e] in bad_node: + ok = False + break + status_.append(ok) + if ok: + for e in a_: + el = full_node_list_from_tree[e] + if el in more_ or el in wait_: + sanity_status["to_complete"][node_more].add(el) + if any(status_): + logger.info(f"check_sanity_tree: {ime} STATUS GOOD {node_more}") + sanity_status["more_status"][node_more] = "GOOD" + else: + logger.info(f"check_sanity_tree: {ime} STATUS BAD {node_more}") + sanity_status["more_status"][node_more] = "BAD" + if ime not in sanity_status["list_of_l_more_to_stop"]: + sanity_status["list_of_l_more_to_stop"].append(ime) + if node_more not in sanity_status["list_of_node_more_to_stop"]: + sanity_status["list_of_node_more_to_stop"].append(node_more) + + sanity_status["check"] = True + + return sanity_status + + +def fetch_score( + r_, + saved_scores, + node_reaction_list, + tree_dict, + full_node_list_from_tree, + map_node2react_smile, + verbose, +): + r_str = str(r_) + if r_str in saved_scores["global"]: + score_ = saved_scores["global"][r_str] + else: + score_ = "N/A" + n = r_[-1] + res = get_path_to_node(tree_dict[node_reaction_list[n]]) + l1 = [full_node_list_from_tree.index(i) for i in res] + message_ = "fetch_score: " + str(len(r_)) + " ==> " + str(r_) + message_ = message_ + " >> " + str(l1) + " score (this one): " + str(score_) + logger.debug(message_) + if verbose: + text = [] + for ik in range(len(r_)): + nik = node_reaction_list[r_[ik]] + t = map_node2react_smile[nik] + text.append(t) + + single_result_v_l_score = {} + single_result_v_l_score["score"] = score_ + single_result_v_l_score["v"] = [i for i in r_] + single_result_v_l_score["l"] = [i for i in l1] + single_result_v_l_score["v_str"] = str(r_) + single_result_v_l_score["l_str"] = str(l1) + single_result_v_l_score["text"] = [t for t in text] + + return single_result_v_l_score + + +def list_is_included(my_list, list_of_lists): + s_my_list = "-".join([str(i) for i in my_list]) + "-" + s_list_of_lists = set(["-".join([str(i) for i in l1]) + "-" for l1 in list_of_lists if not my_list == l1]) + # print('s_list_of_lists', s_list_of_lists) + for s in s_list_of_lists: + # print('s_my_list vs. s', s_my_list, s) + if s_my_list in s: + return True + return False + + +def complete_reactions(list_of_lists, jfin): + # + # list_of_lists=[ [1,2], [1,2,3,6], [1,2,5,29], + # [1,2,3,6,7,10], [1,2,3,6,9,11], [1,2,3,6,9,12] ] + # + # list_of_lists should be updated at the given level (level of jfin) + # jfin an id of the selected finished reactions we want to complete + # jfin is the last id in one of the lists in `list_of_lists` + # + a = [] + necessary_to_completion: List[List[int]] = [] + for i in range(len(list_of_lists)): + my_list = list_of_lists[i] + if jfin == my_list[-1]: + logger.debug(f"complete_reactions: {jfin}, my_list: {my_list}") + if len(my_list) < 4: + # print("strange, because here we are at least in a two-steps reaction") + continue + + # print('check:', list_is_included(my_list, list_of_lists)) + if list_is_included(my_list, list_of_lists): + logger.debug(f"complete_reactions: error in complete_reactions {my_list} list_is_included") + logger.error("complete_reactions: error in complete_reactions list_is_included") + raise Exception("Error in complete_reactions list_is_included") + + necessary_to_completion = [] + for isplit in range(1, len(my_list), 2): + # parent_m2 = my_list[isplit] + # print('parent_m2:', parent_m2, 'with isplit:', isplit) + for k in range(len(list_of_lists)): + k_list = list_of_lists[k] + if len(k_list) <= isplit + 1: + continue + if my_list == k_list: + continue + # if not len(my_list)==len(k_list): continue + # if my_list[-2]==k_list[-2]: continue + if my_list[isplit] == k_list[isplit]: + if my_list[isplit + 1] != k_list[isplit + 1]: + necessary_to_completion.append(k_list) + a.append(k_list[-2]) + # print('necessary to completion:', my_list, k_list) + # print("complete_reactions_necessary_to_completion:", necessary_to_completion) + # Check which routes are alternatives and which are complemetary + list_of_lists = [l1 for l1 in necessary_to_completion] + # making groups + ngr = 0 + groups = {} + nl = len(list_of_lists) + groups[ngr] = [ + 0, + ] + for