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 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 free avalaible APIs such as RDKit offering the scientific community a free tool to plan in advance binding free energy calculations.
- RDKit Release > 2021
- NetworkX
- Matplotlib
- python > 3.8
See AUTHORS.md
conda install -c conda-forge lomap2
Alternatively, you can install the prerequisites and then install from source with:
python setup.py install
As a commandline tool LOMAP can be simply used as:
lomap test/basic/
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:
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 compounds' 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 create the initial graph by connecting the hub ligand with the possible surrounding ligands and add surrounding edges based on the similarities accoss 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 betweeen the database molecules. Two molecules are generated
# related to the scrict 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')
# 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 scrore must be in the
# range [0,1]
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() Open Free Energy took over maintenance of LOMAP in 2022. The original development repository has since been archived, but can be found at https://github.com/MobleyLab/Lomap.