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)

Run from the command line in the src directory:

python -m myopic_mces.myopic_mces input-file output-file

Alternatively, the myopic-mces-package can be downloaded via pip and executed directly:

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

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.

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.

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 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

A PyPI-package is also available, install via:

pip install myopic_mces

A 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.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.