i in range(1, nl): + alt_ = False + for k, v in groups.items(): + for vv in v: + check_alternatives_, _ = check_alternatives(list_of_lists[vv], list_of_lists[i]) + if check_alternatives_: + groups[k].append(i) + alt_ = True + break + if alt_: + break + if not alt_: + ngr = ngr + 1 + groups[ngr] = [ + i, + ] + # --- + logger.debug("complete_reactions: complete_all_reactions_groups " + str(groups)) + l1 = [v for k, v in groups.items()] + # print("complete_all_reactions_groups:", l1) + # --- + combinations_for_completion = [list(i) for i in list(itertools.product(*l1))] + # print( + # "complete_reactions_combinations_for_completion:", combinations_for_completion + # ) + if len(necessary_to_completion) > 0: + results = [[necessary_to_completion[ll] for ll in l1] for l1 in combinations_for_completion] + else: + results = [[[]]] + return results + + +def good_coverage(results_v_l_score_dict, n_initial): + a = [v["v"][0] for k, v in results_v_l_score_dict.items()] + b = [v["v"][0] for k, v in results_v_l_score_dict.items() if min(v["final_score"]) < 40.0] + if len(set(b)) >= int(0.50 * n_initial): + return True + if len(set(b)) >= int(0.33 * n_initial) and len(b) >= n_initial: + return True + if len(set(a)) >= int(0.66 * n_initial): + return True + if len(set(a)) >= int(0.50 * n_initial) and len(a) >= 2 * n_initial: + return True + return False + + +def get_full_text_sets_of_reactions(v, res): + # For each value of the result dictionary `res` + # make the complete list of smiles associated + # to that reaction + # `texts` is at the end a list of sorted lists + texts = [] + n_reactions = len(v["final_score"]) + n_check = len(v["complete_reactions"]) + if not n_reactions == n_check: + logger.debug("get_full_text_sets_of_reactions: error: why len(final_score) is not equal to len(complete_reactions)?") + logger.error("get_full_text_sets_of_reactions: Error: len(final_score) != len(complete_reactions)") + raise Exception("Error: len(final_score) != len(complete_reactions)") + for n in range(n_reactions): + full_text = set(v["text"]) + full_text_sorted_list = sorted(list(full_text)) + if not str(v["complete_reactions"]) == "[[[]]]": + k_tot = len(v["complete_reactions"][n]) + for k in range(k_tot): + l_str = str(v["complete_reactions"][n][k]) + for s in res[l_str]["text"]: + full_text.add(s) + full_text_sorted_list = sorted(list(full_text)) + # Note: even in case of "[[[]]]" there should be the following append! + texts.append(full_text_sorted_list) + # print('debug_get_full_text_sets_of_reactions: v, len(texts), texts', + # v, len(texts), texts) + if len(texts) == 0: + logger.debug("get_full_text_sets_of_reactions: error: why len(texts) is zero?") + logger.error("get_full_text_sets_of_reactions: error: len(texts) is zero?") + raise Exception("get_full_text_sets_of_reactions: error: len(texts) is zero?") + return texts + + +def hash_str(t): + return hashlib.sha1(str(t).encode("utf-8")).hexdigest() + + +def reshape_score(x): + return 1.0 / (1.0 + np.log10(1.0 + x)) + + +def get_retrosynthesis( + results_v_l_score_dict, + sources, + saved_confidences, + saved_classes, +): + logger.debug(f"get_retrosynthesis: len(saved_confidences): {len(saved_confidences)}") + logger.debug(f"get_retrosynthesis: saved_confidences: {saved_confidences}") + d1 = {} + for k_, v_ in results_v_l_score_dict.items(): + for i in range(len(v_["source"])): + source_ = v_["source"][i] + smiles_ = sorted(list(v_["full_smiles"][i])) + d1[source_] = smiles_ + d2 = {k["source"]: k["final_score"] for k in sources} + # Limit to the top N scores (here lower is better) + n_max = 100 + if len(d2) < n_max: + n_max = len(d2) + if n_max <= 10: + topn_threshold = 9999999.0 + logger.debug(f"get_retrosynthesis: topn_threshold N={n_max}: {topn_threshold}") + else: + topn_threshold = sorted([v for k, v in d2.items()])[n_max - 1] + logger.debug(f"get_retrosynthesis: topn_threshold N={n_max}: {topn_threshold}") + # + retrosynthesis = [] + for source_ in d1: + if source_ not in d2: + logger.debug(f"get_retrosynthesis: Strange source_ not in d2: {source_}") + continue + if n_max > 10 and d2[source_] > topn_threshold: + continue # This