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[![CI](https://github.com/OpenFreeEnergy/Lomap/actions/workflows/CI.yaml/badge.svg)](https://github.com/OpenFreeEnergy/Lomap/actions/workflows/CI.yaml)
[![Documentation Status](https://readthedocs.org/projects/lomap/badge/?version=latest)](https://lomap.readthedocs.io/en/latest/?badge=latest)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.8344248.svg)](https://doi.org/10.5281/zenodo.16898468)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.16898468.svg)](https://doi.org/10.5281/zenodo.16898468)
# Lomap
Alchemical free energy calculations hold increasing promise
as an aid to drug discovery efforts. However, applications of
these techniques in discovery projects have been relatively
rare, partly because of the difficulty of planning and setting up
calculations. The lead optimization mapper (LOMAP) was
introduced as an automated algorithm to plan efficient relative
free energy calculations between potential ligands within
a substantial set of compounds. The original LOMAP code was mainly
based on commercial APIs such as OpenEye and Schrodinger. The aim
of this project is to develop a new version of LOMAP based on freely
available APIs such as RDKit offering the scientific community a
free tool to plan in advance binding free energy calculations.

## Prerequisites
* RDKit Release > 2021
* NetworkX
* Matplotlib
* python > 3.8

Authors
-------

See [AUTHORS.md](https://github.com/OpenFreeEnergy/Lomap/blob/main/AUTHORS.md)

The Lead Optimization Mapper (LOMAP) is an automated algorithm for planning

@jthorton jthorton Jun 12, 2026

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Link to the paper for more details on the method as its missing from the docs landing padge.

@hannahbaumann hannahbaumann Jun 12, 2026

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Added this below!

efficient relative free energy calculation networks across a set of ligands,
built on freely available tools such as RDKit.
With the optional [`gufe`](https://github.com/OpenFreeEnergy/gufe) dependency installed,
it also integrates with the [Open Free Energy](https://openfree.energy) ecosystem.
The method is described in the original
[LOMAP publication](https://doi.org/10.1007/s10822-013-9678-y).

## Installation

### Latest Release

You can install using conda (or mamba). Note the package name is `lomap2`:
`conda install -c conda-forge lomap2`

### Development Version
Alternatively, you can install the development version of `lomap` directly from the `main` branch of this repository.

First install the package dependencies using conda (or mamba) in a virtual environment with:
`lomap` is available on conda-forge as the `lomap2` package. See the
[installation documentation](https://lomap.readthedocs.io/en/latest/installation.html)
for install instructions, including the development install and optional
dependencies (`gufe` and `pygraphviz`).

```bash
conda env create -f environment.yaml
conda activate lomap-env
```

Then install `lomap` locally with:

`pip install -e .`

Usage
-----
As a command line tool, LOMAP can be simply used as:
`
lomap test/basic/
`
## Quickstart

For a basic example run:
`python examples/example.py`

For generating radial graphs with a hub, run:
`python examples/example_radial.py`

If you would rather use the API directly, try:
This example uses LOMAP's optional `gufe` bindings to load two example ligands
bundled with the package and plan a perturbation network between them with the
default atom mapper and scorer.

```python
import lomap

# Generate the molecule database starting from a directory containing .mol2 files

db_mol = lomap.DBMolecules("python string pointing to a directory with mol2 files", output=True)

#More graphing options:
# Use the complete radial graph option. The ligand with the most structural similarity to all of the others will be picked as the 'lead compound' and used as the central compound.
db_mol = lomap.DBMolecules("python string pointing to a directory with mol2 files", output=True, radial=True)

# Use a radial graph with a manually specified hub compound
db_mol = lomap.DBMolecules("python string pointing to a directory with mol2 files", output=True, radial=True, hub=filename.mol2)

# Use a radial graph with a manually specified hub compound and fast graphing option
#the fast graphing option creates the initial graph by connecting the hub ligand with the possible surrounding ligands and adds surrounding edges based on the similarities across surrounding nodes
db_mol = lomap.DBMolecules("python string pointing to a directory with mol2 files", output=True, radial=True, hub=filename.mol2, fast=True)

# Calculate the similarity matrix between the database molecules. Two molecules are generated
# related to the strict rule and loose rule

strict, loose = db_mol.build_matrices()

# Generate the NetworkX graph and output the results
nx_graph = db_mol.build_graph()


# Calculate the Maximum Common Subgraph (MCS) between
# the first two molecules in the molecule database
# ignoring hydrogens and depicting the mapping in a file

MC = lomap.MCS.getMapping(db_mol[0].getMolecule(), db_mol[1].getMolecule(), hydrogens=False, fname='mcs.png')
# requires the optional `gufe` dependency (see Installation)
import importlib.resources

import lomap
from gufe import SmallMoleculeComponent

# Two example ligands ship with the package under lomap.tests.data
data = importlib.resources.files("lomap.tests.data")
ligands = [
SmallMoleculeComponent.from_sdf_file(data / name)
for name in ["lig_41.sdf", "lig_74.sdf"]
]

# Build a LigandNetwork using LOMAP's scoring and network-construction rules
network = lomap.generate_lomap_network(
ligands=ligands,
mappers=lomap.LomapAtomMapper(),
scorer=lomap.default_lomap_score,
)
print(f"{len(network.nodes)} ligands, {len(network.edges)} edges")
```

# Alchemical transformation are usually performed between molecules with
# the same charges. However, it is possible to allow this transformation
# manually setting the electrostatic score for the whole set of molecules
# producing a connected graph. The electrostatic score must be in the
# range [0,1]
For proposing individual mappings, customising edge scores, and the full set of
network options, see the [documentation](https://lomap.readthedocs.io).

## Authors

db_mol = lomap.DBMolecules("python string pointing to a directory with mol2 files", output=True, ecrscore=0.1)
strict, loose = db_mol.build_matrices()
nx_graph = db_mol.build_graph()
```
See [AUTHORS.md](https://github.com/OpenFreeEnergy/Lomap/blob/main/AUTHORS.md).

## History

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