myopic-mces 1.1.1

A package for computation of the myopic MCES distance

Computation of myopic MCES distances

Reference implementation for computing myopic Maximum Common Edge Subgraph (MCES) distances, described in the publication Coverage bias in small molecule machine learning (Citation).

Usage

Input and Output file are in csv format per default. Every line in the input-file is one comparison:

input-file: index,SMILES1,SMILES2

output-file: index,myopic MCES distance,computation time in seconds,computation mode

Download via pip and execute:

pip install myopic-mces
myopic_mces input-file output-file

Alternatively, 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.
As PuLP does not ship with solvers anymore, a solver must be configured before running myopic-mces.
The default solver for myopic-mces is 'COIN_CMD', which can be installed via:

pip install pulp[cbc]

Optional Arguments

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 distance is always calculated.
                        Default: 10

--solver string         Solver used for solving the ILP. Examples:'CPLEX_CMD', 'GUROBI_CMD', 'GLPK_CMD'.
                        Default: COIN_CMD

--num_jobs int          Number of jobs; instances to run in parallel.
                        By default this is set to the number of (logical) CPU cores.
                        Default: -1

--hdf5_mode             If set, input will be read from `input-file` in HDF5 format; output will be appended to this file.
                        See "Prepare HDF5 input" below

--hide_rdkit_warnings   If set, attempts to supress RDKit warnings.

Options for the ILP solver

--solver_onethreaded            If set, limits ILP solver to one thread, likely resulting in faster
                                performance with parallel computations (not available for all solvers).

--solver_no_msg                 If set, prevents solver from logging (not available for all solvers)

--solver_time_limit_seconds     Set a time limit for the ILP solver. Computations below the threshold are not guaranteed to be exact anymore!
                                Supported solver is CPLEX_PY, for others, correctness of returned computation
                                modes cannot be guaranteed. (Experimental)

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.

--use_bound_zero            If set, compute and use an additional weaker formula-based lower bound.
                            Use in conjunction with `choose_bound_dynamically`.

--catch_computation_errors  Instead of aborting the computation, instances that failed to compute receive distance "-1".

--jobs_batch_size           Batch size for parallelization.
                            Default: 32

--jobs_dispatch             Pre-dispatch of jobs for parallelization.
                            Default: 10*n_jobs

--use_matrix_lookup         Use with the path to a HDF5 file with precomputed MCES distances. Computation for these 
                            instances will be skipped, using the provided values. HDF5 has to contain distances (key `mces`) 
                            and SMILES (`mces_smiles_order`), like the HDF5 files produced by this script. 
                            NOTE: When used in combination with `prepare_input`, only use with `--no_shuffle`.

--lookup_threshold          Use with `--use_matrix_lookup`: Precomputed values equal or greater than the threshold 
                            will be ignored; these instances will be recomputed

Recommended settings

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 tmpout

The PATH-variable has to be adapted to contain the directory of the CPLEX executable (see the PuLP documentation).

Dependencies and installation

Python packages required are:

rdkit(==2022.09.5)
networkx(==3.0)
pulp[cbc](==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 Arch-Linux@linux-6.2.7 64 bit.

The recommended method of installation is via pip.

pip install myopic_mces

Dependencies 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_mces

Another option is using uv: Download this repository, navigate to the download location and execute the following commands:

uv sync
# to activate the created environment:
source .venv/bin/activate

A typical installation time should not exceed 5 minutes, mostly depending on the internet connection speed to download all required packages.

Example data

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.csv

Typical 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.

Utilities

prepare_input.py

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_000

input-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.

Prepare HDF5 input

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 SMILES
• computation_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.

combine_hdf5_batches.py

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*.hdf5

To 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 labels

filter_dataset.py

If 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 10

Input 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.

Citation

F. Kretschmer, J. Seipp, M. Ludwig, G. W. Klau, and S. Böcker. Coverage bias in small molecule machine learning. Nat Commun 16(1):554, 2025.