Implementation of myopic MCES distance computation, see the preprint at doi:10.1101/2023.03.27.534311 for details.
Input and Output file are in csv format. Every line in the input-file is one comparison:
input-file: index,SMILES1,SMILES2
output-file: index,time taken,myopic MCES distance,status (1 if exact distance, 2/4 if lower bound)
Download via pip and execute:
pip install myopic-mces
myopic_mces input-file output-fileAlternatively, to install directly from this repository:
pip install -e .For usage in Python:
from myopic_mces import MCES
MCES('CC(=O)OC1=CC=CC=C1C(=O)O', 'CN1C=NC2=C1C(=O)N(C(=O)N2C)C')See the PuLP documentation on how to configure ILP solvers. By default, the PuLP-provided COIN-OR solver will be used.
General options
--threshold int Threshold for the comparison.
Exact distance is only calculated if the distance is lower than the threshold.
If set to -1 the exact disatnce is always calculated.
--solver string Solver used for solving the ILP. Examples:'CPLEX_CMD', 'GUROBI_CMD', 'GLPK_CMD'
--num_jobs int Number of jobs; instances to run in parallel.
By default this is set to the number of (logical) CPU cores.
--hdf5_mode Input will be read from `input-file` in HDF5 format; output will be appended to this file.
See "Prepare HDF5 input" below
Options for the ILP solver
--solver_onethreaded Limit ILP solver to one thread, likely resulting in faster
performance with parallel computations (not available for all solvers).
--solver_no_msg Prevent solver from logging (not available for all solvers)
Experimental options for myopic MCES distance computation
--no_ilp_threshold If set, do not add threshold as constraint to ILP,
resulting in longer runtimes and potential violations of the triangle equation.
--choose_bound_dynamically If set, a potentially weaker but faster lower bound will be computed and used
when this bound is already greater than the threshold. By default (without
this option), always the strongest lower bound will be computed and used.
To speed up computations and save space, use the CPLEX solver, HDF5-mode (see below) and enable --choose_bound_dynamically:
PATH=$CPLEX_HOME/bin/x86-64_linux/:$PATH python -m myopic_mces.myopic_mces --threshold 10 --solver CPLEX_CMD --solver_onethreaded --solver_no_msg --hdf5_mode input-file.hdf5 tmpoutThe PATH-variable has to be adapted to contain the directory of the CPLEX executable (see the PuLP documentation).
Python packages required are:
rdkit(==2022.09.5)
networkx(==3.0)
pulp(==2.7.0)
scipy(==1.10.1)
joblib(==1.2.0)
Version numbers in braces correspond to an exemplary tested configuration (under Python version 3.11.0). The program can be run on any standard operating system, tested on Windows 10 64 bit and [email protected] 64 bit.
The recommended method of installation is via pip.
pip install myopic_mcesDependencies can also be installed via conda or mamba:
Download this repository, navigate to the download location and execute the following commands (replacing conda with mamba when using mamba):
conda env create -f conda_env.yml
# to activate the created enironment:
conda activate myopic_mcesA typical installation time should not exceed 5 minutes, mostly depending on the internet connection speed to download all required packages.
The example provided in example/example_data.csv can be run with:
pip install myopic-mces
myopic_mces example/example_data.csv example/example_data_out.csvTypical runtime is about 10s on Windows 10 with all default options, running on 4 cores with 8GB RAM. Exemplary output is provided in example/example_data_out.csv.
For big datasets it is recommended to divide the input into batches, which can be done with this script:
python -m myopic_mces.prepare_input input-file.csv output-folder/ --batch_size 50_000_000input-file.csv has to be formatted as shown above. This creates output_folder with the subdirectory data puts all batches (named batch$i.csv) inside.
To conserve space, input for myopic MCES computation can now be provided as a HDF5-file containing the following "Datasets":
smiles: list of all unique SMILEScomputation_indices: matrix with the shape (n, 3), each row representing one instance to compute. The first column contains the index of the pair, the second and third the indices of the two SMILES, respectively
Example:
smiles = [smiles_a, smiles_b, smiles_c]
computation_indices = [[0, 0, 1], # computes MCES for a vs. b
[1, 0, 2], # computes MCES for a vs. c
[2, 1, 2], # computes MCES for b vs. c
]
Instead of preparing the HDF5-file manually, with the additional option to create batches, prepare_input.py can be used in HDF5-mode:
python -m myopic_mces.prepare_input --batch_size 50_000_000 --hdf5_mode input-smiles.txt output-folder/Created batches (batch$i.hdf5) are written directly to the output-folder.
Batches results can be combined into a single HDF5 file with this script:
python -m myopic_mces.combine_hdf5_batches --out output-file batches/batch*.hdf5To get a square matrix of MCES distances for convenience within Python from this file, simply do:
import h5py
from scipy.spatial.distance import squareform
with h5py.File(hdf5outputfile, 'r') as f:
mces_square = squareform(f['mces'])
smiles = f['mces_smiles_order'] # column/row labelsIf you want to filter datasets for similar structures in a database, you can use this script. This can speed up computations considerably, as for each query structure computations are stopped, when a "match" is found.
python filter_dataset.py --input input-file.json --out output-file --threshold 10Input is provided in the following format (JSON file):
{query_smiles1: [db_smiles1, db_smiles2, ...],
query_smiles2: [db_smiles1, db_smiles2, ...]}
The database can be different for each query SMILES, allow pre-filtering depending on the query.