is just to speed up and keep topN N=30 above + logger.debug(f"get_retrosynthesis: summary_single_retro_result: source: {source_}") + # + single = {} + # + # add `source` to single to tag it + single["source"] = source_ + single["reactions"] = [] + logger.debug("get_retrosynthesis: populating the reactions") + for reaction_smiles in d1[source_]: + d_ = {} + d_["reaction_smiles"] = reaction_smiles + logger.debug(f"get_retrosynthesis: reaction_smiles: {reaction_smiles}") + if reaction_smiles in saved_confidences: + d_["confidence"] = saved_confidences[reaction_smiles] + logger.debug("get_retrosynthesis: confidence: " + str(d_["confidence"])) + if reaction_smiles in saved_classes: + d_["reaction_class"] = saved_classes[reaction_smiles] + single["reactions"].append(d_) + # ----- + if len(d1[source_]) < 1: + logger.debug("get_retrosynthesis: Strange no reactions for " + str(source_) + " " + str(single["reactions"])) + # here score in changed score = 1/(1+log10(1+score)) + single["optimization_score"] = reshape_score(d2[source_]) + retrosynthesis.append(single) + + # sort by score: after applying reshape_score, high score is better than low score + # reshape_score: see above changes score x -> 1/(1+log10(1+x)) + logger.debug(f"get_retrosynthesis: len(retrosynthesis) before applying cut {len(retrosynthesis)}") + # limit max number of returned results + retrosynthesis = sorted(retrosynthesis, key=lambda d: d["optimization_score"], reverse=True)[0 : min(len(retrosynthesis), 30)] + logger.debug(f"get_retrosynthesis: len(retrosynthesis) after applying cut {len(retrosynthesis)}") + return retrosynthesis diff --git a/src/rxn/neb/utils/general.py b/src/rxn/neb/utils/general.py new file mode 100644 index 0000000..6fe063e --- /dev/null +++ b/src/rxn/neb/utils/general.py @@ -0,0 +1,122 @@ +"""General utilities.""" +import ast +from itertools import zip_longest +from typing import Dict, Iterable, List + +import regex as re +from loguru import logger + + +def get_l_from_tree(tree_dict): + leaves = [node for node in tree_dict if tree_dict[node].is_leaf] + full_node_list_from_tree = [tree_dict[node].name for node in tree_dict] + list_of_l = [[full_node_list_from_tree.index(node) for node in get_path_to_node(tree_dict[leaf])] for leaf in leaves] + return list_of_l + + +def get_path_to_node(node): + string_node_list = str(node.path[-1]).replace("Node", "") + string_node_list = string_node_list.replace("('", "").replace("')", "") + # NOTE: node separator not ("/"), now it is (",") + node_list = string_node_list.split(",") + anchestors = node_list[1:] + # \\ replace a double backslash with a single backslash + anchestors = [s.encode().decode("unicode_escape") for s in anchestors] + return anchestors + + +def check_alternatives_among(ipos_limit, l_list_single, l_list_of_leaves): + keep_ = True + n = 0 + debug_ipos_check_set = set() + for i in range(len(l_list_of_leaves)): + check_alternatives_, ipos_check = check_alternatives(l_list_single, l_list_of_leaves[i]) + debug_ipos_check_set.add(ipos_check) + if check_alternatives_ and ipos_check == ipos_limit: + keep_ = False # when False => is not uniq + n = n + 1 + return keep_, n # it is unique + + +def check_alternatives(l_i, l_j): + m = min(len(l_i), len(l_j)) + for i in range(1, m, 2): + if not l_i[i] == l_j[i] and l_i[i - 1] == l_j[i - 1] and (i % 2) == 1: + return True, i + return False, 0 + + +def select_from_saved_here(x, max_num): + message_ = "select_from_saved_here: saved_here: " + str(x) + logger.debug(message_) + lol = [ast.literal_eval(k) for k in x] + keep: List[int] = [] + group: Dict[int, Dict[int, List[int]]] = {} + first_ = False + for i in range(len(lol)): + if len(lol[i]) == 1: + i2 = lol[i][-1] + first_ = True + if first_: + break + else: + i2 = lol[i][-2] + if i2 not in group: + group[i2] = {} + group[i2][i] = x[str(lol[i])] + + message_ = "select_from_saved_here: group: " + str(group) + logger.debug(message_) + + for n_me in range(1, 13): + if first_: + break + keep = [] + for _, xx in group.items(): + best = [k for k, v in sorted(xx.items(), key=lambda item: item[1])][0:n_me] + for b in best: + keep.append(b) + if len(keep) >= max_num: + break + + nlol = [lol[i] for i in keep] + if first_: + nlol = [ast.literal_eval(k) for k, v in sorted(x.items(), key=lambda item: item[1])][0:max_num] + message_ = "select_from_saved_here_first: n_me/len(keep)/len(x)/nlol:" + message_ = message_ + " " + str(n_me) + message_ = message_ + " " + str(len(keep)) + message_ = message_ + " " + str(len(x)) + message_ = message_ + " " + str(nlol) + logger.info(message_) + + return nlol + + +def select_prefix_to_keep(node_reaction_list, save_here): + try: + s_ = [k for k, v in sorted(save_here.items(), key=lambda item: item[1])][0] + list_best = ast.literal_eval(s_) + id_ = list_best[0] + ns_ = node_reaction_list[id_] + prefix_ = re.sub(r"(R\d+)___.*", r"\1", ns_) + logger.info(f"select_prefix_to_keep: keep_this_prefix: {prefix_}") + except Exception: + prefix_ = "" + logger.info("select_prefix_to_keep: keep_this_prefix: FAILS") + logger.debug("select_prefix_to_keep: keep_this_prefix: FAILS") + return prefix_ + + +def batcher(iterable: Iterable, batch_size: int = 32): + """Generate batches from an iterable. + + Args: + iterable: iterable to batch. + batch_size: batch_size. + + Returns: + batches of the desired size. + """ + batched_iterable = [iter(iterable)] * batch_size + for batch in zip_longest(*batched_iterable, fillvalue=None): + yield [element for element in batch if element is not None] diff --git a/src/rxn/neb/utils/smiles.py b/src/rxn/neb/utils/smiles.py new file mode 100644 index 0000000..0f9d046 --- /dev/null +++ b/src/rxn/neb/utils/smiles.py @@ -0,0 +1,106 @@ +"""SMILES utilities.""" +from typing import Set, Tuple + +import regex as re +from rxn.chemutils.multicomponent_smiles import multicomponent_smiles_to_list +from rxn.chemutils.smiles_standardization import standardize_molecules, standardize_smiles + +from ..availability import is_available + + +def multistep_standardize(smiles): + # retro - model + return standardize_smiles( + smiles, + canonicalize=True, + sanitize=True, + inchify=False, + ) + + +def multistep_standardize_fw(molecules): + # forward - model + return standardize_molecules( + molecules, + canonicalize=True, + sanitize=True, + inchify=False, + fragment_bond="~", + ordered_precursors=True, + molecule_token_delimiter=None, + is_enzymatic=False, + enzyme_separator="|", + ) + + +def canonicalize_smiles(smile, model_selection="forward_model"): + if model_selection == "retro_model": + sm = multistep_standardize(smile) + elif model_selection == "forward_model": + sm = multistep_standardize_fw(smile) + else: + sm = "None" + return sm + + +def try_further_splitter(s): + s_ = s + if re.search(r"(\[[A-Z][a-z]?\+?\])\)", s): + s_ = re.sub(r"(\[[A-Z][a-z]?\+?\])\)", r").\1.", s) + else: + s_ = re.sub(r"(\[[A-Z][a-z]?\+?\])", r".\1.", s) + s_ = re.sub(r"\.+", ".", s_) + return s_ + + +def try_further(s): + outcome = False + # repeated twice + s = try_further_splitter(s) + s = try_further_splitter(s) + more_split = s.split(".") + for ms in more_split: + if is_available(ms): + outcome = True + else: + return False + return outcome + + +def simpler_smile_tilde(smile): + # O=C1[C@@H](N~Cl~Cl)CCN1Cc1ccc2[nH]cnc2c1 => O=C1[C@@H](N)CCN1Cc1ccc2[nH]cnc2c1.Cl.Cl + smiles = smile.split(".") + new_smile = "" + for smile in smiles: + z = re.search(r"\(([A-Z][a-z]?)((?:~[A-Z][a-z]?)+)\)", smile) + if z: + end1 = z.span(1)[1] + ini2 = z.span(2)[0] + end2 = z.span(2)[1] + t1 = re.sub(r"~", ".", smile[ini2:end2]) + smile = smile[:end1] + smile[end2:] + t1 + if len(new_smile) > 0: + new_smile = new_smile + "." + smile + else: + new_smile = smile + return new_smile + + +def from_complex_smile_to_fragments(multi) -> Tuple[Set[str], str]: + can_fragments = set() + # Clean for '(N~Cl~Cl)' + multi = simpler_smile_tilde(multi) + elements = multicomponent_smiles_to_list(multi, fragment_bond="~") + for element in elements: + fragments = element.split(".") + for f in fragments: + try: + can_fragments.add(canonicalize_smiles(f)) + except Exception: + can_fragments.add(f + "__NONE__FAILED_canonicalize_smiles") + s_ = "" + for e in sorted(list(can_fragments)): + s_ = s_ + str(e) + "." + if len(s_) > 1: + s_ = s_[0:-1] + return can_fragments, s_