A package for computation of the myopic MCES distance
Project description
Computation of myopic MCES distances
Implementation of myopic MCES distance computation, see the preprint at doi:10.1101/2023.03.27.534311 for details.
Usage
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:
python myopic_mces.py 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.
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 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.
Dependencies and installation
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 Arch-Linux@linux-6.2.7 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.
Example data
The example provided in example/example_data.csv can be run with:
python myopic_mces.py example/example_data.csv example/example_data_out.csv
Alternatively, if the package was installed via pip:
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.
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